scholarly journals Identification of prognostic immune‐related gene signature associated with tumor microenvironment of colorectal cancer

2020 ◽  
Author(s):  
Yuanyuan Wang ◽  
Wei Li ◽  
Xiaojing Jin ◽  
Xia Jiang ◽  
Shang Guo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
Ppi Networks ◽  
Tumor Purity ◽  
Immune Related Genes ◽  
Immune Score

Abstract Background The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC). Methods Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database. Results Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C‐index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen‐IRGs signature. The AUC for 1‐, 3‐, and 5‐year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates. Conclusions In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

scholarly journals Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanyuan Wang ◽  
Wei Li ◽  
Xiaojing Jin ◽  
Xia Jiang ◽  
Shang Guo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
Ppi Networks ◽  
Tumor Purity ◽  
Immune Related Genes ◽  
Immune Score

Abstract Background The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC). Methods Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database. Results Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates. Conclusions In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

scholarly journals Tumor microenvironment characterization in cervical cancer identifies prognostic relevant gene signatures

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249374
Author(s):  
Linyu Peng ◽  
Gati Hayatullah ◽  
Haiyan Zhou ◽  
Shuzhen Chang ◽  
Liya Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Tumor Microenvironment ◽  
Expression Profiles ◽  
Prognostic Indicator ◽  
Hpv Infection ◽  
Functional Enrichment ◽  
Rank Test ◽  
Sequencing Data ◽  
Ppi Networks ◽  
Significant Difference

Objective The aim of this study is to systematically analyze the transcriptional sequencing data of cervical cancer (CC) to find an Tumor microenvironment (TME) prognostic marker to predict the survival of CC patients. Methods The expression profiles and clinical follow-up information of CC were downloaded from the TCGA and GEO. The RNA-seq data of TCGA-CESC samples were used for CIBERSORT analysis to evaluate the penetration pattern of TME in 285 patients, and construct TMEscore. Other data sets were used to validate and evaluate TMEscore model. Further, survival analysis of TMEscore related DEGs was done to select prognosis genes. Functional enrichment and PPI networks analysis were performed on prognosis genes. Results The TMEscore model has relatively good results in TCGA-CESC (HR = 2.47,95% CI = 1.49–4.11), TCGA-CESC HPV infection samples (HR = 2.13,95% CI = 1–4.51), GSE52903 (HR = 2.65, 95% CI = 1.06–6.6), GSE44001 (HR = 2.1, 95% CI = 0.99–4.43). Patients with high/low TMEscore have significant difference in prognosis (log-rank test, P = 0.00025), and the main difference between high TMEscore subtypes and low TMEscore subtypes is immune function-related pathways. Moreover, Kaplan-Meier survival curves found out a list of identified prognosis genes (n = 86) which interestingly show significant enrichment in immune-related functions. Finally, PPI network analysis shows that highly related nodes such as CD3D, CD3E, CD8A, CD27 in the module may become new targets of CC immunotherapy. Conclusions TMEscore may become a new prognostic indicator predicting the survival of CC patients. The prognostic genes (n = 86) may help provide new strategies for tumor immunotherapy.

scholarly journals Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wen Tan ◽  
Maomao Liu ◽  
Liangshan Wang ◽  
Yang Guo ◽  
Changsheng Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Overall Survival ◽  
Tumor Microenvironment ◽  
T Cell Activation ◽  
Good Prognosis ◽  
Functional Enrichment ◽  
Immune Related Genes ◽  
Immune Score ◽  
Go Terms

Abstract Background Breast cancer is one of the most frequently diagnosed cancers among women worldwide. Alterations in the tumor microenvironment (TME) have been increasingly recognized as key in the development and progression of breast cancer in recent years. To deeply comprehend the gene expression profiling of the TME and identify immunological targets, as well as determine the relationship between gene expression and different prognoses is highly critical. Methods The stromal/immune scores of breast cancer patients from The Cancer Genome Atlas (TCGA) were employed to comprehensively evaluate the TME. Then, TME characteristics were assessed, overlapping genes of the top 3 Gene Ontology (GO) terms and upregulated differentially expressed genes (DEGs) were analyzed. Finally, through combined analyses of overall survival, time-dependent receiver operating characteristic (ROC), and protein-protein interaction (PPI) network, novel immune related genes with good prognosis were screened and validated in both TCGA and GEO database. Results Although the TME did not correlate with the stages of breast cancer, it was closely associated with the subtypes of breast cancer and gene mutations (CDH1, TP53 and PTEN), and had immunological characteristics. Based on GO functional enrichment analysis, the upregulated genes from the high vs low immune score groups were mainly involved in T cell activation, the external side of the plasma membrane, and receptor ligand activity. The top GO terms of the upregulated DEGs from the high vs low immune score groups exhibited better prognosis in breast cancer; 15 of them were related to good prognosis in breast cancer, especially CD226 and KLRC4-KLRK1. Conclusions High CD226 and KLRC4-KLRK1 expression levels were identified and validated to correlate with better overall survival in specific stages or subtypes of breast cancer. CD226, KLRC4-KLRK1 and other new targets seem to be promising avenues for promoting antitumor targeted immunotherapy in breast cancer.

