Identifying and evaluating a disulfidptosis-related gene signature to predict prognosis in colorectal adenocarcinoma patients.

Disulfidptosis, a regulated form of cell death, has been recently reported in cancers characterized by high SLC7A11 expression, including invasive breast carcinoma, lung adenocarcinoma, and hepatocellular carcinoma. However, its role in colon adenocarcinoma (COAD) has been infrequently discussed. In this study, we developed and validated a prognostic model based on 20 disulfidptosis-related genes (DRGs) using LASSO and Cox regression analyses. The robustness and practicality of this model were assessed via a nomogram. Subsequent correlation and enrichment analysis revealed a relationship between the risk score, several critical cancer-related biological processes, immune cell infiltration, and the expression of oncogenes and cell senescence-related genes. POU4F1, a significant component of our model, might function as an oncogene due to its upregulation in COAD tumors and its positive correlation with oncogene expression. In vitro assays demonstrated that POU4F1 knockdown noticeably decreased cell proliferation and migration but increased cell senescence in COAD cells. We further investigated the regulatory role of the DRG in disulfidptosis by culturing cells in a glucose-deprived medium. In summary, our research revealed and confirmed a DRG-based risk prediction model for COAD patients and verified the role of POU4F1 in promoting cell proliferation, migration, and disulfidptosis.

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.

Bortezomib-induced peripheral neuropathy: Clinical features, molecular basis, and therapeutic approach.

Bortezomib is the first-line standard and most effective chemotherapeutic for multiple myeloma; however, bortezomib-induced peripheral neuropathy (BIPN) severely affects the chemotherapy regimen and has long-term impact on patients under maintenance therapy. The pathogenesis of BIPN is poorly understood, and basic research and development of BIPN management drugs are in early stages. Besides chemotherapy dose reduction and regimen modification, no recommended prevention and treatment approaches are available for BIPN apart from the International Myeloma Working Group guidelines for peripheral neuropathy in myeloma. An in-depth exploration of the pathogenesis of BIPN, development of additional therapeutic approaches, and identification of risk factors are needed. Optimizing effective and standardized BIPN treatment plans and providing more decision-making evidence for clinical diagnosis and treatment of BIPN are necessary. This article reviews the recent advances in BIPN research; provides an overview of clinical features, underlying molecular mechanisms, and therapeutic approaches; and highlights areas for future studies.

Tumor-derived exosomal ADAM17 promotes pre-metastatic niche formation by enhancing vascular permeability in colorectal cancer.

BACKGROUND: Hematological metastasis has been recognized as a crucial factor contributing to the high rates of metastasis and mortality observed in colorectal cancer (CRC). Notably, exosomes derived from cancer cells participate in the formation of CRC pre-metastatic niches; however, the mechanisms underlying their effects are largely unknown. While our preliminary research revealed the role of exosome-derived disintegrin and metalloproteinase 17 (ADAM17) in the early stages of CRC metastasis, the role of exosomal ADAM17 in CRC hematogenous metastasis remains unclear. METHODS: In the present study, we isolated and purified exosomes using ultracentrifugation and identified exosomal proteins through quantitative mass spectrometry. In vitro, co-culture assays were conducted to evaluate the impact of exosomal ADAM17 on the permeability of the blood vessel endothelium. Vascular endothelial cell resistance, the cell index, membrane protein separation, flow cytometry, and immunofluorescence were employed to investigate the mechanisms underlying exosomal ADAM17-induced vascular permeability. Additionally, a mouse model was established to elucidate the role of exosomal ADAM17 in the modulation of blood vessel permeability and pre-metastatic niche formation in vivo. RESULTS: Our clinical data indicated that ADAM17 derived from the circulating exosomes of patients with CRC could serve as a blood-based biomarker for predicting metastasis. The CRC-derived exosomal ADAM17 targeted vascular endothelial cells, thus enhancing vascular permeability by influencing vascular endothelial cadherin cell membrane localization. Moreover, exosomal ADAM17 mediated the formation of a pre-metastatic niche in nude mice by inducing vascular leakage, thereby promoting CRC metastasis. Nonetheless, ADAM17 selective inhibitors effectively reduced CRC metastasis in vivo. CONCLUSIONS: Our results suggest that exosomal ADAM17 plays a pivotal role in the hematogenous metastasis of CRC. Thus, this protein may serve as a valuable blood-based biomarker and potential drug target for CRC metastasis intervention.

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer.

Objective: To establish a nomogram prediction model (based on clinicopathological and radiological features) for the development of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). Methods: This retrospective study included patients with CRC who underwent surgery at Changshu No.1 People's Hospital and the Second Affiliated Hospital of Soochow University between January 2016 and December 2018. The clinical, pathological, and radiological features of each patient were investigated. Risk factors for MLM were identified by univariable and multivariable analyses. The predictive nomogram for MLM development was constructed. The predictive performance of the nomogram was estimated by the receiver operating characteristics curve, calibration curve, and decision curve analysis. Results: This study included 161 patients with CRC [median age: 66 (range, 33-87) years]. Fifty-nine developed MLM after a median of 12 (range, 2-52) months after surgery. The multivariable logistic regression analysis showed that age >66 years (OR=3.471, 95% CI: 1.272-9.473, P=0.015), N2 stage (OR=6.534, 95% CI: 1.456-29.317, P=0.014), positive vascular invasion (OR=2.995, 95% CI: 1.132-7.926, P=0.027), positive tumor deposit (OR=4.451, 95% CI: 1.153-17.179, P=0.030), and linear (OR=6.774, 95% CI: 1.306-35.135, P=0.023) and nodal pericolic fat infiltration patterns (OR=8.762, 95% CI: 1.521-50.457, P=0.015) were independently associated with MLM. These five factors were used to create a nomogram. The area under the receiver operating characteristics curve of the nomogram was 0.866 (95% CI: 0.803-0.914), indicating favorable prediction performance. The calibration curve of the nomogram showed a satisfactory agreement between the predicted and actual probabilities. Conclusions: A nomogram prediction model based on five clinicopathological and radiological features might have favorable prediction performance for MLM in patients who underwent surgery for CRC. Hence, the present study proposes a nomogram that can easily be used to predict MLM after CRC surgery based on readily available features.