A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer.

Background: Increasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients. Methods: This inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, who were separated into a training group and an internal validation group. The nomogram was built with the training set, then internally verified with SEER data, and externally validated with two different data sets. Based on the RNA-seq data, ELNs-related DERNAs (DElncRNAs, DEmiRNAs, andDEmRNAs) and immune cells were identified. The LASSO-Cox regression analysis was utilized to construct ELNs-related DERNAs and immune cell prognostic signature in The Cancer Genome Atlas (TCGA) cohort. The OS of subgroups with high- and low-ELN signature was compared using the Kaplan-Meier (K-M) analysis. A nomogram was successfully constructed based on the ELNs signature and other clinical characteristics. The concordance index (C-index), calibration plot, receiver operating characteristic curve, and decision curve analysis (DCA) were all used to evaluate the nomogram model. The meta-analysis, the Gene Expression Profiling Interactive Analysis database, and reverse transcription-quantitative PCR (RT-qPCR) were utilized to validate the RNA expression or abundance of prognostic genes and immune cells between GC tissues and normal gastric tissues, respectively. Finally, we analyzed the correlations between immune checkpoints, chemotherapy drug sensitivity, and risk score. Results: The multivariate analysis revealed that the high ELNs improved OS compared with low ELNs (hazard ratio [HR] = 0.659, 95% confidence interval [CI]: 0.626-0.694, p < 0.0001). Using the training set, a nomogram incorporating ELNs was built and proven to have good calibration and discrimination (C-index [95% CI], 0.714 [0.710-0.718]), which was validated in the internal validation set (C-index [95% CI], 0.720 [0.714-0.726]), the TCGA set (C-index [95% CI], 0.693 [0.662-0.724]), and the Chinese set (C-index [95% CI], 0.750 [0.720-0.782]). An ELNs-related signature model based on ELNs group, regulatory T cells (Tregs), neutrophils, CDKN2B-AS1, H19, HOTTIP, LINC00643, MIR663AHG, TMEM236, ZNF705A, and hsa-miR-135a-5p was constructed by the LASSO-Cox regression analysis. The result showed that OS was remarkably lower in patients with high-ELNs signature compared with those with low-ELN signature (HR = 2.418, 95% CI: 1.804-3.241, p < 0.001). This signature performed well in predicting 1-, 3-, and 5-year survival (AUC [95% CI] = 0.688 [0.612-0.763], 0.744 [0.659-0.830], and 0.778 [0.647-0.909], respectively). The multivariate Cox analysis illustrated that the risk score was an independent predictor of survival for patients with GC. Moreover, the expression of prognostic genes (LINC00643, TMEM236, and hsa-miR-135a-5p) displayed differences between GC tissues and adjacent non-tumor tissues. The C-index of the nomogram that can be used to predict the OS of GC patients was 0.710 (95% CI: 0.663-0.753). Both the calibration plots and DCA showed that the nomogram has good predictive performance. Moreover, the signature was significantly correlated with the N stage and T stage. According to our analysis, GC patients in the low-ELN signature group may have a better immunotherapy response and OS outcome. Conclusions: We explored the prognostic role of ELNs in GC and successfully constructed an ELNs signature linked to the GC prognosis in TCGA. The findings manifested that the signature is a powerful predictive indicator for patients with GC. The signature might contain potential biomarkers for treatment response prediction for GC patients. Additionally, we identified a novel and robust nomogram combining the characteristics of ELNs and clinical factors for predicting 1-, 3-, and 5-year OS in GC patients, which will facilitate personalized survival prediction and aid clinical decision-making in GC patients.

Pan-Cancer Analysis of PDIA3: Identifying It as a Potential Biomarker for Tumor Prognosis and Immunotherapy.

Protein disulfide isomerase A3 (PDIA3) is a kind of thiol oxidoreductase with a wide range of functions, and its expression is elevated in a variety of tumors, which is closely related to the invasion and metastasis of tumor cells, and has a significant impact on the immunogenicity of tumor cells. Although more and more studies have shown that PDIA3 plays an important role in the occurrence and development of many tumors, there is no systematic pan-cancer study on PDIA3. Therefore, in this study, the differential expression of PDIA3 in 33 kinds of tumors was analyzed to explore its ability to regulate tumor immunity as a biomarker and evaluate its role in different cancer onset stages or clinical prognosis. In this paper, by analyzing the multilevel data including 33 kinds of cancers in the databases of Cancer Genome Atlas (TCGA), UCSC Xena, Cancer Cell Encyclopedia (CCLE), Genotypic Tissue Expression (GTEx), Human Protein Atlas (HPA), cBioPortal, and GDC; the differential expression level of PDIA3 in different types of malignant tumors and its relationship with prognosis and the potential correlation between PDIA3 expression and microsatellite instability (MSI), tumor mutation load (TMB), mismatch repair gene (MMR), DNA methylation level, and immune infiltration level were analyzed with bioinformatics. The results showed that PDIA3 was highly expressed in 19 types of cancers, but downregulated only in THCA. Next, PDIA3 in different tumors was positively or negatively correlated with patient outcome, Kaplan-Meier survival analysis showed that PDIA3 plays an important role in the prognosis of patients with KIRP, KICH, and CESC and may be used as a prognostic biomarker, and the methylation level of PDIA3 promoter region was closely related to patient outcome in eight tumors. The expression level of PDIA3 was correlated with TMB in 13 tumors and MSI in 9 tumors. Among them, the expression level of PDIA3 in THYM has the strongest correlation with TMB, and the expression level of PDIA3 in READ has the strongest correlation with MSI. In addition, the expression of PDIA3 in eight kinds of tumors, including BRCA, HNSC, THYM, LGG, LUAD, LUSC, PRAD, and THCA, had the highest correlation with the infiltration degree of immune cells, and the expression of PDIA3 had the highest correlation with the infiltration degree of 11 kinds of immune cells, including regulatory T cell and macrophages. And LGG is the tumor most likely to be affected by the tumor microenvironment to affect its development and prognosis. To sum up, this study suggests that PDIA3 plays an important role in the occurrence and development of KIRP, KICH, and CESC and in the immunotherapeutic response of THYM, READ, and LGG and can be used as a prognostic biomarker for these tumors.