Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis.
Introduction:
As one of the most common digestive system malignancies, colorectal cancer (CRC) imposed grave danger on the public health. Cellular senescence involves gradual changes in functionality and reproducibility leading to abnormalities, including apoptosis resistance and enhanced secretion of inflammatory factors.
Methods
Cellular senescence-related gene set was determined by the application of WCGNA. We performed single-cell annotations of CRC cells and determined crucial signaling pathways through Cell chat analysis. Using LASSO and Cox analyses, we identified a gene set with prognostic values. Our model was validated using independent external cohort. In addition, we employed ssGSEA and xCell analyses to describe the detailed profile of infiltrated immune cells.
Results
We identified 3 distinct cell clusters in CRC samples, including T cells, myeloid cells, and B cells. We found that MIF signaling to CD74 + CD44 and CXCR4 displayed the highest interaction probability in the B cells communication. We determined a set of 6 genes of prognostic significance, GPR88, PTH1R, SFRP2, GPX3, ELFN1, and MS4A2. The prognostic differences between the two groups in the internal and external sets were found to be statistically significant. We observed higher infiltration of the activated B cells, CD4 + T cells, and CD8 + T cells in the LR group, which was characterized with an inferior prognosis. The abundance of CD8 + T cells were highly correlated with plasmacytoid and activated dendritic cells and follicular T helper cells.
Conclusion
Our study developed a prognostic model based on cellular senescence, which demonstrated significant efficacy in stratifying patients with CRC. Our findings offer new insights into potential precision immune treatments for this disease, with the hope of improving patient outcomes.
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