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Objective: To construct a new model for predicting the outcomes and therapeutic efficacy of colorectal cancer (CRC). Methods: Firstly, the univariate analysis and the least absolute shrinkage and selection operator (LASSO)-Cox regression analysis were used to train the GSE39582 dataset to construct a prognostic signature of colorectal cancer (PSCRC), and external datasets CRC_TCGA and GSE17536 were used to validate PSCRC. The correlations of PSCRC with clinical indicators, tumor immune microenvironment and immune cells infiltration were evaluated, and the molecular function of PSCRC was analyzed by gene set enrichment analysis (GSEA). Next, seven factors, such as PSCRC and clinical stage, were integrated to draw a prognostic nomogram, and the prognostic effect was evaluated by the decision curve analysis (DCA). Finally, the efficacy of immunotherapy and chemotherapy was predicted. Results: We constructed a PSCRC, which was validated by two external datasets, confirming its high prognostic sensitivity and specificity. TNM staging significantly affected the risk scores of PSCRC (all P < 0.001); PSCRC showed significantly positively correlations with tumor microenvironment matrix (TME)score, immune score, and ESTIMATE score (all P < 0.001), significantly positive correlations with infiltrations such as neutrophils (all P < 0.05), and significantly negative correlations with infiltrations like activated memory CD4+ T cells (all P < 0.01). In addition, the GSEA analysis indicated that PSCRC might participate in oxidative phosphorylation, angiogenesis, hypoxia and inflammatory response. Importantly, the newly constructed model showed a good prognostic ability, with a C-index of 0.765 and a 95% confidence interval (CI) of 0.747 to 0.783 (P < 0.001). Finally, in the three datasets, the therapy prediction results revealed that the low - risk scoring group had a high response rate to immunotherapy (all P < 0.001); PSCRC was significantly negatively correlated with the half inhibitory concentration (IC50) of chemotherapy drugs such as imatinib, dasatinib, and pazopanib (all P < 0.001), and significantly positively correlated with the IC50 of metformin, sorafenib and other drugs (all P <0.001). Conclusion: We construct a PSCRC, which provides a robust model for CRC prognosis. and also offers a potential marker for predicting treatment. [ABSTRACT FROM AUTHOR]

Objective: To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. Methods: Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. Results: The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. Conclusion: The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate. [ABSTRACT FROM AUTHOR]

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg‐related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg‐related prognostic genes were screened from Treg‐related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg‐related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6‐gene Treg‐related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low‐risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1‐, 3‐ and 5‐year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low‐risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high‐risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6‐gene Treg‐associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients. [ABSTRACT FROM AUTHOR]

Objective: The incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients. Methods: Overall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan–Meier (K–M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment. Results: According to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723–0.797) in the development cohort and 0.796 (95% CI 0.737–0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K–M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively. Conclusion: For NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients. Methods: Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO‐Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence‐free survival (BCRFS). Selected gene signatures established a risk score system. Univariate and multivariate analyses of prognostic factors about BCRFS were performed using the Cox proportional hazards regression models. A nomogram based on multivariate analysis was plotted to facilitate clinical application. Kyoto Encyclopedia of Gene and Genomes (KEGG) and Gene Ontology (GO) analyses were then executed for differentially expressed genes (DEGs). Results: Notably, the risk score could significantly identify BCRFS by time‐dependent receiver operating characteristic (t‐ROC) curves in the training set (3‐year area under the curve (AUC) = 0.820, 5‐year AUC = 0.809) and the validation set (3‐year AUC = 0.723, 5‐year AUC = 0.733). Conclusions: Clinically, the nomogram model, which incorporates Gleason score and the risk score, could effectively predict BCRFS and potentially be utilized as a useful tool for the screening of BCRFS in PCa. [ABSTRACT FROM AUTHOR]