Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Background Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD). Methods “GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs. Results A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05. Conclusion In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.


INTRODUCTION
Lung cancer is the third leading cause of death in the world (Siegel et al., 2020;Kara et al., 2021), which is classified into small cell lung cancer (SCLC), lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) . Non-small cell lung cancer (NSCLC) accounted for 85% of lung cancer and the 5-year survival rate of the patients is less than 20% (Bade and Dela Cruz, 2020). LUAD is the most important type of NSCLC Hou and Yao, 2021;. In recent years, although the treatment of lung cancer is diversified, such as chemotherapy, immunotherapy, targeted therapy, the prognosis of patients with advanced lung cancer is still poor (Noreldeen et al., 2020;Wang et al., 2020). More than 60% of LUAD patients missed the best targeted treatment time due to the difficulty of diagnosis, which can reduce the survival rate (Kris et al., 2014;Chen et al., 2016). In recent years, the resistance of the majority of LUAD patients to multiple antitumor drugs has led to a decrease in the cure rate of LUAD. Therefore, it is very necessary to explore the early gene markers and treatment targets for the prognosis of patients .
The immune system is currently recognized as a determinant of cancer (Janssen et al., 2017). With the development of modern technology, new immunotherapy drugs have made remarkable achievements and improved the prognosis of patients (Yang et al., 2021). Combined with various clinical studies, immunotherapy may replace the traditional treatment (Zhai et al., 2021). Understanding TME and recognizing genes related to TME can provide new ideas for immunotherapy. In the TME, many cancer cells and immune cells mediate signaling pathways (Marwitz et al., 2021), involving in tumor progression and drug resistance (Santaniello et al., 2019;Ling et al., 2020). In view of the low treatment rate of LUAD patients with high pathogenicity, it is imperative to find new gene markers (Zhou C. S. et al., 2021).
As for a non-coding RNA, lncRNA was limited to code protein . In recent years, there has been increasing evidence that the expression of lncRNA involves in cancer progression, such as cancer metastasis (Li et al., 2016;Kim et al., 2018), drug resistance (Han et al., 2017), and apoptosis (Zhao et al., 2017). ceRNA network was composed of mRNAs, lncRNAs, and miRNAs . lncRNA MALAT1 modulates cell migration, proliferation by sponging miRNA146a to regulate CXCR4 in acute myeloid leukemia (Sheng X. F. et al., 2021). ceRNA network represents a novel layer of gene regulation that controls both physiological and pathological processes .
Our study aims to explore immune-related genes and construct forecast model for clinical guidance and prognosis analysis in LUAD using TCGA and GEO common databases. The relationship between target genes and immune cells is studied through various authoritative databases. We built the lncRNAs-miRNAs-CDK2 ceRNA network innovatively, found potential prognostic markers with LUAD. Our study may provide new molecular and therapeutic strategies for the treatment and prognosis of LUAD patients.

Gene Expression mRNA Seq, Clinical Data Collection
We downloaded the gene expression, clinical data, Pan-Cancer Atlas Hub from GEO database 1 , UCSC Xena 2 , GDSC 3 .

Validation of CDK2 in Different Databases
UALCAN 7  is a comprehensive, userfriendly, and interactive web resource for analyzing cancer OMICS data. Combing with clinical data, we explored the correlation between CDK2 expression and clinical indicators, such as age, grade, sex, smoking habits, stage, TP-53 mutation, weight. Heat maps were showed the positive and negative related genes with CDK2. We analyzed the overall survival (OS) and Relapse-Free Survival (RFS) about CDK2 for LUAD patients by PrognoScan 8 .

Further Study About CDK2 Expression in 33-Cancers: Pan-Cancer Analysis
Compared with normal lung tissues, CDK2 expression was highly expressed (P < 0.05) in LUAD by Oncomine 9 (Dai et al., 2021) Batch Related Genes, ROC Analysis, Functional Enrichment, GSEA Analysis in LUAD To research the major molecule CDK2, we showed 50 positive and negative genes in LUAD by heat maps (Person test method). The 1, 3, 5, and 8 years' ROC curves were constructed by "ROC" R package. The abscissa of the prediction model was False Positive (FP) and the ordinate was True Positive (TP). Next, we screened out the top 300 genes with the most significant positive correlation with CDK2 for enrichment analysis. Bar and bubble charts showed the classical functions of 300 genes by "clusterprofiler" R package. We downloaded the GSEA4.1.0 11 (Cao et al., 2021a) to investigate Kyoto Encyclopedia of Genes and Genomes (KEGG) of CDK2-related 300 genes. The top of 50 terms were sorted by P-value in circle graph.

