Novel immune-nutritional prognostic ratio predicts long-term survival in stage I–III colorectal cancer

Background: Colorectal cancer (CRC) is a common and highly lethal malignancy worldwide. Even after curative resection, patients with stage I–III disease remain at substantial risk of recurrence and mortality. The Prognostic Immune and Nutritional Index (PINI) and lymphocyte-to-monocyte ratio (LMR) have been validated as prognostic markers in cancer, yet their individual predictive performance remains limited. We developed a novel Immune-Nutritional Prognostic Ratio (INPR) integrating PINI and LMR to provide a more comprehensive assessment of immune, nutritional, and inflammatory status. This study further evaluated its value in predicting 1-, 3-, and 5-year survival in stage I–III CRC.

Methods: We retrospectively analyzed data from 556 colorectal cancer patients at two hospitals, with one serving as the validation cohort. Receiver operating characteristic (ROC) curves were used to determine optimal cutoff values for PINI and LMR, and the area under the curve (AUC) was applied to assess predictive performance. KKaplan–Meier analysis showed that lower PINI and LMR were associated with shorter overall survival (OS). The INPR, integrating both markers, demonstrated superior accuracy. Variables linked to OS were selected using the Boruta algorithm and multivariable Cox regression, and a nomogram model was developed and validated internally and externally.

Results: The Youden index identified optimal cutoff values of 3.50 for PINI and 2.65 for LMR, with low levels independently predicting shorter OS. The INPR, integrating both, stratified patients into low-, intermediate-, and high-risk groups, with 5-year OS rates of 93.30%, 59.35%, and 28.57% in the training cohort (p<0.001). INPR outperformed either marker alone, showing higher AUC. A nomogram incorporating variables selected by the Boruta algorithm and multivariable Cox regression demonstrated stable and superior prognostic performance in both internal and external validation.

Conclusion: Our findings demonstrate that INPR is a simple, accessible, and effective prognostic tool for postoperative risk stratification in stage I–III CRC patients, providing valuable guidance for optimizing individualized treatment strategies.

Introduction

According to the latest global cancer statistics, colorectal cancer (CRC) ranks as the third most common malignancy, accounting for approximately 9.6% of all newly diagnosed cancers. It is also the second leading cause of cancer-related death, responsible for about 9.3% of cancer mortality worldwide (1). Despite advances in surgical techniques, perioperative management, and adjuvant therapies in recent years, the long-term prognosis of patients with stage I–III CRC remains unsatisfactory (2, 3). Even after curative resection, 20%–40% of patients experience recurrence or metastasis within five years (4–6). Therefore, accurately identifying high-risk patients in the early postoperative period and optimizing follow-up frequency and individualized treatment strategies are critical challenges in current clinical practice.

An increasing body of evidence indicates that the host’s systemic inflammatory response and nutritional status play pivotal roles in tumor initiation, progression, and therapeutic response (6, 7). Inflammatory mediators can promote tumor cell proliferation, angiogenesis, and immune evasion, whereas malnutrition may impair immune surveillance and treatment tolerance (8–10). Composite indices that integrate inflammatory and nutritional parameters have attracted considerable attention because they are simple, low-cost, and highly reproducible, making them valuable tools for prognostic assessment in cancer.

Currently, multiple hematological inflammation- and nutrition-related biomarkers have been applied in CRC prognostic studies, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) (11–14). These indices are easily obtainable, inexpensive, and reproducible, offering promising clinical applicability. However, their predictive accuracy can be influenced by acute stress, comorbidities, and treatment-related factors, limiting their generalizability. Moreover, most studies have focused on single markers, which fail to comprehensively reflect the multidimensional effects of inflammation, immunity, and nutrition.

The Prognostic Immune and Nutritional Index (PINI), calculated from serum albumin and monocyte counts, reflects the host’s nutritional reserves and immune status (15, 16). Low PINI levels have been associated with poor outcomes in gastrointestinal and other solid malignancies. Similarly, the lymphocyte-to-monocyte ratio (LMR) represents the balance between antitumor immune activity and tumor-promoting inflammatory responses (17, 18). A low LMR indicates reduced lymphocyte-mediated cytotoxicity and increased monocyte-driven tumor progression, which may lead to worse survival outcomes.

Although both PINI and LMR have demonstrated independent prognostic value in multiple studies, single indices remain limited in predictive power and may not capture the complex host–tumor interactions driving CRC progression. This limitation underscores the need for integrative approaches that combine multidimensional biological information to improve prognostic accuracy. Based on this rationale, we propose a novel composite index—the immune-nutritional prognostic ratio (INPR)—which integrates PINI and LMR into a single score. While PINI reflects the nutritional and immune baseline of the patient, LMR captures the dynamic inflammatory–immune balance influencing tumor progression. Combining these parameters may offer a more comprehensive characterization of a patient’s immune-nutritional status and inflammatory response, thereby improving risk assessment.

To date, no study has systematically evaluated the prognostic value of combining PINI and LMR in patients with stage I–III CRC after curative resection, nor has any study integrated the two into a single model to predict 1-, 3-, and 5-year survival. Our study integrates INPR with clinicopathological features to construct a nomogram model using the Boruta feature selection algorithm and multivariable Cox regression. Both internal and external validation were performed to assess its generalizability and clinical applicability.

This study aims to investigate the value of INPR in predicting overall survival (OS) after curative resection in patients with stage I–III CRC. We hypothesized that INPR would provide superior prognostic accuracy compared with single indices and that an INPR-based nomogram could serve as an effective tool for postoperative risk stratification, ultimately supporting more individualized follow-up and treatment strategies.

Patients and methods

Study population

From January 2016 to December 2017, a total of 862 patients with CRC were assessed for eligibility at two centers. After excluding 306 patients who did not meet the eligibility criteria, declined participation, or were removed for other reasons, 556 patients were finally included in the study. Among them, 389 patients from the Affiliated Cancer Hospital of Xinjiang Medical University (XJCH) comprised the training cohort, while 167 patients from the People’s Hospital of Bortala Mongolian Autonomous Prefecture (PHBM) in Xinjiang were assigned to the external validation cohort (Figure 1A). The inclusion criteria were as follows (1): primary CRC confirmed by postoperative histopathology; (2) radical surgical resection performed; (3) age > 18 years; (4) preoperative laboratory parameters available within 1 week before surgery; and (5) complete and reliable clinical data, with the ability to complete follow-up. The exclusion criteria were (1) having non-primary CRC; (2) with other primary cancers; (3) patients with unresectable distant metastases; (4) patients with hematological and autoimmune diseases; (5) patients with severe hepatic or renal insufficiency or diseases causing malnutrition; and (6) patients who received long-term parenteral nutritional support prior to surgery due to gastrointestinal dysfunction or malnutrition. Routine short-term fluid or glucose supplementation during the 24–48 hour preoperative fasting period was not considered parenteral nutrition.

