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Background: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD‐related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. Method: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome‐based CRC prognostic models. Result: Our integrated model successfully identified differentially expressed PCD‐related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high‐risk and low‐risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. Conclusion: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC. [ABSTRACT FROM AUTHOR]

Background: The data of the prognostic role of V‐Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations in early‐stage lung adenocarcinoma (LUAD) patients is scarce. This study aimed to investigate the proportion, clinicopathological features, and prognostic significance of patients with stage I LUAD carrying BRAF mutations. Methods: We collected 431 patients with pathological stage I LUAD from cBioPortal for Cancer Genomics and 1604 LUAD patients tested for BRAF V600E and epidermal growth factor receptor (EGFR) mutations from Shanghai Pulmonary Hospital. Survival curves were drawn by the Kaplan–Meier method and compared by log‐rank test. Cox proportional hazard models, propensity‐score matching (PSM), and overlap weighting (OW) were performed in this study. The primary endpoint was recurrence‐free survival (RFS). Results: The proportion of BRAF mutations was estimated at 5.6% in a Caucasian cohort. BRAF V600E mutations were detected in six (1.4%) patients in Caucasian populations and 16 (1.0%) patients in Chinese populations. Two BRAF V600E‐mutant patients were detected to have concurrent EGFR mutations, one for 19‐del and one for L858R. For pathological stage I LUAD patients, BRAF mutations were not significantly associated with worse RFS than wild‐type BRAF patients (HR = 1.111; p = 0.885). After PSM and OW, similar results were presented (HR = 1.352; p = 0.742 and HR = 1.246; p = 0.764, respectively). BRAF V600E mutation status also lacked predictive significance for RFS (HR, 1.844; p = 0.226; HR = 1.144; p = 0.831 and HR = 1.466; p = 0.450, respectively). Conclusions: In this study, we demonstrated that BRAF status may not be capable of predicting prognosis in stage I LUAD patients. There is a need for more data to validate our findings. [ABSTRACT FROM AUTHOR]

Background: Although branched chain amino acid transaminase 1 (BCAT1) has been identified to play an essential role in multiple tumors, no studies on its role in pan‐cancer have been consulted before. Methods: The study comprehensively analyzes the expression, potential mechanisms, and clinical significance of BCAT1 in pan‐cancer through utilizing 16,847 samples, providing novel clues for the treatment of cancers. A Kruskal–Wallis test and the Wilcoxon rank‐sum and signed‐rank tests were applied to investigate diverse BCAT1 expression between various groups (e.g., cancer tissues versus normal tissues). Spearman's rank correlation coefficient was used in all correlation analyses in the study. Cox analyses and Kaplan‐Meier curves were utilized to identify the prognosis significance of BCAT1 expression in cancers. The significance of BCAT1 expression in differentiating cancer and non‐cancer tissues was explored via the area under the receiver operating characteristic curves (AUC). Results: The differential expression of BCAT1 was detected in various cancers (p < 0.05), which is relevant to some DNA methyltransferases expression. BCAT1 expression was associated with mismatch repair gene expression, immune checkpoint inhibitors expression, microsatellite instability, and tumor mutational burden in some cancers, indicating its potential in immunotherapy. BCAT1 expression showed prognosis significance and played a risk role in multiple cancers (hazard ratio > 0, p < 0.05). BCAT1 expression also demonstrated conspicuous ability to distinguish some cancers tissues from their normal tissues (AUC > 0.7), indicating its potential to detect cancers. Further analyses on head and neck squamous cell carcinoma certified upregulated BCAT1 expression at both mRNA and protein levels in this disease based on in‐house tissue microarrays and multicenter datasets. Conclusions: For the first time, the research comprehensively demonstrates the overexpression of BCAT1 in pan‐cancer, which improves the understanding of the pathogenesis of BCAT1 in pan‐cancer. Upregulated BCAT1 expression represented a poor prognosis for cancers patients, and it serves as a potential marker for cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Metastasis accounts for 90% of cancer death worldwide, and effective therapeutic strategies are lacking. The aim of this work is to identify the key drivers in tumor metastasis and screen therapeutics for treatment of esophageal squamous cell carcinoma (ESCC). Gene Ontology analysis of The Cancer Genome Atlas (TCGA) gene expression datasets of ESCC patients with or without lympy metastasis identifies that TGFβ2 is highly enriched in the pathways essential for tumor metastasis and upregulates in the metastatic ESCC tumors. High TGFβ2 expression in ESCC correlates with metastasis and patient survival, and functionally contributes to tumor metastasis via activating extracellular signal‐regulated kinases (ERK) signaling. By screening of a library consisting of 429 bioactive compounds, imperatorin is verified as a novel TGFβ2 inhibitor, with robustly suppressive effect on tumor metastasis in multiple mice models. Mechanistically, direct binding of imperatorin and CREB1 inhibits phosphorylation, nuclear translocation of CREB1, and its interaction with TGFβ2 promoter, represses TGFβ2 expression and fibroblasts‐secreted CCL2, and then inactivates ERK signaling to block cancer invasion and abrogates the paracrine effects of fibroblasts on tumor angiogenesis and metastasis. Overall, the findings suggest the use of TGFβ2 as a diagnostic and prognostic biomarker and therapeutic target in ESCC, and supports the potential of imperatorin as a novel therapeutic strategy for cancer metastasis. [ABSTRACT FROM AUTHOR]

Neuroblastoma (NBL) is the most frequently encountered extracranial solid neoplasm and impacts significantly on the survival of patients, especially in cases of advanced tumor stage or relapse. A long noncoding RNA (lncRNA) signature to predict the survival of patients with NBL is proposed in this paper. Differentially expressed lncRNA (DElncRNA) was selected using the Limma plus Voom package in R based on the RNA‐sequencing data downloaded from the Therapeutically Applicable Research To Generate Effective Treatments database and Genotype‐Tissue Expression database. Univariate cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate cox regression analysis were conducted to identify candidate DElncRNAs for the risk signature. Consequently, 10 DElncRNAs were designated as candidate DElncRNAs for the risk signature. Time‐dependent receiver operating characteristic curves and Kapan–Meier survival curves confirmed the efficacy of the risk signature in predicting the survival of patients with NBL (area under the curve = 0.941; p ≤.001). One of the DElncRNA constituent subparts (LINC01010) was significantly associated with the survival outcome of patients with NBL in GSE62564 (p =.004). Thus, a risk signature comprising 10 DElncRNAs was identified as effective for individual risk stratification and the survival prediction outcomes of patients with NBL. [ABSTRACT FROM AUTHOR]