Immune Infiltration of CDK2 in LUAD
CIBERSORT 12  is an analysis tool to provide an estimation of the abundances of member cell types in a mixed cell population using gene expression data. We selected immune cells associated with CDK2 (Pearson correlation coefficient r > 0.15). Four CDK2-related immune cells were shown in circle graph.

Correlation Analysis of Immune Cells and CDK2 Expression
R package was used to analyze the relation of CDK2 and immune cell infiltration score (P ≤ 0.05). A total of 33-cancers, according to the median of CDK2 gene expression, the samples were divided into high expression groups and low expression groups. Then, we discussed the different expression of immune cells (P < 0.05). The above results were shown by scatter plots and box plots.
Exploring CDK2-Related Immune Genes and GSEA Analysis TISIDB 13  is a web portal for tumor and immune system interaction, which integrates multiple heterogeneous data types. First, we found CDK2-related Immunomodulators with P-value < 0.05. Next, cBioPortal 14 (Lu et al., 2021) was used to explore the genes related to Immunomodulators. DAVID and STRING 15  were used to analyze the Gene Ontology (GO), protein interaction of 49 genes. GSEA 4.1.0 further analyzed the NOM p-val and FDR q-val of these immune related genes.

Combining Clinical Data and Constructing Nomogram Model
Through LUAD clinical data, "survival, " "survminer" R packages were used to construct the COX regression model of age, gender, and stage. Forest plot  was shown related Hazard ratio (HR), Log-Rank, Concordance index of LUAD. "rms" R package was used to structure the Nomogram model (Liu et al., 1976) of age, gender, stage, T, N, M.

Univariate, Multivariate Cox Regression and Time ROC Analysis
Using "Survival" R package, we explored the univariate Cox analysis of 49 genes (P < 0.05). Different P-value and HR of statistically significant genes were shown in forest map. According to the results of univariate regression analysis, the significant genes were further analyzed by multivariate analysis. According to the risk score of genes selected by multiple factors, the LUAD samples were divided into lower-risk and higher-risk groups. The risk score was as follows: Risk score = Expgene1 × Coefgene1+Expgene2 × Coefgene2+ Expgene3 × Coefgene3+ Expgene4 × Coefgene4. "survival, " "survminer, " "survival ROC" R packages were performed survival probability curve and ROC curve.
Combining the patient's high and low risk situation and the state of life, death, we used "pheatmap" R package to show the heat map.

The CDK2 Expression Was Further Verified by Histochemistry
We use Human Protein Atlas (HPA) database to verify CDK2 expression in LUAD tissues and normal lung tissues.

Identification of CDK2 Related ceRNA Network
We explored CDK2 related miRNAs from Targetscan 16 (Barnett-Itzhaki et al., 2021), miRWalk 17 (Sheng L. P. et al., 2021), mirDB 18 (Ren et al., 2021), Starbase 19 (Li et al., 2014). All miRNAs from four databases were analyzed by Venn map 20 . Kaplan-Meier Plotter software was used to explore the prognostic value of miRNAs with LUAD. The lncRNAs that regulate common FIGURE 1 | The overall research ideas of this paper were shown as follows.
Frontiers in Cell and Developmental Biology | www.frontiersin.org miRNAs were screened by Starbase database (Li et al., 2014). The Cytoscape  was used to build ceRNA network and Cytohubba selected the key node genes according to the degree in the network.

The Expression of CDK2 Was Verified by Polymerase Chain Reaction (PCR)
We did the basic experimental verification of PCR about CDK2 in LUAD cell lines (A549, H1299, H1975) and normal bronchial epithelial cell line (BEAS-2B). GraphPad Prism7 21 software was used to count the differences between cancer cell lines and normal cell line. 21 https://www.graphpad.com/ Correlation Between CDK2 and IC50 of Anti-tumor Drugs We downloaded the response data of 192 anti-tumor drugs in more than 1000 cancer cell lines. "Ggplot2" R package was used to explore the correlation between the CDK2 expression and IC50 of 192 anti-tumor drugs by box diagrams and point diagrams.