Figure 1

Figure 1. Study flowchart and determination of optimal cutoffs for LMR and PINI. (A) Flow diagram of patient enrollment, exclusion, and cohort allocation. (B) Receiver operating characteristic (ROC) curves of LMR and PINI for overall survival in the training cohort. (C) Restricted cubic spline (RCS) analysis showing the nonlinear association between LMR and overall survival. (D) RCS analysis showing the nonlinear association between PINI and overall survival.

The study was reviewed and approved by the Ethics Committee of the Affiliated Cancer Hospital of Xinjiang Medical University (Approval No. K-2024056) and the Ethics Committee of the People’s Hospital of Bortala Mongolian Autonomous Prefecture (Approval No. LLSH20241221). All procedures were conducted in accordance with the Declaration of Helsinki, and informed consent was obtained from all participants and/or their legal guardians. This study is a retrospective observational cohort analysis and does not constitute a clinical trial; therefore, trial registration was not required according to ICMJE and CONSORT guidelines.

Data collection

Clinical and pathological data were retrospectively retrieved from the electronic medical record systems of the Affiliated Cancer Hospital of Xinjiang Medical University and the People’s Hospital of Bortala Mongolian Autonomous Prefecture in Xinjiang Uygur Autonomous Region, respectively. The dataset included: (1) demographic and baseline characteristics such as age, sex, height, weight, and history of smoking or alcohol consumption; (2) preoperative laboratory parameters obtained within 3–5 days before surgery, prior to the initiation of fasting or bowel preparation, including platelet, lymphocyte, neutrophil, and monocyte counts, hemoglobin, albumin, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9) levels; (3) postoperative pathological findings, including tumor differentiation grade, vascular invasion, perineural invasion, and TNM classification; (4) follow-up information including survival status and survival time. Pathological staging was determined in accordance with the 8th edition (2018) of the American Joint Committee on Cancer (AJCC) Cancer Staging Manual.

Patients were followed up through outpatient visits and telephone contact every 3 months during the first 2 years after surgery and every 6 months thereafter. When telephone contact was unsuccessful, survival status was obtained from the national cancer registry. Follow-up lasted 60–80 months (median, 70 months). Patients lost to follow-up or alive without recurrence at the last follow-up were censored in the survival analysis.

Calculated from hematological indices: PINI = [albumin (g/dL) × 0.9] - [absolute monocyte count (/μL) × 0.0007]; LMR = lymphocytes count (10⁹/L)/monocytes count (10⁹/L).

Statistical analysis

All statistical analyses were performed using SPSS software version 29.0 and R software version 4.4.1. Baseline clinical characteristics were summarized as follows: categorical variables were presented as counts and percentages and compared between groups using the chi-square test or Fisher’s exact test, as appropriate. Continuous variables were analyzed using the independent-samples t-test or one-way analysis of variance (ANOVA), while the Wilcoxon rank-sum test was applied for non-normally distributed or ordinal data. Differences in general clinical and pathological characteristics between the training and validation cohorts were also examined. ROC curve analysis was conducted in the training cohort to determine the optimal cutoff values for PINI and LMR, and the AUC was calculated to assess their predictive performance. These predefined thresholds were subsequently applied consistently in the validation cohort without recalibration. Kaplan–Meier survival curves were plotted for PINI and LMR, and the log-rank test was used to evaluate differences in OS. In addition, restricted cubic spline (RCS) models were applied to explore potential nonlinear associations between PINI, LMR, and mortality risk. A novel composite prognostic indicator, the INPR, was then established by integrating PINI and LMR, which represent complementary dimensions of systemic nutritional–inflammatory status and immune–inflammatory balance. Optimal cut-off values for PINI and LMR were determined using ROC curve analysis. Based on these thresholds, a novel composite prognostic indicator was established with the following scoring criteria: INPR = 0 (PINI < 3.50 and LMR < 2.65), INPR = 1 (PINI ≥ 3.50 or LMR ≥ 2.65), and INPR = 2 (PINI ≥ 3.50 and LMR ≥ 2.65). Patients were subsequently categorized into three biologically and clinically interpretable risk groups according to their combined PINI/LMR status, allowing for improved prognostic discrimination and clinical applicability. Univariate Cox regression analysis was first used to identify potential prognostic factors. To determine independent predictors of OS, we employed the Boruta algorithm in combination with multivariable Cox proportional hazards regression analysis. Based on the variables identified by these methods, a nomogram model was developed using the “rms” package in R to predict individual survival probabilities for CRC patients. The performance of the nomogram was assessed using ROC curves and calibration plots, and its predictive accuracy was validated in both the internal (training cohort) and external (validation cohort) datasets. The P value < 0.05 was considered statistically significant for all analyses. To assess potential multicollinearity between the composite indices integrated into the INPR model, variance inflation factor (VIF) and tolerance analyses were performed prior to multivariable modeling. The results demonstrated that all variables included in the construction of INPR exhibited low collinearity (all VIF values < 2.0), indicating no significant multicollinearity among PINI, LMR, or their constituent components. These findings support the statistical independence and methodological feasibility of integrating PINI and LMR into a composite prognostic indicator.

Results

Clinicopathological features of training and validation cohorts

A total of 556 patients diagnosed with CRC were included in this study, and their clinicopathological characteristics were collected and analyzed. As summarized in Table 1, the patients were divided into a training cohort (n = 389) and a validation cohort (n = 167). Baseline demographic and clinical characteristics were generally comparable between the two cohorts, with no statistically significant differences. Overall, 324 patients (58.3%) were male, and the median age was 62 years. Histories of smoking and alcohol consumption were present in 30.8% and 18.5% of patients, respectively. According to the TNM classification, 17.3% of patients were stage I, 44.8% were stage II, and 37.9% were stage III. Histologically, moderately differentiated adenocarcinoma was the predominant subtype (68.2%), followed by poorly differentiated (17.4%), undifferentiated (9.2%), and well-differentiated tumors (5.2%). Perineural invasion was observed in 94 patients (16.9%), and the same proportion of patients exhibited lymphovascular invasion. Regarding tumor markers, elevated CEA and CA19–9 levels were detected in 200 (36.0%) and 61 (11.0%) patients, respectively; these biomarkers are widely used to evaluate tumor burden and disease progression in CRC.