scholarly journals Identification of Tumor Microenvironment-related Prognostic Genes in Colorectal Cancer based on Bioinformatic Methods

2021 ◽  
Author(s):  
Yi Liu ◽  
Long Cheng ◽  
Chao Li ◽  
Chen Zhang ◽  
Wang Lei ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Networks ◽  
Chemokine Signaling ◽  
Chemokine Signaling Pathway

Abstract Colorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to malignant progression of CRC (stage, p=0.014; metastasis, p=0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein-protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine-cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in an independent cohort (GSE17386). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

scholarly journals Identification of the Immune-Related Genes in Tumor Microenvironment That Associated With the Recurrence of Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Liu Chengcheng ◽  
Qi Wenwen ◽  
Gong Ningyue ◽  
Zhu Fangyuan ◽  
Xu Runtong ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Head And Neck ◽  
Squamous Cell ◽  
Immune Cells ◽  
Functional Enrichment ◽  
Prognostic Scores ◽  
Expression Data ◽  
Immune Infiltration ◽  
Ppi Networks ◽  
Immune Related Genes

Head and neck squamous cell carcinomas (HNSCC) are still one of the most common malignant tumors in China, with a high metastasis rate and poor prognosis. The tumor immune microenvironment can affect the occurrence, development and prognosis of tumors, but the underlying mechanism is still unclear. In this study, we tried to describe the correlation between the recurrence of HNSCC and the tumor microenvironment (TME). The expression data [estimate the level of tumor stromal and immune infiltration, expression data (ESTIMATE)] algorithm was used to identify and estimate highly correlated stromal cells, immune cells, and prognostic scores in 116 samples of head and neck cancer patients from The Cancer Genome Atlas (TCGA) dataset. The functional enrichment analysis and protein-protein interaction (PPI) networks of differential expressed genes (DEGs) were constructed. Subsequently, the abundance of various infiltrating immune cells was estimated with the tumor immune estimation resource (TIMER) and the infiltration pattern of immune cells were explored in HNSCC. A total of 407 immune-related genes were identified to involve in the TME. We found that CCR5, CD3E, CD4, and HLA -DRB1 were the most obvious DEGs and the dendritic cells (DCs) showed the highest abundance in the TME of HNSCC. In addition, the unsupervised cluster analysis determined 10 clusters of immune infiltration patterns, and indicated that immune infiltrated CD4 + T and B cells may be related to the prognosis of HNSCC. In conclusion, our research determined the list of immune genes and immune infiltrating cells related to the prognosis of HNSCC, and provided a perspective for HNSCC evolution, anti-tumor drugs selection, and drug resistance research.

scholarly journals Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11219
Author(s):  
Yandong Miao ◽  
Hongling Zhang ◽  
Bin Su ◽  
Jiangtao Wang ◽  
Wuxia Quan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

scholarly journals Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi Liu ◽  
Long Cheng ◽  
Chao Li ◽  
Chen Zhang ◽  
Lei Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Networks ◽  
Chemokine Signaling ◽  
Chemokine Signaling Pathway

AbstractColorectal cancer (CRC) ranks fourth among the deadliest cancers globally, and the progression is highly affected by the tumor microenvironment (TME). This study explores the relationship between TME and colorectal cancer prognosis and identifies prognostic genes related to the CRC microenvironment. We collected the gene expression data from The Cancer Genome Atlas (TCGA) and calculated the scores of stromal/immune cells and their relations to clinical outcomes in colorectal cancer by the ESTIMATE algorithm. Lower immune scores were significantly related to the malignant progression of CRC (metastasis, p = 0.001). We screened 292 differentially expressed genes (DEGs) by dividing CRC cases into high and low stromal/immune score groups. Functional enrichment analyses and protein–protein interaction (PPI) networks illustrated that these DEGs were closely involved in immune response, cytokine–cytokine receptor interaction, and chemokine signaling pathway. Six DEGs (FABP4, MEOX2, MMP12, ERMN, TNFAIP6, and CHST11) with prognostic value were identified by survival analysis and validated in two independent cohorts (GSE17538 and GSE161158). The six DEGs were significantly related to immune cell infiltration levels based on the Tumor Immune Estimation Resource (TIMER). The results might contribute to discovering new diagnostic and prognostic biomarkers and new treatment targets for colorectal cancer.