Differentially Expressed mRNAs for GSE68465
A total of 250 genes were chosen by GEO2R according to the P-value and LogFC. 161 mRNAs were highly expressed and 78 Frontiers in Cell and Developmental Biology | www.frontiersin.org mRNAs were expressed at lower levels in 19 adjacent non-LUAD tissues and 433 LUAD tissues. The study design of this project was shown in Figure 1. The volcano map, scatter plots, peak plot of GSE68465 samples were displayed in Figure 2. A total of 15 highly expressed genes and 15 low expressing genes were shown in Table 1.

GO, KEGG Analysis of 250 DEmRNAs
Through Metascape software, we identified a number of pathways significantly enriched, including activation of immune response, regulation of cell adhesion, T cell activation involved in immune response, regulation of cell adhesion, PID-HNF3B PATHWAY ( Figure 3A). The interactions between different pathways were shown in Figure 3B. Figures 3C,D drew the P-value of different pathways. According to the relationship of DE mRNAs, several protein analyses were performed in the Figure 3E. CDK2, MYB, GATA3 related to each other in some tumor pathway and the results were listed in circle graph ( Figure 3F). DAVID was used to analyze the KEGG results of genes and we found 9 mRNAs participated in the classic P13-AKT signaling pathway, such as MYB, COL3A1, COL4A1, CSF1R, CDK2, ITGB7, LAMA2, TLR2, and VWF. Combining with the Overall Survival (OS) of LUAD patients, we chose CDK2 as targeted molecule (P < 0.05). KEGG software further was used to identify the upstream molecules of CDK2 in P13-AKT signaling pathway. Finally, the CDKN1A might regulate downstream molecule CDK2 to influence cell cycle progression in LUAD. Through the scatter plot, there was a positive correlation between CDK2 and CDKN1A using GEPIA 22 (P = 0, R = 0.59) ( Figure 3G).

Further Study of CDK2 and Its Prognostic Values in LUAD
Through the Ualcan database, we studied the expression of CDK2 in LUAD tissues and normal tissues. CDK2 expression was high in 515 LUAD tissues in comparison with 59 normal tissues (P = 1.624E-12) ( Figure

The Expression of CDK2 in Pan-Cancer Analysis
Through the Oncomine database, CDK2 expression was higher in 15 cancer tissues in comparison with normal tissues ( Figure 5A). There was a great difference between cancer tissue and normal tissue. The statistical significance between normal and tumor tissues was further found in the TIMER database (the more " * " symbol, the greater the difference) ( Figure 5B). Mata analysis of 15 published studies on LUAD showed that expression of CDK2 was higher in LUAD (Median Rank = 5384.0, P = 4.16E-6) ( Figure 5C). By t-test, box plot and peak plot of CDK2 in LUAD were shown in Figures 5D,E (t-Test = 3.249, Fold Change = 1.332, P = 0.003). Using the R package, we ranked CDK2 by its expression in 33 cancers ( Figure 5F). Through different R packages, the expression of CDK2 was relatively higher in 17 cancers (P < 0.05) (Figures 6A-Q). The different expression levels of CDK2 had statistical significance on the stage of patients (P < 0.05) (Figures 6R-Y). There was significant difference in the expression of CDK2 between stage I and stage III, I and IV, II, and IV LUAD ( * / * * ). For different types of cancer, we conducted paired differential expression of CDK2. Simultaneously, we found that there was statistical significance in 14 cancer types (P < 0.05) (Figures 7A-N). In 10 cancer types,   high CDK2 expression was associated with poor prognosis of the patients (P < 0.05) (Figures 7O-X).