Table 1

Table 1. Patient demographics and baseline characteristics.

Association of PINI and LMR with clinicopathological characteristics in the training cohort

In the training cohort, PINI levels showed significant associations with several clinicopathological factors. Specifically, variations in PINI were significantly related to patient age (p = 0.003), alcohol consumption (p = 0.027), tumor T stage (p = 0.019), presence of perineural invasion (p = 0.002), and elevated CA19–9 levels (p = 0.004). Similarly, LMR levels were significantly correlated with T stage (p = 0.002) and overall TNM stage (p = 0.005). By contrast, neither PINI nor LMR demonstrated statistically significant associations with sex, body mass index (BMI), smoking status, N stage, tumor differentiation, vascular invasion, or CEA levels (Table 2).

Table 2

Table 2. Association of preoperative CIPI and PAR with clinicopathological features in the training cohort.

Optimal cutoff determination and prognostic significance of PINI and LMR

In the training cohort, ROC curve analysis was first performed to assess the prognostic discriminatory ability of PINI and LMR and to determine their optimal cutoff points. The AUC of PINI was 0.786, with an optimal cutoff value of 3.50; for LMR, the AUC was 0.694, with an optimal cutoff of 2.65. Based on these thresholds, PINI values ≥3.50 and <3.50 were categorized into high (H-PINI) and low (L-PINI) groups, respectively; similarly, LMR values ≥2.65 and <2.65 were classified as high (H-LMR) and low (L-LMR) groups (Figure 1B). RCS analysis further demonstrated that decreasing levels of both PINI and LMR were associated with a progressively increasing risk of death, indicating a stable and significant prognostic relationship between these indices and OS (Figures 1C, D).

Association of PINI and LMR with OS

In the training cohort, the median OS was 67 months. Kaplan–Meier survival analysis revealed that patients in the low PINI (L-PINI) group had significantly shorter OS than those in the high PINI (H-PINI) group (P < 0.001). Similarly, patients with low LMR (L-LMR) had significantly worse OS than those with high LMR (H-LMR) (P < 0.001). These findings indicate that low PINI or LMR levels are significantly associated with poorer OS in CRC patients (Figures 2A, B).

Figure 2

Figure 2. Kaplan–Meier survival analyses and predictive performance of INPR. (A) Overall survival (OS) stratified by PINI in the training cohort. (B) OS stratified by LMR in the training cohort. (C) OS stratified by INPR risk groups (INPR = 0, 1, 2). (D) Receiver operating characteristic (ROC) curve showing the predictive accuracy of INPR for OS.

INPR score stratification and association with survival outcomes

By integrating the prognostic information from PINI and LMR, we developed the INPR score (Table 3). In the training cohort of 389 patients, three distinct risk categories were defined: the high-risk group (INPR = 0), comprising patients with PINI < 3.50 and LMR < 2.65 (n = 42, 10.8%); the intermediate-risk group (INPR = 1), including those with PINI < 3.50 and LMR ≥ 2.65, or PINI ≥ 3.50 and LMR < 2.65 (n = 123, 31.62%); and the low-risk group (INPR = 2), consisting of patients with PINI ≥ 3.50 and LMR ≥ 2.65 (n = 224, 57.58%). Kaplan–Meier analysis revealed striking differences in 5-year OS across these groups, with survival rates of 28.57%, 59.35%, and 93.30%, respectively (P < 0.001; Figure 2C).

Table 3

Table 3. Scoring criteria of the Immune-Nutritional Prognostic Ratio (INPR) based on PINI and LMR levels.

In addition, comparative analysis demonstrated that INPR provided superior prognostic discrimination compared with either PINI or LMR alone. INPR achieved an area under the ROC curve (AUC) of 0.811, outperforming both PINI (AUC = 0.786) and LMR (AUC = 0.694), indicating improved predictive accuracy (Figure 2D).

Independent prognostic factors for OS in CRC patients

In the training cohort, univariate and multivariate Cox regression analyses were performed to evaluate clinicopathological variables associated with overall survival (OS) in CRC patients (Table 4). Univariate analysis revealed that BMI, N stage, TNM stage, tumor differentiation, nerve invasion, intravascular tumor emboli, CEA, PINI, LMR, and INPR were significantly associated with OS (P < 0.05). Multivariate analysis further identified tumor differentiation, CEA, PINI, LMR, and INPR as independent prognostic factors (P < 0.05). Specifically, patients with poorly differentiated tumors had a substantially higher risk of death compared with those with moderate differentiation (HR = 2.57, 95% CI: 1.55–4.27, P < 0.001). Elevated CEA levels were independently associated with shortened OS (HR = 2.53, 95% CI: 1.61–4.11, P < 0.001). A higher PINI (≥3.50) was associated with a significantly reduced mortality risk (HR = 0.57, 95% CI: 0.28–1.18, P = 0.030), while a higher LMR (≥2.65) independently predicted better OS (HR = 0.56, 95% CI: 0.33–0.96, P = 0.036). Regarding the novel scoring system, patients with INPR ≤1 exhibited a markedly higher risk of death compared with those with INPR = 2, with INPR demonstrating strong and consistent prognostic value in both univariate and multivariate analyses (HR = 0.17, 95% CI: 0.07–0.46, P < 0.001).

Table 4

Table 4. Univariate and multivariate analysis of influencing factors (Cox regression).

Collectively, these findings indicate that INPR offers superior prognostic stratification compared with traditional inflammatory and nutritional biomarkers. Based on this foundation, we further employed the Boruta algorithm to assess the relative importance of all candidate variables. The analysis identified eight variables as important contributors to OS, namely INPR, LMR, PINI, perineural invasion, tumor differentiation, tumor stage, N stage, and smoking status. In contrast, seven variables—including CA19-9, intravascular tumor emboli, T stage, drinking history, BMI, age, and sex—were not retained as important predictors. Notably, CEA fell into the “tentative” category as defined by the Boruta algorithm, suggesting uncertain relevance and the need for further evaluation (Figure 3A).

Figure 3

Figure 3. Construction and performance of the INPR-based nomogram. (A) Variable importance ranking for overall survival. (B) Nomogram integrating clinicopathological factors and INPR for predicting 1-, 3-, and 5-year overall survival. (C) Calibration curves of the nomogram at 1, 3, and 5 years. (D) Time-dependent ROC curves of the nomogram in the training cohort. (E) Time-dependent ROC curves of the nomogram in the validation cohort.

