Computational Approaches for Biomarker Detection and Precision Therapeutics in Cancers

Cover image for research topic "Computational Approaches for Biomarker Detection and Precision Therapeutics in Cancers"
97.3K
views
141
authors
21
articles
Editors
3
Impact
Loading...
Original Research
03 December 2021
Prognostic Implications and Immune Infiltration Analysis of ALDOA in Lung Adenocarcinoma
Guojun Lu
1 more and 
Yu Zhang
Expression of ALDOA from Oncomine (A) ALDOA expression in different types of cancers. Red means up-regulated and blue means down-regulated (B-H) BOX plot showing the mRNA expression levels of ALDOA in Selamat lung, Landi lung, Hou lung, Okayama lung, Stearman lung, Su lung, and Garber lung datasets, respectively.

Background: aldolase A (ALDOA) has been reported to be involved in kinds of cancers. However, the role of ALDOA in lung adenocarcinoma has not been fully elucidated. In this study, we explored the prognostic value and correlation with immune infiltration of ALDOA in lung adenocarcinoma.

Methods: The expression of ALDOA was analyzed with the Oncomine database, the Cancer Genome Atlas (TCGA), and the Human Protein Atlas (HPA). Mann-Whitney U test was performed to examine the relationship between clinicopathological characteristics and ALDOA expression. The receiver operating characteristic (ROC) curve and Kaplan-Meier method were conducted to describe the diagnostic and prognostic importance of ALDOA. The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct PPI networks and identify hub genes. Functional annotations and immune infiltration were conducted.

Results: The mRNA and protein expression of ALDOA were higher in lung adenocarcinoma than those in normal tissues. The overexpression of ALDOA was significantly correlated with the high T stage, N stage, M stage, and TNM stage. Kaplan-Meier showed that high expression of ALDOA was correlated with short overall survival (38.9 vs 72.5 months, p < 0.001). Multivariate analysis revealed that ALDOA (HR 1.435, 95%CI, 1.013–2.032, p = 0.042) was an independent poor prognostic factor for overall survival. Functional enrichment analysis showed that positively co-expressed genes of ALDOA were involved in the biological progress of mitochondrial translation, mitochondrial translational elongation, and negative regulation of cell cycle progression. KEGG pathway analysis showed enrichment function in carbon metabolism, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis. The “SCNA” module analysis indicated that the copy number alterations of ALDOA were correlated with three immune cell infiltration levels, including B cells, CD8+ T cells, and CD4+ T cells. The “Gene” module analysis indicated that ALDOA gene expression was negatively correlated with infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, and macrophages.

Conclusion: Our study suggested that upregulated ALDOA was significantly correlated with tumor progression, poor survival, and immune infiltrations in lung adenocarcinoma. These results suggest that ALDOA is a potential prognostic biomarker and therapeutic target in lung adenocarcinoma.

6,012 views
16 citations
5,838 views
30 citations
12,483 views
35 citations
7,306 views
14 citations
4,554 views
25 citations
Validation and training cohort accuracies of the 10-gene signature. (A) ROC, (B) PRC. The Lasso model was first estimated based on the full training dataset using the 10 genes as features, and then the estimated model was applied to the validation cohort. The training cohort model accuracy is overoptimistic as no cross validation was used and the training and test data are the same; see Figure 5 for cross-validated training cohort model accuracy. For comparison, we randomly selected 10 genes 100 times, estimated 100 Lasso models in the training cohort, and then tested these random gene classifiers on the validation cohort. The 10 random gene curve shows the average performance of the random classifiers, and the error bars show the standard error of the mean (SEM). In panel (B), the dashed horizontal line corresponds to a theoretical random classifier with AU-PRC = 0.473.
7,293 views
17 citations
Original Research
13 January 2021
A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme
Ming Gao
10 more and 
Shiguang Zhao
im-lncRNAs enable evaluate GBM immunophenotypes. (A) ROC curves for prediction of GBM tumor immunophenotypes based on the im-lncScore (orange) or immune expression signatures (other colors) in the TCGA GBM cohort. (B) Density plot showing the distribution of im-lncScore in IH and IL samples. (C) Heatmap showing the im-lncScore (the first line), ESs of immune expression signatures (second to sixth lines), and infiltration levels of tumor immune cells (last six lines) in GSE121810 cohort.

Glioblastoma multiforme (GBM) is the most aggressive primary tumor of the central nervous system. As biomedicine advances, the researcher has found the development of GBM is closely related to immunity. In this study, we evaluated the GBM tumor immunoreactivity and defined the Immune-High (IH) and Immune-Low (IL) immunophenotypes using transcriptome data from 144 tumors profiled by The Cancer Genome Atlas (TCGA) project based on the single-sample gene set enrichment analysis (ssGSEA) of five immune expression signatures (IFN-γ response, macrophages, lymphocyte infiltration, TGF-β response, and wound healing). Next, we identified six immunophenotype-related long non-coding RNA biomarkers (im-lncRNAs, USP30-AS1, HCP5, PSMB8-AS1, AL133264.2, LINC01684, and LINC01506) by employing a machine learning computational framework combining minimum redundancy maximum relevance algorithm (mRMR) and random forest model. Moreover, the expression level of identified im-lncRNAs was converted into an im-lncScore using the normalized principal component analysis. The im-lncScore showed a promising performance for distinguishing the GBM immunophenotypes with an area under the curve (AUC) of 0.928. Furthermore, the im-lncRNAs were also closely associated with the levels of tumor immune cell infiltration in GBM. In summary, the im-lncRNA signature had important clinical implications for tumor immunophenotyping and guiding immunotherapy in glioblastoma patients in future.

4,602 views
23 citations
Article Cover Image
7,633 views
39 citations
Fetching...
Open for submission
Frontiers Logo

Frontiers in Genetics

Advances in circRNA Research: Disease Associations and Diagnostic Innovations
Edited by Mengting Niu, Quan Zou, Xiaoqing Ru
Deadline
13 June 2025
Submit a paper
Recommended Research Topics
Frontiers Logo

Frontiers in Genetics

User-Friendly Tools Applied to Genetics or Systems Biology
Edited by Helder Nakaya, Juilee Thakar, Vinicius Maracaja-Coutinho
139.2K
views
92
authors
16
articles
Frontiers Logo

Frontiers in Genetics

Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data
Edited by Chunquan Li, Dechao Bu, DECHEN Lin LIN, Sun Liang, Masaharu Hazawa
76.5K
views
152
authors
19
articles
Frontiers Logo

Frontiers in Genetics

Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research, Volume II
Edited by Lixin Cheng, Hongwei Wang, Shibiao Wan
175.1K
views
330
authors
53
articles
Frontiers Logo

Frontiers in Genetics

Papers of Sixth China Computer Federation Bioinformatics Conference
Edited by Xuefeng Cui, Fa Zhang, Wang Guohua, Chunhou Zheng, Hongmin Cai, Qinghua Jiang, Kang Ning
10.4K
views
19
authors
4
articles
Frontiers Logo

Frontiers in Genetics

Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data - Volume II
Edited by Chunquan Li, Dechao Bu, DECHEN Lin LIN, Masaharu Hazawa, Sun Liang
26K
views
49
authors
5
articles