Functional Enrichment Analysis of CDK2
We compared CDK2 high expression groups with low expression groups in LUAD, and Figures 8A,B showed the top differentially expressed genes in two groups of cancer specimens. We took FP as the abscissa and TP as the ordinate and showed the AUC curve of 1, 3, 5, and 8 years to forecast the survival of patients. The areas under the curve were 0.465, 0.524, 0.612, and 0.575 ( Figure 8C). "Clusterprofiler" R package was used to show the function analysis. And CDK2 was related to the DNA replication, regulation of cell cycle, cell cycle checkpoint, P53 signaling pathway. The bubble and bar charts were shown in Figures 8D-G. Wave charts about GO, KEGG, Reactome showed that CDK2 involved in classic tumor signaling pathways, such as regulation of TP53 activity, PTEN regulation, apoptosis, P13K-AKT signaling pathway (Figures 8H-J). The circle graph was shown in Figure 8K. For different immune cells, CDK2 was positively correlated with CD4 T cells (Cor = 0.1679, P = 0.0003), macrophage M1 (Cor = 0.2437, P = 1.40E-07) and negatively correlated with Mast cells (Cor = -0.1545, P = 0.0009) by CIBERSOPT algorithm (Figure 8L).

Characteristics of CDK2 Immune Cells Infiltration
In LUAD, we further investigated different expression of CDK2 in different immune cell types. We found that 14 immune cells were closely related to CDK2 expression, such as T cells Follicular  (Figures 9A-N). According to the median value of CDK2 expression, patients with LUAD were divided into the high expression groups and low expression groups. In different groups, the expression of 10 immune cells had systematic differences (P < 0.05) (Figures 9O-X).

Construction of Immune Related Forecasting Model
Through the TISIDB database, we found CDK2-related immunomodulators and the heat maps of immunopotentiators and immunosuppressants were shown in Figures 10A,B. By sorting the P-value (P < 0.05), we identified 13 immunosuppressants (ADORA2A, BTLA, CD274, CSF1R,  IL10, KDR, LAG3, LGALS9, PDCD1, PDCD1LG2, TGFB1,  TIGIT, VTCN1) and 21 immunopotentiators (CD27, CD276,  CD28, CD40LG, CD48, CD70, CD80, CXCL12, CXCR4,  ENTPD1, HHLA2, ICOSLG, IL2RA, IL6, IL6R, KLRC1, MICB,  PVR, TMEM173, TNFRSF13B, ULBP1) with a high correlation of CDK2. In the cBioProtal, we explored forty-nine genes associated with 34 immunomodulators. Clinical data and gene expression data were downloaded from TCGA database. Forty-nine genes were mixed from the TCGA using "perl" and "R" package ( Figure 10C). Through the Metascape database, the most abundant pathway of 49 genes was the immune system process, other function terms were biological adhesion, biological regulation, cell proliferation ( Figure 10D). The BP, CC, MF was showed specifically in Table 2. The protein network interaction (PPI) of these 49 immune related genes were shown in the Figure 10E (P < 1.0e-16). GSEA analyzed the function, ES, NES, NOM p-val, FDR q-val of 49 genes. The statistically significant items were PUJANA_ATM_PCC_NETWORK and INTRACELLULAR_SIGNAL_TRANSDUCTION (P < 0.01) ( Figure 10F and Table 3). Combing with clinical data and expression matrix, univariate and multivariate regression analyses were performed on age, gender and tumor stage (Total P = 1.399e-08) ( Table 4). The forest map showed HR and concordance index (0.7) of LUAD. As predicted, the tumor stage was an independent risk factor for the prognosis of patients with LUAD ( Figure 10G). Nomogram model combined several clinical factors, such as age, gender, stage, T, N, M to intuitively analyze the prognosis of LUAD ( Figure 10H). Each patient can be assessed by nomogram model based on baseline clinical data. Univariate regression analysis showed that 36 genes were associated with the prognosis of LUAD (P < 0.05). We showed the HR, HR95L, HR95H, P-value of these 36 significant genes. CDK2 was an independent prognostic gene in LUAD (HR > 1) ( Table 5 and Figure 10I). Multivariate regression analysis was used to analyze the 36 genes and only four genes (SIT1, SNAI3, ASB2, and CDK2) were included in the prediction model ( Table 6). Through the GEPIA database, SIT1 (P = 0.04), SNAI3 (P = 0.00035), ASB2 (P = 0.00027) were lower expressed in    (Figures 10J-L). To verify the prognostic model, the risk curve showed that the high-risk group was more lethal to lung cancer patients (P = 6.223E-04) ( Figure 10M). Based on different risk scores, patients were divided into high and low risk groups using "R" package ( Figures 10N,O). The ROC curve showed the different AUC of 4 interesting genes and CDK2 had more predictive value in the prognostic model (AUC = 0.579) (Figure 10P).