Development and validation of a prognostic nomogram for OS prediction in CRC

Based on the multivariate Cox regression analysis and Boruta feature selection, five key variables—CEA, tumor differentiation, PINI, LMR, and INPR—were incorporated into the nomogram to estimate individualized prognostic risk in patients with CRC. Among these predictors, INPR and tumor differentiation contributed most prominently to survival prediction, with patients exhibiting INPR ≤ 1 or poor differentiation showing a markedly increased mortality risk. Moreover, lower PINI and LMR values and higher CEA levels were each significantly associated with worse prognosis (Figure 3B).

For performance evaluation, we first performed internal validation. In the training cohort, time-dependent ROC analysis yielded AUCs of 0.847, 0.873, and 0.852 for predicting 1-, 3-, and 5-year OS, respectively, demonstrating strong discriminative performance (Figure 3D). Calibration curves showed excellent concordance between predicted and observed survival probabilities at all three time points (Figure 3C). In addition, the calibration plots and decision curve analyses for 1-, 3-, and 5-year OS (Figures 4A–F) consistently supported the nomogram’s high predictive accuracy and clinical utility.

Figure 4

Figure 4. Calibration and decision curve analysis (DCA) for predicting 1-, 3-, and 5-year overall survival (OS) in the training cohort. (A, C, E) Calibration curves for 1-, 3-, and 5-year OS. (B, D, F) DCA curves for 1-, 3-, and 5-year OS.

To further assess the robustness and generalizability of the model, we conducted external validation in an independent cohort. The nomogram yielded AUCs of 0.939, 0.778, and 0.865 for predicting 1-, 3-, and 5-year OS, respectively, which were comparable to those observed in the training cohort (Figure 3E). The calibration plots and decision curve analyses (Figures 5A–F) demonstrated consistent results, further supporting the nomogram’s strong prognostic performance and clinical decision-making utility. Overall, the concordant findings from both internal and external validation confirm that the nomogram offers excellent predictive accuracy and represents a reliable tool for OS risk stratification in CRC patients.

Figure 5

Figure 5. Calibration and decision curve analysis (DCA) for predicting 1-, 3-, and 5-year overall survival (OS) in the validation cohort. (A, C, E) Calibration curves for 1-, 3-, and 5-year OS. (B, D, F) DCA curves for 1-, 3-, and 5-year OS.

Discussion

CRC remains a major global health burden, and even after curative resection for stage I–III disease, the risks of recurrence and cancer-related mortality remain substantial (19). In this context, the development of clinically feasible, low-cost, and biologically interpretable prognostic tools is of critical importance. In the present study, we developed and externally validated a novel composite index, the INPR, by integrating PINI and LMR. Our findings demonstrate that INPR provides superior prognostic discrimination compared with either component alone and enables clinically meaningful risk stratification through a user-friendly nomogram, thereby offering a practical framework for individualized postoperative management.

Chronic inflammation has long been recognized as a central hallmark of cancer, playing a pivotal role in colorectal carcinogenesis and progression (20–22). Elevated circulating monocytes serve as precursors of tumor-associated macrophages (TAMs), which accumulate within the tumor microenvironment and are sustained by inflammatory mediators such as IL-6, tumor necrosis factor-α (TNF-α), and prostaglandin E2 (PGE2) (23–27). These cytokines activate persistent NF-κB and STAT3 signaling, establishing a chronic pro-tumorigenic inflammatory state that fosters tumor cell survival, epithelial–mesenchymal transition, and immune evasion (28–30). Conversely, lymphocytes, particularly CD8⁺ cytotoxic T lymphocytes and Th1-polarized CD4⁺ T cells, represent the core of adaptive antitumor immunity. A reduced lymphocyte count is associated with impaired interferon-γ production, diminished granzyme/perforin-mediated cytotoxicity, and enhanced susceptibility to immune checkpoint–mediated exhaustion (31, 32). Consequently, a low LMR biologically reflects a shift in the immune equilibrium toward a monocyte/TAM-dominant, immunosuppressive microenvironment and provides a strong mechanistic basis for its adverse prognostic impact in CRC (33–35).

Therefore, the LMR functions as an integrated marker reflecting the balance between antitumor immune activity and tumor-promoting inflammatory responses. A decreased LMR indicates impaired host immunity accompanied by heightened protumor inflammation, both of which are strongly linked to poor prognosis. Consistent with previous evidence in gastrointestinal cancers, our study demonstrated that a low LMR was significantly associated with shorter OS in patients with CRC (36, 37).

Beyond inflammation, nutritional status represents a critical determinant of host–tumor interactions in colorectal cancer. Serum albumin is not merely an indicator of protein reserves but also a functional antioxidant that buffers reactive oxygen species and modulates systemic redox homeostasis. Hypoalbuminemia is therefore associated with heightened oxidative stress, impaired drug transport capacity, and dysfunctional immune cell signaling. In malnourished states, immune cells undergo profound immunometabolic disturbances, characterized by mitochondrial dysfunction, reduced ATP generation, and impaired amino acid availability, which compromise effector T-cell proliferation and macrophage polarization balance (38–40). Furthermore, cancer-associated metabolic reprogramming, including the Warburg effect and enhanced fatty acid oxidation, creates a nutrient-deprived tumor microenvironment that competitively suppresses antitumor lymphocyte function while favoring the survival and suppressive activity of TAMs (41, 42). This metabolic competition reinforces a vicious cycle of immune suppression and tumor progression. Taken together, these findings provide a robust biological rationale for incorporating nutritional parameters into composite indices such as INPR, which reflect both systemic and local immunometabolic perturbations in CRC (43).

