Construction of an immune-related gene signature to predict survival and treatment outcome in gastric cancer.

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However, it is unclear whether immune-related genes (IRGs) and the tumor immune microenvironment can predict prognosis for patients with gastric cancer (GC). The mRNA expression data in GC tissues (n = 368) were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs in patients with GC were determined using a computational difference algorithm. A prognostic signature was constructed using COX regression and random survival forest (RSF) analyses. In addition, datasets related to \"gemcitabine resistance\" and \"trastuzumab resistance\" (GSE58118 and GSE77346) were obtained for GEO database, and DEGs associated with drug-resistance were screened. Then, we analyzed correlations between gene expression and cancer immune infiltrates via Tumor Immune Estimation Resource (TIMER) site. The cBioportal database was used to analyze drug-resistant gene mutation status and survival. One hundred and fifty-five differentially expressed IRGs were screened between GC and normal tissues, and a prognostic signature consisting of four IRGs (NRP1, PPP3R1, IL17RA, and FGF16) was closely related to the overall survival (OS). According to cutoff value of risk score, patients were divided into high-risk and low-risk group. Patients in the high-risk group had shorter OS compared to the low-risk group in both the training (p < 0.0001) and testing sets (p = 0.0021). In addition, we developed a 5-IRGs (LGR6, DKK1, TNFRSF1B, NRP1, and CXCR4) signature which may participate in drug resistance processes in GC. Survival analysis showed that patients with drug-resistant gene mutations had shorter OS (p = 0.0459) and DFS (p < 0.001). We constructed four survival-related IRGs and five IRGs related to drug resistance which may contribute to predict the prognosis of GC.", "claims": [{"id": "claim-method", "statement": "Survival curves were generated by the Kaplan-Meier method and compared by log-rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33661721", "locator": "sections[8] \"Statistical analysis\" paragraph 2", "quote": "Survival curves were generated by the Kaplan-Meier method and compared by log-rank test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA-sequencing data (level 3) and corresponding clinical information of 368 patients with gastric cancer were downloaded from the TCGA data portal (https://cancergenome.nih.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33661721", "locator": "sections[3] \"Screening for differentially expressed IRGs\" paragraph 2", "quote": "RNA-sequencing data (level 3) and corresponding clinical information of 368 patients with gastric cancer were downloaded from the TCGA data portal (https://cancergenome.nih.gov/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33661721"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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