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Immune cell infiltration-associated signature in colon cancer and its prognostic implications.

ABSTRACT: Tumor immune cell infiltration (ICI) has been reported in various studies to be correlated with tumor diagnosis, clinical treatment sensitivity and prognosis. It is an important direction to study the characteristics of immune cell infiltration and develop new prognostic markers to improve the treatment of colon cancer. In this paper, we systematically analyzed the ICI characteristics and obtained three ICI clusters. Then, the ICI scores were constructed and its prognostic implications were discussed. From the results, the ICI score patterns were linked to a great survival difference (p<0.001). A high ICI score was characterized by a higher fraction of plasma cells, CD8⁺ T cells, memory resting CD4⁺ T cells, monocytes, eosinophils and dendritic cells, which had better prognosis. Macrophages and neutrophils were increased in low ICI score patients with decreased overall survival. Immune checkpoint molecules (PDCD1, CD274, LAG3, IDO1, CTLA-4, TIGHT and HAVCR2) were found to be significantly overexpressed in the low ICI score subgroup. In addition, we also studied the correlation between the tumor mutation burden (TMB) and ICI score. This study indicated the ICI score could serve as a potential prognostic biomarker for colon cancer patients' immunotherapy.

SUBMITTER: Deng D

PROVIDER: S-EPMC8386549 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND Colon adenocarcinoma (COAD) is one of the most common and fatal malignant tumors, which increases the difficulty of prognostic predictions. Thus, new biomarkers for the diagnosis and prognosis of COAD should be explored. Ferroptosis is a recently identified programmed cell death process that has the characteristics of iron-dependent lipid peroxide accumulation. However, the predictive value of ferroptosis-related genes (FRGs) for COAD still needs to be further clarified. AIM To identify some critical FRGs and construct a COAD patient prognostic signature for clinical utilization. METHODS The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus databases were the data sources for mRNA expression and corresponding COAD patient clinical information. Differentially expressed FRGs were recognized using R and Perl software. We constructed a multi-FRG signature of the TCGA-COAD cohort by performing a univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis. COAD patients from the Gene Expression Omnibus cohort were utilized for verification. RESULTS Our research showed that most of the FRGs (85%) were differentially expressed between the corresponding adjacent normal tissues and cancer tissues in the TCGA-COAD cohort. Seven FRGs were related to overall survival (OS) in the univariate Cox analysis (all P < 0.05). A model with five FRGs (AKR1C1, AKR1C3, ALOX12, CRYAB, and FDFT1) was constructed to divide patients into high- and low-risk groups. The OS of patients in the high-risk group was significantly lower than that of the low-risk group (all P < 0.01 in the TCGA and Gene Expression Omnibus cohorts). The risk score was an independent prognosticator of OS in the multivariate Cox analysis (hazard ratio > 1, P < 0.01). The predictive capacity of the model was verified by a receiver operating characteristic curve analysis. In addition, a nomogram based on the expression of five hub FRGs and risk score can precisely predict the OS of individual COAD cancer patients. Immune correlation analysis and functional enrichment analysis results revealed that immunology-related pathways were abundant, and the immune states of the high-risk group and the low-risk group were different. CONCLUSION In conclusion, a novel five FRG model can be utilized for predicting prognosis in COAD. Targeting ferroptosis may be a treatment option for COAD.

Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.

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Immune cell infiltration-associated signature in colon cancer and its prognostic implications.

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