INPR was developed based on the biologically complementary prognostic roles of the PINI and LMR. Although both indices incorporate monocyte counts, they capture distinct and non-redundant biological dimensions of the host–tumor interaction. PINI primarily reflects the interaction between nutritional reserve (serum albumin) and systemic inflammatory burden, thereby representing the host’s metabolic and inflammatory vulnerability. In contrast, LMR reflects the dynamic balance between antitumor adaptive immunity (lymphocytes) and pro-tumor innate inflammatory activity (monocytes), serving as a surrogate indicator of immune equilibrium. We additionally confirmed through formal multicollinearity diagnostics that PINI and LMR did not exhibit significant statistical redundancy, supporting their integration into a composite index. Therefore, INPR is not a mathematically redundant construct, but rather a biologically rational and multidimensional biomarker that captures immune, inflammatory, and nutritional dysregulation in colorectal cancer. This complementary design underlies the superior prognostic performance of INPR compared with single-parameter indices. Biologically, an elevated monocyte burden reflects enhanced recruitment of circulating monocytes into the tumor microenvironment, where they differentiate into TAMs under the influence of cytokines such as IL-6, transforming growth factor-β (TGF-β), and colony-stimulating factor-1 (CSF-1). These TAMs are frequently polarized toward an immunosuppressive M2-like phenotype through activation of canonical signaling pathways, including STAT3, NF-κB, and HIF-1α, thereby promoting angiogenesis, extracellular matrix remodeling, and immune escape via vascular endothelial growth factor (VEGF) and matrix metalloproteinases (44, 45). Simultaneously, lymphopenia reflects impaired cytotoxic immune surveillance, characterized by functional exhaustion of CD8⁺ T cells and natural killer cells through immune checkpoint axes such as PD-1/PD-L1 and CTLA-4, further reinforcing an immunosuppressive tumor niche (46–48). In parallel, hypoalbuminemia represents not only a state of malnutrition but also a surrogate marker of systemic oxidative stress and chronic inflammatory catabolism, which disrupts immune cell metabolism and effector function. Emerging evidence suggests that nutritional deprivation and cancer-associated metabolic reprogramming, including aerobic glycolysis and fatty acid oxidation, competitively impair antitumor immunity, creating a maladaptive feedback loop between immune dysfunction and metabolic stress. Therefore, INPR constitutes a composite biomarker that transcends isolated hematological parameters, reflecting convergent activation of inflammatory signaling, immune exhaustion, TAM-mediated immunosuppression, and metabolic–nutritional dysregulation, which together drive tumor progression and adverse clinical outcomes in CRC.

In recent years, the prognostic landscape of colorectal cancer has increasingly shifted toward multidimensional models that integrate immune contexture, metabolic reprogramming, and TME features. In particular, the concept of “immune-hot” tumors, characterized by abundant cytotoxic lymphocyte infiltration, and “immune-cold” tumors, marked by immune exclusion and macrophage-dominated suppressive niches, has emerged as a powerful framework to explain heterogeneity in clinical outcomes (49–51). Within this paradigm, the balance between lymphocytes and monocytes reflected by LMR may serve as a readily accessible peripheral surrogate of tumor immune phenotypes. Recent studies focusing on hot–cold tumor-related signatures have demonstrated strong prognostic relevance in CRC. Several systemic inflammation- and nutrition-related prognostic scores, including the NLR, PLR, SII, PNI, and controlling nutritional status (CONUT) score, have been proposed for colorectal cancer risk stratification (52–56). However, most of these tools focus on a single biological dimension and therefore fail to comprehensively capture the complex interaction between immune competence, inflammatory burden, and metabolic resilience. In contrast, INPR was specifically designed to integrate complementary immunological and nutritional information into a unified, biologically coherent framework. Our results demonstrated that INPR achieved superior prognostic performance compared with either PINI or LMR alone, supporting the concept that multidimensional indices provide incremental clinical value beyond traditional unidimensional markers.

The principal clinical value of INPR lies in its simplicity, accessibility, and capacity to meaningfully refine postoperative, risk-adapted management in colorectal cancer. Unlike conventional clinicopathological parameters that primarily reflect tumor burden, INPR captures the complex biological interplay between host systemic vulnerability and tumor aggressiveness. In clinical practice, patients classified as high risk (INPR = 0) may benefit from intensified surveillance strategies and consideration of more aggressive adjuvant therapeutic approaches, whereas those in the low-risk category (INPR = 2) may be suitable candidates for de-escalated follow-up and avoidance of overtreatment. In the multivariate model, some traditional clinicopathological variables, such as TNM stage and neural invasion, did not retain independent statistical significance. This finding may be explained by the comprehensive nature of INPR, which integrates systemic nutritional, inflammatory, and immune-related information and may partially capture the downstream biological effects of tumor burden and invasive behavior. Importantly, this does not undermine the clinical relevance of these established prognostic factors but rather highlights the strong integrated prognostic performance of INPR. Although the proportion of patients classified as extremely high risk represented a relatively small subset of the overall cohort, this group consistently exhibited markedly unfavorable survival outcomes in both the derivation and external validation cohorts. This distribution likely reflects the underlying biological heterogeneity of colorectal cancer, rather than statistical instability. Importantly, the reproducibility of this risk pattern across independent cohorts supports the robustness and clinical relevance of INPR in identifying biologically aggressive disease phenotypes. Nevertheless, large-scale, prospective, multicenter studies are still warranted to further confirm the stability, generalizability, and real-world utility of this stratification strategy.

It is also important to consider the potential influence of regional and ethnic characteristics on the observed associations. The present cohort was derived from Xinjiang, a region characterized by unique dietary patterns, cultural habits, and substantial ethnic diversity. Traditional dietary structures, including relatively high intake of animal protein and fat in certain subpopulations, as well as genetic heterogeneity, may influence baseline nutritional indices such as serum albumin and systemic inflammatory status. These population-specific features could affect the distribution of immune–nutritional biomarkers and their prognostic thresholds. Therefore, further validation of the INPR model in geographically and ethnically diverse cohorts is essential to confirm its universal applicability.

Several limitations of the present study should be acknowledged. First, as a retrospective observational study, the possibility of selection bias cannot be completely excluded. Second, this cohort was derived from only two hospitals in Xinjiang, China, which may limit the generalizability of our findings to broader populations; therefore, further validation in larger, multicenter, and multi-ethnic cohorts is warranted. Third, the analysis was restricted to baseline preoperative parameters, and dynamic longitudinal changes in immune–nutritional status were not evaluated. Fourth, detailed information on postoperative adjuvant chemotherapy, including treatment regimens and completion rates, was not uniformly available and therefore could not be incorporated into the multivariable models. Given the established impact of adjuvant therapy on survival in stage II–III colorectal cancer, residual confounding cannot be entirely excluded, and the independent prognostic value of INPR should be interpreted with appropriate caution. Finally, the underlying biological mechanisms through which INPR influences colorectal cancer progression remain incompletely understood and require further mechanistic investigation.

