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pmid:33401247

Identification and validation of an immune-related gene signature predictive of overall survival in colon cancer.

Xuening Zhang · Hao Zhao · Xuezhong Shi · Xiaocan Jia · Yongli Yang

Aging · 2020 doi:10.18632/aging.202317

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Experiment

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Data

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Analysis

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pmid:33401247
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title
Identification and validation of an immune-related gene signature predictive of overall survival in colon cancer.
doi
10.18632/aging.202317
pmid
33401247
authors array · 5 items
[0]
Xuening Zhang
[1]
Hao Zhao
[2]
Xuezhong Shi
[3]
Xiaocan Jia
[4]
Yongli Yang
journal
Aging
year
2020
abstract
The heterogeneity and complexity of tumor-immune microenvironments lead to diverse immunotherapy effects among colon cancer patients. It is crucial to identify immune microenvironment-related biomarkers and construct prognostic risk models. In this study, the immune and stromal scores of 415 cases from TCGA were calculated using the ESTIMATE algorithm. AXIN2, CCL22, CLEC10A, CRIP2, RUNX3, and TRPM5 were screened and established a prognostic immune-related gene (IRG) signature using by univariate, LASSO, and multivariate Cox regression models. The predicted performance of IRG signature was external validated by GSE39582 (n=519). Stratified survival analysis showed IRG signature was an effective predictor of survival in patients with different clinical characteristics. The protein expression level of six genes was validated by immunohistochemistry analysis. Difference analysis indicated the mutation rate, immune cell of resting NK cells and regulatory T cells infiltration and four immune checkpoints of PD-1, PD-L1, LAG3 and VSIR expression levels in the high-risk group were significantly higher than those in the low-risk group. A nomogram incorporating the gene signatures and clinical factors was demonstrated had a good accuracy (1-, 3-, and 5-year AUC= 0.799, 0.791, 0.738). Our study identified a novel IRG signature, which may provide some references for the clinical precision immunotherapy of patients.
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Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized for further screening of prognostic genes to avoid overfitting the model.
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sections[14] "Construction, validation, and subgroup analysis of prognostic risk score model" paragraph 2
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Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized for further screening of prognostic genes to avoid overfitting the model.
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Fragments per kilobase million (FPKM) data on RNA-Seq and corresponding clinical characteristics of colon cancer were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, up to June 06, 2020) using the Genomic Data Commons (GDC) tool. The original data included 482 tumor tissues and 42 adjacent tissues. FPKM data was translated into transcripts per million (TPM) expression data. Based on the requirement of data integrality, patients that met the following criteria were excluded from the subsequent analysis: (1) repeated patient records; (2) missing follow-up time, T/N/M staging, or pathological stage information; (3) follow-up time was 0 days. Finally, our study identified 415 colon cancer samples as analytical datasets. All data were from publicly available databases, and there were no ethical issues involved.
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sections[12] "Data download and preparation" paragraph 2
quote
Fragments per kilobase million (FPKM) data on RNA-Seq and corresponding clinical characteristics of colon cancer were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, up to June 06, 2020) using the Genomic Data Commons (GDC) tool.
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sections[12] "Data download and preparation" paragraph 2
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The original data included 482 tumor tissues and 42 adjacent tissues.
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source:pmid:33401247
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sections[12] "Data download and preparation" paragraph 2
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FPKM data was translated into transcripts per million (TPM) expression data.
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sections[12] "Data download and preparation" paragraph 2
quote
Based on the requirement of data integrality, patients that met the following criteria were excluded from the subsequent analysis: (1) repeated patient records; (2) missing follow-up time, T/N/M staging, or pathological stage information; (3) follow-up time was 0 days.
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sections[12] "Data download and preparation" paragraph 2
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Finally, our study identified 415 colon cancer samples as analytical datasets.
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source:pmid:33401247
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sections[12] "Data download and preparation" paragraph 3
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All data were from publicly available databases, and there were no ethical issues involved.
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Fragments per kilobase million (FPKM) data on RNA-Seq and corresponding clinical characteristics of colon cancer were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, up to June 06, 2020) using the Genomic Data Commons (GDC) tool. The original data included 482 tumor tissues and 42 adjacent tissues. FPKM data was translated into transcripts per million (TPM) expression data. Based on the requirement of data integrality, patients that met the following criteria were excluded from the subsequent analysis: (1) repeated patient records; (2) missing follow-up time, T/N/M staging, or pathological stage information; (3) follow-up time was 0 days. Finally, our study identified 415 colon cancer samples as analytical datasets. All data were from publicly available databases, and there were no ethical issues involved.
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Fragments per kilobase million (FPKM) data on RNA-Seq and corresponding clinical characteristics of colon cancer were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, up to June 06, 2020) using the Genomic Data Commons (GDC) tool. The original data included 482 tumor tissues and 42 adjacent tissues. FPKM data was translated into transcripts per million (TPM) expression data. Based on the requirement of data integrality, patients that met the following criteria were excluded from the subsequent analysis: (1) repeated patient records; (2) missing follow-up time, T/N/M staging, or pathological stage information; (3) follow-up time was 0 days. Finally, our study identified 415 colon cancer samples as analytical datasets. All data were from publicly available databases, and there were no ethical issues involved.
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Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]
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Comparison of our immune-related gene signature using the ESTIMATE algorithm with immune-related gene signature using other methods.
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Subsequently, the least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized for further screening of prognostic genes to avoid overfitting the model.
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Fragments per kilobase million (FPKM) data on RNA-Seq and corresponding clinical characteristics of colon cancer were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, up to June 06, 2020) using the Genomic Data Commons (GDC) tool. The original data included 482 tumor tissues and 42 adjacent tissues. FPKM data was translated into transcripts per million (TPM) expression data. Based on the requirement of data integrality, patients that met the following criteria were excluded from the subsequent analysis: (1) repeated patient records; (2) missing follow-up time, T/N/M staging, or pathological stage information; (3) follow-up time was 0 days. Finally, our study identified 415 colon cancer samples as analytical datasets. All data were from publicly available databases, and there were no ethical issues involved.
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distinct clinical, molecular and survival characteristics [Expression]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:33401247:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:43e52ad06547262e0a3f99c988858f01741be2ececdf5ae85f0cf29ef644b026", "bytes": 552, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[8]/table-wrap[1]#t1", "version": null, "note": "Comparison of our immune-related gene signature using the ESTIMATE algorithm with immune-related gene signature using other methods.", "structure": {"pmcid": "PMC7803520", "section_index": 8, "section_title": "Comparison of the IRG signature using the ESTIMATE algorithm with models using other methods", "row_count": 6, "column_count": 4}, "table": {"label": "Table 1", "caption": "Comparison of our immune-related gene signature using the ESTIMATE algorithm with immune-related gene signature using other methods.", "rows": [["Immune-related gene signature", "AUC values"], ["1-Year", "3-Year", "5-Year"], ["Entire TCGA cohort", "", "", ""], ["Using ESTIMATE (Our model)", "0.795", "0.757", "0.728"], ["Using CIBERSORT (PMID 31689990)", "-", "0.676", "0.661"], ["Using IMMPORT (PMID 32375704)", "-", "0.663", "0.713"]], "row_count": 6, "column_count": 4, "numeric_cells": 12, "header_rows": 0, "locator": "section[8]/table-wrap[1]#t1"}}], "gaps": ["asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE39582 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], 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