Triglyceride-to-glucose index (TyG), a key diagnostic marker for insulin resistance, has been linked to colorectal cancer (CRC). Nevertheless, TyG prognostic significance in CRC survival has not been established. This study seeks to develop and validate a robust machine learning (ML) based predictive model combining TyG and inflammatory markers for predicting long-term survival outcomes in CRC patients. A retrospective study was performed on (n=1,893) CRC patients who underwent radical surgery at The First Affiliated Hospital of Kunming Medical University. The patients were randomly assigned to training cohort (70%) and an internal validation cohort (30%). An external validation cohort (n=493) from another hospital was used to test model generalizability. Independent prognostic factors were identified via multivariate Cox regression. An integrative predictive model was constructed using various ML algorithms [random survival forest (RSF), eXtreme Gradient Boosting (XGBoost), gradient boosting machines (GBM)], evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, calibration plots, and decision curve analyses (DCAs). The final model integrated eight independent prognostic factors: age, lymphocyte-to-monocyte ratio (LMR), TyG, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA199), Union for International Cancer Control (UICC) stage, tumor differentiation, and perineural invasion. The model achieved good predictive accuracy (C-index: training =0.742, internal validation =0.735, external validation =0.752). ROC curves demonstrated robust predictive accuracy for 1-, 3-, and 5-year survival [area under the curve (AUC): 0.79, 0.76, 0.74, respectively]. SHapley Additive exPlanations (SHAP) analysis ranked TyG as the second-most influential prognostic indicator. Elevated TyG index independently predicts favorable long-term outcomes in CRC patients. Our validated ML model, combining TyG, inflammatory markers, and clinicopathological features, provides a reliable, economical, and practical clinical tool for prognosis assessment. Further multicenter, prospective studies are necessary to confirm the widespread applicability of this model.

The exclusion of patients with prior malignancies from clinical trials remains controversial, yet its prognostic impact on adenocarcinoma of esophagogastric junction (AEG) is poorly characterized. This retrospective cohort study aimed to evaluate the prevalence and survival implications of prior cancer in AEG patients, providing evidence for refining trial eligibility criteria. The Surveillance, Epidemiology, and End Results databases were used to collect data between 1975 and 2021, and 10,895 AEG patients were included in the study. Inclusion required histologically confirmed AEG with complete follow-up data and only those with one prior cancer preceding the index AEG diagnosis to ensure temporal clarity. Prior cancers were classified according to their primary site, frequency, stage, and timing. Survival analyses included Kaplan-Meier curves, multivariate Cox regression, and competing risk models (Fine and Gray's). Propensity score matching was applied as a sensitivity analysis to validate the robustness of the primary findings. Among 10,895 eligible patients, 16.58% (n=1,806) had prior cancer history and a median age of 67 years, with 79.20% male patients. The cohort showed significantly different overall survival (median 13 months for prior cancer vs. 16 months for no prior cancer). Disease stage distribution was: localized (18.40%), regional (28.80%), distant (29.30%), and unstaged (23.50%). The most common types of prior cancer were prostate (28.07%), and colon and cecum (11.13%). Additionally, 38.7% of the prior tumors were staged localized disease at prior cancer diagnosis. The median time period between detection of first and subsequent malignancies was 6.0 years. Patients with prior cancer were older (median age: 73 vs. 67 years) and had higher rates of localized AEG (23.30% vs. 17.50%, P<0.001) but lower chemotherapy utilization (46.50% vs. 57.50%, P<0.001) or radiation therapy (37.60% vs. 42.50%, P<0.001). Prior cancer was independently associated with worse overall survival [adjusted hazard ratio (HR) =1.16, 95% confidence interval (CI): 1.09-1.23, P<0.001] but not cancer-specific survival (HR =1.03, 95% CI: 0.96-1.10, P=0.43). However, Subgroup analyses revealed several patient populations where prior malignancy demonstrated no adverse prognostic impact. Patients with prior cancer diagnosed within 1 year showed comparable overall survival (HR =1.03, 95% CI: 0.91-1.15, P=0.67) and significantly better cancer-specific survival (HR =0.58, 95% CI: 0.49-0.68, P<0.001) in competing risk analysis. Those with localized-stage prior cancers exhibited no significant difference in cancer-specific survival (HR =0.91, 95% CI: 0.82-1.02, P=0.12). Notably, specific cancer types including breast (HR =1.00, 95% CI: 0.84-1.20, P=0.97), and lymphoma (HR =1.17, 95% CI: 0.89-1.54, P=0.27) showed neutral effects on overall survival. While prior cancer history adversely impacts overall survival in AEG patients, specific subgroups-particularly those with select cancer types-exhibit comparable outcomes. These findings suggest that current exclusion criteria may be overly restrictive, and support refining trial eligibility to include well-selected prior-cancer patients.