Conclusion

In summary, our study demonstrated that INPR is a practical and robust prognostic indicator that enables refined risk stratification in patients with stage I–III CRC. Compared with individual markers, INPR exhibited markedly superior predictive performance. When incorporated into a nomogram, INPR provides a promising framework for individualized postoperative management. Future prospective, multicenter investigations are warranted to validate its clinical applicability and to elucidate the biological mechanisms underlying its prognostic value.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by The Ethics Committee of Xinjiang Medical University Cancer Hospital and The Ethics Committee of the People’s Hospital of Bortala Mongolian Autonomous Prefecture. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

KW: Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. BZ: Conceptualization, Data curation, Investigation, Methodology, Supervision, Writing – review & editing. KL: Data curation, Formal Analysis, Investigation, Project administration, Writing – review & editing. ZYZ: Data curation, Formal Analysis, Investigation, Methodology, Writing – review & editing. XZ: Funding acquisition, Investigation, Supervision, Validation, Writing – review & editing. J-MG: Data curation, Investigation, Resources, Supervision, Validation, Writing – review & editing. RA: Supervision, Validation, Writing – review & editing. EW: Supervision, Validation, Writing – review & editing. YP: Supervision, Validation, Writing – review & editing. LL: Supervision, Validation, Writing – review & editing. ZLZ: Methodology, Project administration, Supervision, Validation, Writing – review & editing, Conceptualization. YC: Methodology, Project administration, Supervision, Validation, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was financially supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Project No. 2022D01C297).

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2024) 74:229–63. doi: 10.3322/caac.21834

PubMed Abstract | Crossref Full Text | Google Scholar

2. Benson AB, Venook AP, Adam M, Chang G, Chen YJ, Ciombor KK, et al. Colon cancer, version 3.2024, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. (2024) 22. doi: 10.6004/jnccn.2024.0029

PubMed Abstract | Crossref Full Text | Google Scholar

3. Chagpar R, Xing Y, Chiang YJ, Feig BW, Chang GJ, You YN, et al. Adherence to stage-specific treatment guidelines for patients with colon cancer. J Clin Oncol. (2012) 30:972–9. doi: 10.1200/jco.2011.39.6937

PubMed Abstract | Crossref Full Text | Google Scholar

4. Osterman E and Glimelius B. Recurrence risk after up-to-date colon cancer staging, surgery, and pathology: analysis of the entire swedish population. Dis Colon Rectum. (2018) 61:1016–25. doi: 10.1097/dcr.0000000000001158

PubMed Abstract | Crossref Full Text | Google Scholar

5. Chi H, Huang J, Yan Y, Jiang C, Zhang S, Chen H, et al. Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms. Front Mol Biosci. (2023) 10:1254232. doi: 10.3389/fmolb.2023.1254232

PubMed Abstract | Crossref Full Text | Google Scholar

6. Diakos CI, Charles KA, McMillan DC, and Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. (2014) 15:e493–503. doi: 10.1016/s1470-2045(14)70263-3

PubMed Abstract | Crossref Full Text | Google Scholar

7. Pan C, Deng D, Wei T, Wu Z, Zhang B, Yuan Q, et al. Metabolomics study identified bile acids as potential biomarkers for gastric cancer: A case control study. Front Endocrinol (Lausanne). (2022) 13:1039786. doi: 10.3389/fendo.2022.1039786

PubMed Abstract | Crossref Full Text | Google Scholar

8. Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, et al. ESPEN guidelines on nutrition in cancer patients. Clin Nutr. (2017) 36:11–48. doi: 10.1016/j.clnu.2016.07.015

PubMed Abstract | Crossref Full Text | Google Scholar

9. Ma H, Liu J, Jin H, Zhang M, Liang Q, and Guo Z. Comprehensive characterization of NK cell-related genes in cutaneous melanoma identified a novel prognostic signature for predicting the prognosis, immunotherapy, and chemotherapy efficacy. Discov Oncol. (2025) 16:1243. doi: 10.1007/s12672-025-03074-1

PubMed Abstract | Crossref Full Text | Google Scholar

10. Yuan K, Zhao S, Ye B, Wang Q, Liu Y, Zhang P, et al. A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients. Front Pharmacol. (2023) 14:1192777. doi: 10.3389/fphar.2023.1192777

PubMed Abstract | Crossref Full Text | Google Scholar

11. Kim JH, Lee JY, Kim HK, Lee JW, Jung SG, Jung K, et al. Prognostic significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with stage III and IV colorectal cancer. World J Gastroenterol. (2017) 23:505–15. doi: 10.3748/wjg.v23.i3.505

PubMed Abstract | Crossref Full Text | Google Scholar

12. Takeda K, Umezawa R, Yamamoto T, Takahashi N, Suzuki Y, Kishida K, et al. Lymphocytopenia following adjuvant radiotherapy for breast cancer. Prec Radiat Oncol. (2024) 8:22–9. doi: 10.1002/pro6.1221

PubMed Abstract | Crossref Full Text | Google Scholar

13. Ozawa T, Ishihara S, Nishikawa T, Tanaka T, Tanaka J, Kiyomatsu T, et al. The preoperative platelet to lymphocyte ratio is a prognostic marker in patients with stage II colorectal cancer. Int J Colore Dis. (2015) 30:1165–71. doi: 10.1007/s00384-015-2276-9

PubMed Abstract | Crossref Full Text | Google Scholar

14. Keskinkilic M, Semiz HS, Ataca E, and Yavuzsen T. The prognostic value of immune-nutritional status in metastatic colorectal cancer: Prognostic Nutritional Index (PNI). Supp Care Can. (2024) 32:374. doi: 10.1007/s00520-024-08572-6

PubMed Abstract | Crossref Full Text | Google Scholar

15. Xie H, Wei L, Liu M, Liang Y, Yuan G, Gao S, et al. Prognostic significance of preoperative prognostic immune and nutritional index in patients with stage I-III colorectal cancer. BMC Can. (2022) 22:1316. doi: 10.1186/s12885-022-10405-w

PubMed Abstract | Crossref Full Text | Google Scholar

16. Xu Z, Zhang M, Guo Z, Chen L, Yang X, Li X, et al. Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma. Apoptosis. (2023) 28:860–80. doi: 10.1007/s10495-023-01832-6

PubMed Abstract | Crossref Full Text | Google Scholar

17. Özkan O, Peker P, Geçgel A, and Göker E. Prognostic value of preoperative lymphocyte-to-monocyte ratio in patients with recurrent colorectal cancer. Med (Kaunas). (2025) 61:10. doi: 10.3390/medicina61040707

PubMed Abstract | Crossref Full Text | Google Scholar

18. Zhang M, Zhang F, Wang J, Liang Q, Zhou W, and Liu J. Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments. J Transl Med. (2024) 22:423. doi: 10.1186/s12967-024-05237-0

PubMed Abstract | Crossref Full Text | Google Scholar

19. Meng K and Lu H. Clinical application of high-LET radiotherapy combined with immunotherapy in Malignant tumors. Prec Radiat Oncol. (2024) 8:42–6. doi: 10.1002/pro6.1225

PubMed Abstract | Crossref Full Text | Google Scholar

20. Hanahan D and Weinberg RA. Hallmarks of cancer: the next generation. Cell. (2011) 144:646–74. doi: 10.1016/j.cell.2011.02.013

