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Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C -index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group. Conclusion. We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.

Objective: We aimed to construct a multi-immune gene model for the prognosis of colorectal cancer. This study would not only provide important clinical data for the evaluation of survival and prognosis of colorectal cancer, but provide insights into the tumor immune mechanisms.Methods: Colorectal cancer gene expression and clinicopathological data were downloaded from the TCGA database, and then we performed gene expression analysis to obtain differentially expressed genes. In addition, we downloaded immune genes from the ImmPort immune gene database, and obtained differentially expressed immune genes after intersection with the differentially expressed colorectal cancer genes. We further performed survival analysis of the differential immune genes to obtain prognosis-related genes, which were used to construct a multi-immune gene prognostic model. We then analyzed the impact of the prognostic model risk score on the survival of colorectal cancer patients through survival analysis, using ROC analysis. In addition, we performed risk curve analysis to validate the accuracy of the prognostic model risk score in assessing the prognosis of colorectal cancer, and also conducted independent prognostic analysis. Finally, we analyzed the correlation between the immune genes, and transcription factors as well as immune cells.Results: Our analysis showed that prognosis of the high-risk group as evaluated by the immune gene prognosis model risk score was poor (P<0.001). The prognostic model risk score could accurately classify the colorectal patients and has high accuracy in the analysis of prognosis of colorectal cancer (AUC=0.861). Our data demonstrated a certain correlation between the immune genes, transcription factors and immune cells.Conclusions: The constructed prognostic model could accurately assess the prognosis and survival of patients with colorectal cancer. Immune genes might regulate malignant progression of tumors by modulating the production of transcription factors and immune cells. This study demonstrated the influence of immune factors on the prognosis of colorectal cancer and provided a reference for further studies evaluating the role of immunity in the development of colorectal cancer.

PurposeTo explore fecal immune-related proteins that can be used for colorectal cancer (CRC) diagnosis.Patients and methodsThree independent cohorts were used in present study. In the discovery cohort, which included 14 CRC patients and 6 healthy controls (HCs), label-free proteomics was applied to identify immune-related proteins in stool that could be used for CRC diagnosis. Exploring potential links between gut microbes and immune-related proteins by 16S rRNA sequencing. The abundance of fecal immune-associated proteins was verified by ELISA in two independent validation cohorts and a biomarker panel was constructed that could be used for CRC diagnosis. The validation cohort I included 192 CRC patients and 151 HCs from 6 different hospitals. The validation cohort II included 141 CRC patients, 82 colorectal adenoma (CRA) patients, and 87 HCs from another hospital. Finally, the expression of biomarkers in cancer tissues was verified by immunohistochemistry (IHC).ResultsIn the discovery study, 436 plausible fecal proteins were identified. And among 67 differential fecal proteins (|log2 fold change| &gt; 1, P&lt; 0.01) that could be used for CRC diagnosis, 16 immune-related proteins with diagnostic value were identified. The 16S rRNA sequencing results showed a positive correlation between immune-related proteins and the abundance of oncogenic bacteria. In the validation cohort I, a biomarker panel consisting of five fecal immune-related proteins (CAT, LTF, MMP9, RBP4, and SERPINA3) was constructed based on the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The biomarker panel was found to be superior to hemoglobin in the diagnosis of CRC in both validation cohort I and validation cohort II. The IHC result showed that protein expression levels of these five immune-related proteins were significantly higher in CRC tissue than in normal colorectal tissue.ConclusionA novel biomarker panel consisting of fecal immune-related proteins can be used for the diagnosis of CRC.