The PCR Results of CDK2 Expression
Compared with the expression of CDK2 in BEAS-2B cell line, the expression of CDK2 in A549 cell line was increased, but there was no statistical difference (P = 0.0808). There were significant differences between H1299 cell line (P = 0.0006) and H1975 cell line (P = 0.0030) ( Figure 11M).  Table 7 and Figures 12A-T).

DISCUSSION
In recent years, the role of immune invasion in the progression of LUAD has been improved. Immunotherapy can resist tumor cells by activating the activity of immune molecules. It is necessary to find effective immune related-markers to predict prognosis and apply the individual therapy. And the study of TME is the key to overcome drug resistance . Our study first explored the prognostic molecule CDK2, which was differently expressed in LUAD tissues and adjacent non-LUAD tissues (P = 1.624E-12). High expression of CDK2 in LUAD has poor prognosis. In order to verify CDK2 in all kinds of cancers, we carried out a pan-cancer study. We further showed the expression, stage analysis, paired expression, survival condition in 33-cancer. The expression, stage and prognosis of CDK2 were obviously different in many cancers. According to published articles, a gene marker is no longer limited to one cancer, but is extended to various cancers, so the credibility has been improved than before. Based on CDK2, we searched for relevant immunomodulators and combined with the clinical expression data of LUAD to conduct univariate and multivariate regression models. Then the Nomogram forecast model of age, gender, stage, T, N, M provided the clinical significance and prognosis. The patient's condition will be assessed according to the total risk score. Nomogram has a great effect on the prognosis of patients with LUAD. Finally, multivariate regression analysis showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were important for this immune model and significant for the prognosis of patients (P < 0.05). CDK2 was a risk molecule with independent prognosis (HR = 1.291, P = 0.035). This provides potential markers for targeted therapy with LUAD.
Cell-dependent kinases (CDKs) are involved in proliferation, DNA damage repair (Lim and Kaldis, 2013) and treatment of various tumors Majumdar et al., 2021). A variety of mechanisms, including chaperone, positive phosphorylation and negative phosphorylation  regulate the activity of CDK family. Recently, CDK family molecules have been used as novel molecular markers for tumor-targeted therapy. As a star molecule, CDK2 (Kawakami et al., 2020) participates in classic pathways in various cancers, such as colorectal cancer (Somarelli et al., 2020), neuroblastoma (Poon et al., 2020), breast cancer (Hur et al., 2020), hepatocellular carcinoma (Hou et al., 2019), and prostate cancer (Washino et al., 2019). However, there are few studies on the mechanism of CDK2 in lung cancer. We studied the relationship between CDK2 and tumor immunity, providing a new idea for immunotherapy about LUAD.
In this study, we found that the expression of CDK2 was related to various immune cells including T cells (P = 0.05), Macrophages (P = 0), Eosinophils (P = 0.01), Mast cells (P = 0). Therefore, CDK2 can affect the tumorigenesis and proliferation by regulating the mechanism of immune inflammation. Combined with results of COX regression, target molecule CDK2 provides prognosis analysis and treatment strategy.
Cell cycle imbalance is common in different tumors . Cell-dependent kinase (CDK) is involved in many tumors related biological processes, such as cell cycle, immune checkpoint (Hume et al., 2020), RNA transcription (Al-Sanea et al., 2021), cell proliferation . Recently, there have been studies on CDK4 (Pandey et al., 2021) and CDK6 (Pandey et al., 2021), but the mechanism of CDK2 (El-Sattar et al., 2021) in cancer is relatively lacking. In our study, we deeply studied CDK2 and constructed the related immune prediction model. And CDK2 was associated in the P13K-AKT  signaling pathway in LUAD, inducing cell proliferation (Wu et al., 2020). Combing with clinical data, we constructed the forest map and Nomogram model (Cao et al., 2021b;Jang et al., 2021;Wang R. R. et al., 2021;Wang K. et al., 2021) of clinical characteristics. Through the features of patients, we can calculate the total score to evaluate the prognosis of patients. The prognosis of patients can be evaluated by a simple calculation method.