PubMed Abstract | Crossref Full Text | Google Scholar

21. Zhang B, Liu J, Mo Y, Zhang K, Huang B, and Shang D. CD8+ T cell exhaustion and its regulatory mechanisms in the tumor microenvironment: key to the success of immunotherapy. Front Immunol. (2024) 15:1476904. doi: 10.3389/fimmu.2024.1476904

PubMed Abstract | Crossref Full Text | Google Scholar

22. Liu R, Tang L, Liu Y, Hu H, and Liu J. Causal relationship between immune cell signatures and colorectal cancer: a bi-directional, two-sample mendelian randomization study. BMC Can. (2025) 25:387. doi: 10.1186/s12885-025-13576-4

PubMed Abstract | Crossref Full Text | Google Scholar

23. Qian BZ and Pollard JW. Macrophage diversity enhances tumor progression and metastasis. Cell. (2010) 141:39–51. doi: 10.1016/j.cell.2010.03.014

PubMed Abstract | Crossref Full Text | Google Scholar

24. Song B, Chi H, Peng G, Song Y, Cui Z, Zhu Y, et al. Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma. Front Oncol. (2022) 12:975255. doi: 10.3389/fonc.2022.975255

PubMed Abstract | Crossref Full Text | Google Scholar

25. Mashayekhi V, Schomisch A, Rasheed S, Aparicio-Puerta S, Risch T, Yildiz D, et al. The RNA binding protein IGF2BP2/IMP2 alters the cargo of cancer cell-derived extracellular vesicles supporting tumor-associated macrophages. Cell Commun Signal. (2024) 22:344. doi: 10.1186/s12964-024-01701-y

PubMed Abstract | Crossref Full Text | Google Scholar

26. Mantovani A, Sica A, Allavena P, Garlanda C, and Locati M. Tumor-associated macrophages and the related myeloid-derived suppressor cells as a paradigm of the diversity of macrophage activation. Hum Immunol. (2009) 70:325–30. doi: 10.1016/j.humimm.2009.02.008

PubMed Abstract | Crossref Full Text | Google Scholar

27. Yang Y, Song L, Cao J, Zeng W, Liu J, Shi X, et al. Peripheral monocyte count is associated with the risk of liver metastasis: A study of 7187 newly diagnosed patients with colorectal cancer. Indian J Can. (2025) 62:45–51. doi: 10.4103/ijc.ijc_1126_21

PubMed Abstract | Crossref Full Text | Google Scholar

28. Omatsu M, Nakanishi Y, Iwane K, Aoyama N, Duran A, Muta Y, et al. THBS1-producing tumor-infiltrating monocyte-like cells contribute to immunosuppression and metastasis in colorectal cancer. Nat Commun. (2023) 14:5534. doi: 10.1038/s41467-023-41095-y

PubMed Abstract | Crossref Full Text | Google Scholar

29. Wu D, Liu Y, Liu J, Ma L, and Tong X. Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma. Sci Rep. (2024) 14:17460. doi: 10.1038/s41598-024-68111-5

PubMed Abstract | Crossref Full Text | Google Scholar

30. van Baarle L, De Simone V, Schneider L, Santhosh S, Abdurahiman S, Biscu F, et al. IL-1R signaling drives enteric glia-macrophage interactions in colorectal cancer. Nat Commun. (2024) 15:6079. doi: 10.1038/s41467-024-50438-2

PubMed Abstract | Crossref Full Text | Google Scholar

31. Ballhausen A, Swoboda S, Horst D, Fruehauf S, Graeven U, Müller L, et al. Spatial tumor immune microenvironment as a prognostic and predictive biomarker in anti-EGFR-based maintenance for RAS wt metastatic CRC-the panaMa (AIO KRK0212) trial. Clin Cancer Res. (2025) 31:4049–58. doi: 10.1158/1078-0432.CCR-25-0879

PubMed Abstract | Crossref Full Text | Google Scholar

32. Xu X, Yin Y, Zhang L, Wang D, Zhou Y, and Li Q. Research progress of cardiotoxicity caused by radiotherapy in breast cancer. Prec Radiat Oncol. (2024) 8:153–8. doi: 10.1002/pro6.1241

PubMed Abstract | Crossref Full Text | Google Scholar

33. Saris J, Li Yim AYF, Bootsma S, et al. Peritoneal resident macrophages constitute an immunosuppressive environment in peritoneal metastasized colorectal cancer. Nat Commun. (2025) 16:3669. doi: 10.1038/s41467-025-58999-6

PubMed Abstract | Crossref Full Text | Google Scholar

34. Calabrò A, Drommi F, Sidoti Migliore G, Pezzino G, Vento G, Freni J, et al. Neutrophil-like monocytes increase in patients with colon cancer and induce dysfunctional TIGIT+ NK cells. Int J Mol Sci. (2024) 25:8470. doi: 10.3390/ijms25158470

PubMed Abstract | Crossref Full Text | Google Scholar

35. Chen S, Chang WH, Zhang J, Liu XY, Gao T, Qi XW, et al. A longitudinal dynamic change in LMR can be a biomarker for recurrence in fusobacterium nucleatum-positive colorectal cancer patients. J Inflammation Res. (2024) 17:11587–604. doi: 10.2147/JIR.S489432

PubMed Abstract | Crossref Full Text | Google Scholar

36. Fernandez M, Todeschini L, P Keenan B, Rosenberg D, Hernandez S, Zampese M, et al. Novel computational analysis identifies cytotoxic lymphocyte-to-monocyte balance in tumors as a predictor of recurrence-free survival in colorectal carcinoma. Ann Surg Oncol. (2025) 32:6980–90. doi: 10.1245/s10434-025-17599-w

PubMed Abstract | Crossref Full Text | Google Scholar

37. Huang X, Chi H, Gou S, Guo X, Li L, Peng G, et al. An aggrephagy-related lncRNA signature for the prognosis of pancreatic adenocarcinoma. Genes (Basel). (2023) 14:124. doi: 10.3390/genes14010124

PubMed Abstract | Crossref Full Text | Google Scholar

38. Chan JC, Chan DL, Diakos CI, Engel A, Pavlakis N, Gill A, et al. The lymphocyte-to-monocyte ratio is a superior predictor of overall survival in comparison to established biomarkers of resectable colorectal cancer. Ann Surg. (2017) 265:539–46. doi: 10.1097/sla.0000000000001743