scholarly journals Prognostic Signatures Based on Thirteen Immune-Related Genes in Colorectal Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Xiao-Bo Ma ◽  
Yuan-Yuan Xu ◽  
Meng-Xuan Zhu ◽  
Lu Wang
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Immune Related Genes

BackgroundThe immunosuppressive microenvironment is closely related to tumorigenesis and cancer development, including colorectal cancer (CRC). The aim of the current study was to identify new immune biomarkers for the diagnosis and treatment of CRC.Materials and MethodsCRC data were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Sequences of immune-related genes (IRGs) were obtained from the ImmPort and InnateDB databases. Gene set enrichment analysis (GSEA) and transcription factor regulation analysis were used to explore potential mechanisms. An immune-related classifier for CRC prognosis was conducted using weighted gene co-expression network analysis (WGCNA), Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis. ESTIMATE and CIBERSORT algorithms were used to explore the tumor microenvironment and immune infiltration in the high-risk CRC group and the low-risk CRC group.ResultsBy analyzing the IRGs that were significantly associated with CRC in the module, a set of 13 genes (CXCL1, F2RL1, LTB4R, GPR44, ANGPTL5, BMP5, RETNLB, MC1R, PPARGC1A, PRKDC, CEBPB, SYP, and GAB1) related to the prognosis of CRC were identified. An IRG-based prognostic signature that can be used as an independent potentially prognostic indicator was generated. The ROC curve analysis showed acceptable discrimination with AUCs of 0.68, 0.68, and 0.74 at 1-, 3-, and 5- year follow-up respectively. The predictive performance was validated in the train set. The potential mechanisms and functions of prognostic IRGs were analyzed, i.e., NOD-like receptor signaling, and transforming growth factor beta (TGFβ) signaling. Besides, the stromal score and immune score were significantly different in high-risk group and low-risk group (p=4.6982e-07, p=0.0107). Besides, the proportions of resting memory CD4+ T cells was significantly higher in the high-risk groups.ConclusionsThe IRG-based classifier exhibited strong predictive capacity with regard to CRC. The survival difference between the high-risk and low-risk groups was associated with tumor microenvironment and immune infiltration of CRC. Innovative biomarkers for the prediction of CRC prognosis and response to immunological therapy were identified in the present study.

scholarly journals Identification and validation of a miRNA-related expression signature for tumor mutational burden in colorectal cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lijun Xu ◽  
Qing Zheng
Keyword(s):  
Colorectal Cancer ◽  
Immune Response ◽  
Validation Cohort ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Immune Checkpoints ◽  
Positive Correlation ◽  
Expression Signature ◽  
Mutational Burden ◽  
Tumor Mutational Burden

Abstract Background Tumor mutational burden (TMB) is a promising predictor, which could stratify colorectal cancer (CRC) patients based on the response to immune checkpoint inhibitors (ICIs). MicroRNAs (miRNAs) act as the key regulators of anti-cancer immune response. However, the relationship between TMB and miRNA expression profiles is not elucidated in CRC. Methods Differentially expressed miRNAs (DE miRNAs) between the TMBhigh group and the TMBlow group were identified for the CRC cohort of the TCGA database. In the training cohort, a miRNA-related expression signature for predicting TMB level was developed by the least absolute shrinkage and selection operator (LASSO) method and tested with reference to its discrimination, calibration, and decision curve analysis (DCA) in the validation cohort. Functional enrichment analysis of these TMB-related miRNAs was performed. The correlation between this miRNA-related expression signature and three immune checkpoints was analyzed. Results Twenty-one out of 43 DE miRNAs were identified as TMB-related miRNAs, which were used to develop a miRNA-related expression signature. This TMB-related miRNA signature demonstrated great discrimination (AUCtest set = 0.970), satisfactory calibration (P > 0.05), and clinical utility in the validation cohort. Functional enrichment results revealed that these TMB-related miRNAs were mainly involved in biological processes associated with immune response and signaling pathways related with cancer. This miRNA-related expression signature showed a median positive correlation with PD-L1 (R = 0.47, P < 0.05) and CTLA4 (R = 0.39, P < 0.05) and a low positive correlation with PD-1 (R = 0.16, P < 0.05). Conclusion This study presents a miRNA-related expression signature which could stratify CRC patients with different TMB levels.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

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