Background: Previous studies reported that stress-induced phosphoprotein 1 (STIP1) can be secreted by hepatocellular carcinoma (HCC) cells and is increased in the serum of HCC patients. However, the therapy-monitoring and prognostic value of serum STIP1 in HCC remains unclear. Here, we aimed to systemically explore the prognostic significance of serum STIP1 in HCC. Methods: A total of 340 HCC patients were recruited to this study; 161 underwent curative resection and 179 underwent transcatheter arterial chemoembolization (TACE). Serum STIP1 was detected by enzyme-linked immunosorbent assay (ELISA). Optimal cutoff values for serum STIP1 in resection and TACE groups were determined by receiver operating characteristic (ROC) analysis. Prognostic value was assessed by Kaplan-Meier, log-rank, and Cox regression analyses. Predictive values of STIP1 for objective response (OR) to TACE and MVI were evaluated by ROC curves and logistic regression. Results: Serum STIP1 was significantly increased in HCC patients when compared with chronic hepatitis B patients or health donors (both P <0.05). Optimal cutoff values for STIP1 in resection and TACE groups were 83.43 and 112.06 ng/ml, respectively. High pretreatment STIP1 was identified as an independent prognosticator. Dynamic changes in high STIP1 status were significantly associated with long-term prognosis, regardless of treatment approaches. Moreover, post-TACE STIP1 was identified as an independent predictor for OR, with a higher area under ROC curve (AUC-ROC) than other clinicopathological features. Specifically, pretreatment STIP1 was significantly increased in patients with microvascular invasion (MVI), and was confirmed as a novel, powerful predictor for MVI. Conclusions: Serum STIP1 is a promising biomarker for outcome evaluation, therapeutic response assessment, and MVI prediction in HCC. Integration serum STIP1 detection into HCC management might facilitate early clinical decision making to improve the prognosis of HCC.
Objective Cell metabolism plays a vital role in the proliferation, metastasis and sensitivity to chemotherapy drugs of colorectal cancer. The purpose of this multicenter cohort study is to investigate the potential genes indicating clinical outcomes in colorectal cancer patients. Methods We analyzed gene expression profiles of colorectal cancer to identify differentially expressed genes then used these differentially expressed genes to construct prognostic signature based on the least absolute shrink-age and selection operator Cox regression model. In addition, the multi-gene signature was validated in independent datasets including our multicenter cohort. Finally, nomograms were set up to evaluate the prognosis of colorectal cancer patients. Results Seventeen metabolism-related genes were determined in the least absolute shrink-age and selection operator model to construct signature, with area under receiver operating characteristic curve for relapse-free survival, 0.741, 0.755 and 0.732 at 1, 3 and 5 year, respectively. External validation datasets, GSE14333, GSE37892, GSE17538 and the Cancer Genome Atlas cohorts, were analyzed and stratified, indicating that the metabolism-related signature was reliable in discriminating high- and low-risk colorectal cancer patients. Area under receiver operating characteristic curves for relapse-free survival in our multicenter validation cohort were 0.801, 0.819 and 0.857 at 1, 3 and 5 year, respectively. Nomograms incorporating the genetic biomarkers and clinical pathological features were set up, which yielded good discrimination and calibration in the prediction of prognosis for colorectal cancer patients. Conclusion An original metabolism-related signature was developed as a predictive model for the prognosis of colorectal cancer patients. A nomogram based on the signature was advantageous to facilitate personalized counselling and treatment of colorectal cancer patients.
BackgroundCuproptosis is a novel form of programmed cell death termed as Cu-dependent cytotoxicity. However, the roles of cuproptosis-associated genes (CAGs) in lung adenocarcinoma (LUAD) have not been explored comprehensively.MethodsWe obtained CAGs and utilized consensus molecular clustering by “non-negative matrix factorization (NMF)” to stratify LUAD patients in TCGA (N = 511), GSE13213 (N = 117), and GSE31210 (N = 226) cohorts. The ssGSEA and CIBERSORT algorithms were used to evaluate the relative infiltration levels of immune cell types in tumor microenvironment (TME). The risk score based on CAGs was calculated to predict patients’ survival outcomes.ResultsWe identified three cuproptosis-associated clusters with different clinicopathological characteristics. We found that the cuproptosis-associated cluster with the worst survival rates exhibited a high enrichment of activated CD4/8+ T cells. In addition, we found that the cuproptosis-associated risk score could be used for patients’ prognosis prediction and provide new insights in immunotherapy of LUAD patients. Eventually, we constructed a nomogram-integrated cuproptosis-associated risk score with clinicopathological factors to predict overall survival in LUAD patients, with 1-, 3-, and 5-year area under curves (AUCs) being 0.771, 0.754, and 0.722, respectively, all of which were higher than those of the TNM stage.ConclusionsIn this study, we uncovered the biological function of CAGs in the TME and its correlations with clinicopathological parameters and patients’ prognosis in LUAD. These findings could provide new angles for immunotherapy of LUAD patients.
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