PubMed Abstract | Crossref Full Text | Google Scholar

39. Gharzeddine K, Gonzalez Prieto C, Malier M, Hennot C, Grespan R, Yamaryo-Botté Y, et al. Metabolic reprogramming of hypoxic tumor-associated macrophages through CSF-1R targeting favors treatment efficiency in colorectal cancers. J Immunother Can. (2024) 12:e009602. doi: 10.1136/jitc-2024-009602

PubMed Abstract | Crossref Full Text | Google Scholar

40. Zhang P, Zhang H, Tang J, Ren Q, Zhang J, Chi H, et al. The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy. Aging (Albany NY). (2023) 15:10305–29. doi: 10.18632/aging.205077

PubMed Abstract | Crossref Full Text | Google Scholar

41. Kabiljo J, Theophil A, Homola J, Renner A, Stürzenbecher N, Ammon D, et al. Cancer-associated fibroblasts shape early myeloid cell response to chemotherapy-induced immunogenic signals in next generation tumor organoid cultures. J Immunother Can. (2024) 12:e009494. doi: 10.1136/jitc-2024-009494

PubMed Abstract | Crossref Full Text | Google Scholar

42. Liu J, Zhang W, Chen L, Wang X, Mao X, Wu Z, et al. VSIG4 promotes tumour-associated macrophage M2 polarization and immune escape in colorectal cancer via fatty acid oxidation pathway. Clin Transl Med. (2025) 15:e70340. doi: 10.1002/ctm2.70340

PubMed Abstract | Crossref Full Text | Google Scholar

43. Zhou M, Gu Q, Zhou M, Yang S, Liu Y, Guan B, et al. Extensive study on the associations of 12 composite inflammatory indices with colorectal cancer risk and mortality a cross-sectional analysis of NHANES 2001-2020. Int J Surg. doi: 10.1097/JS9.0000000000002996

PubMed Abstract | Crossref Full Text | Google Scholar

44. Fan J, Wang L, Zhang C, Wu L, Han L, Zhang X, et al. PDIA3 driven STAT3/PD-1 signaling promotes M2 TAM polarization and aggravates colorectal cancer progression. Aging (Albany NY). (2024) 16:8880–97. doi: 10.18632/aging.205847

PubMed Abstract | Crossref Full Text | Google Scholar

45. Fan X, Lu L, Wang S, Zhou Q, and Lin B. A novel necroptosis related prognostic signature for skin cutaneous melanoma based on transcriptome and single cell sequencing analysis. Sci Rep. (2025) 15:20650. doi: 10.1038/s41598-025-07829-2

PubMed Abstract | Crossref Full Text | Google Scholar

46. Zhan Y, Xu J, Zhang Z, Hu Y, Li Y, Qian J, et al. Targeting SPHK1 in macrophages remodels the tumor microenvironment and enhances anti-PD-1 immunotherapy efficacy in colorectal cancer liver metastasis. Cancer Commun (Lond). (2025) 45:1203–28. doi: 10.1002/cac2.70047

PubMed Abstract | Crossref Full Text | Google Scholar

47. Zhang B, Chen X, Wang Z, Guo F, Zhang X, Huang B, et al. Identifying endoplasmic reticulum stress-related molecular subtypes and prognostic model for predicting the immune landscape and therapy response in pancreatic cancer. Aging (Albany NY). (2023) 15:10549–79. doi: 10.18632/aging.205094

PubMed Abstract | Crossref Full Text | Google Scholar

48. Ye X, Liu Y, Wei L, Sun Y, Zhang X, Wang H, et al. Monocyte/macrophage-mediated transport of dual-drug ZIF nanoplatforms synergized with programmed cell death protein-1 inhibitor against microsatellite-stable colorectal cancer. Adv Sci (Weinh). (2024) 11:e2405886. doi: 10.1002/advs.202405886

PubMed Abstract | Crossref Full Text | Google Scholar

49. Li Y, Xiong H, Liang T, Liu Y, He L, Tan W, et al. Missed colorectal cancer diagnosis by screening colonoscopy based on the PLCO cancer screening trial. Int J Colore Dis. (2025) 40:206. doi: 10.1007/s00384-025-04952-4

PubMed Abstract | Crossref Full Text | Google Scholar

50. Xiao Y, Zhang G, Xiao Y, Li Z, Liu H, He L, et al. Prognostic stratification of colorectal cancer by immune profiling reveals SPP1 as a key indicator for tumor immune status. Oncologie. doi: 10.1515/oncologie-2025-0248

Crossref Full Text | Google Scholar

51. Chi J, Luo GY, Shan HB, Lin JZ, Wu XJ, and Li JJ. Recanalization of anastomotic occlusion following rectal cancer surgery using a rendezvous endoscopic technique with transillumination: A case report. World J Gastroenterol. (2024) 30:4149–55. doi: 10.3748/wjg.v30.i37.4149

PubMed Abstract | Crossref Full Text | Google Scholar

52. Walsh SR, Cook EJ, Goulder F, Justin TA, and Keeling NJ. Neutrophil-lymphocyte ratio as a prognostic factor in colorectal cancer. J Surg Oncol. (2005) 91:181–4. doi: 10.1002/jso.20329

PubMed Abstract | Crossref Full Text | Google Scholar

53. Zhang M, Liu J, Zhang F, Liang Q, and Guo Z. Comprehensive characterization of neddylation related genes in cutaneous melanoma identified a novel prognostic signature for treatment outcomes, immune landscape. Discov Oncol. (2024) 15:722. doi: 10.1007/s12672-024-01627-4

PubMed Abstract | Crossref Full Text | Google Scholar

54. Kwon HC, Kim SH, Oh SY, Lee S, Lee JH, Choi HJ, et al. Clinical significance of preoperative neutrophil-lymphocyte versus platelet-lymphocyte ratio in patients with operable colorectal cancer. Biomarkers. (2012) 17:216–22. doi: 10.3109/1354750x.2012.656705

PubMed Abstract | Crossref Full Text | Google Scholar

55. Matysiak K, Hojdis A, and Szewczuk M. Survival modelling using machine learning and immune-nutritional profiles in advanced gastric cancer on home parenteral nutrition. Nutrients. (2025) 17:10. doi: 10.3390/nu17152414

PubMed Abstract | Crossref Full Text | Google Scholar

56. Huang B, Yang Y, Liu J, Zhang B, and Lin N. Ubiquitination regulation of mitochondrial homeostasis: a new sight for the treatment of gastrointestinal tumors. Front Immunol. (2025) 16:1533007. doi: 10.3389/fimmu.2025.1533007

PubMed Abstract | Crossref Full Text | Google Scholar