Compared with the current studies, the advantage is that we first established CDK2 related immune forecast model through the univariate, multivariate regression analysis and different "R" packages. This forecast was significant for LUAD patients. And the molecule CDK2 was related to various immune cells and may regulate mechanism in some way. The correlation between CDK2 and immune cells was listed by box diagram and point diagram. Compared with previous studies, they may research some markers in only signal cancer. But we explored detailed information of CDK2 in 33-cancer, a pancancer analysis was shown in our study. In various cancers, CDK2 has corresponding systematic significance with several clinical aspects (P < 0.05). CDK2 was associated with clinical stage, age, pathological type, TP-53 mutation, smoking with LUAD. According to the risk score, LUAD patients were divided into the high-risk group and low-risk group. There was a great difference between different risk curves (Yu et al., 2021). The survival time of the higher risk group was shorter than that of the lower risk group (P = 6.223E-04). This indicates that the immune related model is of great significance for the prognosis of patients with LUAD. All data were collected from reliable GEO database, TCGA, UCSC Xena and this increases the reliability of the data. In recent years, ceRNA network has certain significance in various cancers. The functions of many non-coding genes have been gradually explored (Yuan et al., 2014). Seven miRNAs (hsa-miR-302b-3p, hsa-miR-372-3p, hsa-miR-302a-3p, hsa-miR-373-3p, hsa-miR-520a-3p, hsa-miR-520d-3p, and hsa-miR-302d-3p), six lncRNAs (XIST, SNHG16, RP11-145M9.4, MAP3K14, MIR4720, and RP11-379K17.11) were considered as potential biomarkers in LUAD. Combined with the current literature, these non-coding genes have been studied in other cancers, but less in LUAD. This study provides potential therapeutic targets for LUAD and contributes to immunotherapy.
In recent years, nomogram model has been considered as a tool to predict tumor prognosis (Balachandran et al., 2015;Huang et al., 2016), such as colorectal cancer (Huang et al., 2016) and cervical cancer (Rose et al., 2015). Nomogram model meets our desire for biologically and clinically integrated models. It is not limited to one factor, but combines with various influencing factors to evaluate the prognosis of patients. We constructed nomogram model of age, gender, stage, T, N, M. According to the corresponding score of each influencing factor, the risk index of LUAD patients is estimated to evaluate the survival time.
According to our anti-tumor drug resistance research findings, the IC50 of four drugs (Axitinib, SB216763, KU-55933, BMS-536924) were positive with expression of CDK2. This indicates that cancer patients with high expression of CDK2 are prone to resistance to the above four drugs. Other 16 drugs were negative with the expression of CDK2. The patients with high expression of CDK2 had good response to the above 16 drugs and low resistance rate. Three drugs Cisplatin, Cytarabine, Nilotinib are considered as classic anti-cancer drugs. They are effective for patients with high expression of CDK2 cancer and it is not easy to cause drug resistance. Of course, the mechanism between CDK2 and drug resistance still needs to further be studied.
There are disadvantages in our study compared with the current articles. The target molecules lack of experimental verification and big data support. The regulatory mechanism of target genes is unclear.
In summary, immune-related forecast model was constructed by univariate regression and multivariate regression analysis to guide prognosis of LUAD. K-M curve verified the highrisk group had a poor prognosis (P < 0.01). Because of the correlation of CDK2 and various immune cells, CDK2 may be involved in tumor regulation of immune infiltration. CDK2 provides a new immunotherapy target for LUAD, which can improve the prognosis. The construction of ceRNA network provides a new way for exploring potential gene markers with LUAD. Drug resistance research can help patients choose a reasonable treatment plan, not blindly targeted therapy or immunosuppressive therapy.

CONCLUSION
In conclusion, we discovered a set of four genes, including expression of CDK2, has a significant prognostic value in LUAD. CDK2 expression is highly associated with immune responses in the cancer. We made some prediction to link CDK2 expression with drug responses and miRNA expression.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ supplementary material.

AUTHOR CONTRIBUTIONS
T-TL and RL conceived and designed the study. CH, J-PL, JY, and X-LJ collected the literature. T-TL drafted the manuscript. Y-QQ revised the manuscript. All the authors read and approved the final manuscript.