Developing an advanced diagnostic model for hepatocellular carcinoma through multi-omics integration leveraging diverse cell-death patterns.

Introduction: Hepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data. Methods: Six categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments. Results: Collectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy. Conclusions: The seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

The optimal intravesical maintenance chemotherapy scheme for the intermediate-risk group non-muscle-invasive bladder cancer.

OBJECTIVE: Although the current European Association of Urology(EAU) guideline recommends that patients with intermediate-risk non-muscle-invasive bladder cancer (NMIBC) should accept intravesical chemotherapy or Calmette-Guerin (BCG) for no more than one year after transurethral resection of bladder tumor(TURBT), there is no consensus on the optimal duration of chemotherapy. Hence, we explored the optimal duration of maintenance intravesical chemotherapy in patients with intermediate-risk NMIBC. SUBJECTS AND METHODS: This was a real-world single-center retrospective cohort study. In total 158 patients with pathologically confirmed intermediate-risk NMIBC were included, who were divided into 4 subgroups based on the number of instillations given. We used Cox regression analysis and survival analysis chart to explore the 3-yr recurrence outcomes of tumor.The optimal duration was determined by receive operating characteristic curve (ROC). RESULTS: The median follow-up was 5.2 years. Compared with instillation for 1-2 months, the Hazard Ratios(HR) values of instillation for less than 1 month, maintenance instillation for 3-6 months and > 6 months were 3.57、1.57 and 0.22(95% CI 1.27-12.41;0.26-9.28;0.07-0.80, P = 0.03;0.62;0.02, respectively). We found a significant improvement in 3-yr relapse-free survival in intermediate-risk NMIBC patients who maintained intravesical instillation chemotherapy for longer than 6 months, and the best benefit was achieved with 10.5 months of maintenance chemotherapy by ROC. CONCLUSIONS: In our scheme, the optimal duration of intravesical instillation with pirrubicin is 10.5 months. This new understanding provides valuable experience for the precise medical treatment model of intermediate-risk NMIBC.

A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes.

Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer-Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670-0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes.

Nomogram for Predicting Risk of Digestive Carcinoma Among Patients with Type 2 Diabetes.

PURPOSE: Digestive carcinomas remain a major health burden worldwide and are closely related to type 2 diabetes. The aim of this study was to develop and validate a digestive carcinoma risk prediction model to identify high-risk individuals among those with type 2 diabetes. PATIENTS AND METHODS: The prediction model was developed in a primary cohort that consisted of 655 patients with type 2 diabetes. Data were collected from November 2013 to December 2018. Clinical parameters and demographic characteristics were analyzed by logistic regression to develop a model to predict the risk of digestive carcinomas; then, a nomogram was constructed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. The results were internally validated by a bootstrapping procedure. The independent validation cohort consisted of 275 patients from January 2019 to December 2019. RESULTS: Predictors in the prediction nomogram included sex, age, insulin use, and body mass index. The model showed good discrimination (C-index 0.747 [95% CI, 0.718-0.791]) and calibration (Hosmer-Lemeshow test P=0.541). The nomogram showed similar discrimination in the validation cohort (C-index 0.706 [95% CI, 0.682-0.755]) and good calibration (Hosmer-Lemeshow test P=0.418). Decision curve analysis demonstrated that the nomogram would be clinically useful. CONCLUSION: We developed a low-cost and low-risk model based on clinical and demographic parameters to help identify patients with type 2 diabetes who might benefit from digestive cancer screening.