The optimal treatment strategies for gastric cancer with peritoneal metastasis (GCPM) are extensively debated, especially regarding the appropriateness of aggressive local treatments. This study aimed to construct robust competing-risk nomograms for prognostic prediction and to establish a novel risk-stratification system to facilitate individualized therapeutic decision-making. Patients diagnosed with GCPM between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts at a 7:3 ratio. Propensity score matching (PSM) was applied to evaluate the survival impact of different treatment strategies. Independent prognostic factors in the training cohort were selected using a combination of least absolute shrinkage and selection operator (LASSO) regression and stepwise Akaike information criterion (AIC), and these variables were subsequently used to construct Cox [for overall survival (OS)] and Fine-Gray [for cancer-specific survival (CSS)] nomograms. Model discrimination, calibration, predictive accuracy, and clinical utility were assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Risk stratification was further performed based on the nomogram-derived scores, and treatment benefits were analyzed across different risk groups. A total of 4,280 patients were included, comprising 2,996 in the training cohort and 1,284 in the validation cohort. PSM analysis demonstrated that, among unstratified patients, aggressive therapy significantly improved survival compared with chemotherapy-based treatment (P<0.001). Twelve independent prognostic factors, including treatment strategy, tumor grade, and metastatic burden, were identified for the construction of nomograms. The OS and CSS nomograms demonstrated favorable discrimination [C-index: 0.68-0.71; 2-year area under the curve (AUC): 0.792; 95% confidence interval (CI): 0.769-0.815] and calibration in both cohorts, while DCA suggested a potential for greater clinical net benefit than the American Joint Committee on Cancer (AJCC) staging system across a range of threshold probabilities. Risk stratification utilizing nomogram scores effectively distinguished subgroups with different prognostic levels. Treatment-benefit analyses revealed that aggressive therapy was associated with significantly reduced the risk of death in the low-risk group [hazard ratio (HR) =0.6, P<0.001], whereas it was associated with increased mortality risk in the high-risk group (characterized by advanced age and extensive tumor burden; HR >1, P<0.001). This study developed and validated competing-risk nomograms for GCPM patients and proposed a new risk-stratification system. This system supports the concept of risk-adapted therapeutic strategies, suggesting that low-risk patients may be more likely to derive survival benefits from aggressive therapy, while high-risk patients are better suited for systemic chemotherapy or palliative care to avoid ineffective and potentially harmful overtreatment.

Rituximab (RIT) resistance significantly hampers the treatment efficacy in diffuse large B-cell lymphoma (DLBCL), leading to poor prognosis. We aim to investigate the potential of stigmasterol, a natural compound, to reverse RIT resistance in DLBCL through modulation of the mitogen-activated protein kinase 1 (MAPK1) signaling pathway. The RIT-resistant DLBCL model, Raji-4RH cells, was established by prolonged exposure to increasing concentrations of RIT. The effects of stigmasterol on cell viability, proliferation, migration, and invasion were assessed using Cell Counting Kit-8 (CCK-8) assays, 5-ethynyl-2'-deoxyuridine (EdU) staining, and Transwell assays. Additionally, the role of MAPK1 signaling in stigmasterol's mechanism of action was evaluated by overexpressing MAPK1 in Raji-4RH cells, and the expression of key multidrug resistance (MDR) proteins was analyzed. Stigmasterol effectively reversed RIT resistance by inhibiting cell proliferation, migration, and invasion. It induced G0/G1 cell cycle arrest and apoptosis, significantly downregulated MDR proteins P-gp, MRP5, and BCRP, and inhibited MAPK1 phosphorylation. The anti-resistance effects of stigmasterol were partially reversed by MAPK1 overexpression, confirming that MAPK1 signaling plays a critical role in its mechanism. Stigmasterol significantly reverses RIT resistance in DLBCL by inhibiting MAPK1 phosphorylation and downregulating MDR protein expression. These findings provide new insights into overcoming RIT resistance and support the potential of stigmasterol as a therapeutic strategy for refractory DLBCL.

Microbiota and their metabolites form a dynamic regulatory network that modulates the tumor microenvironment (TME) and immune cell functionality, emerging as critical targets in cancer immunotherapy. Despite remarkable advances in immune checkpoint inhibitors (ICIs), clinical efficacy remains limited by primary or acquired resistance in a substantial proportion of patients. Accumulating evidence indicates that the gut microbiota-metabolite-immune axis is a critical determinant of ICI responsiveness, but the underlying molecular mechanisms and tumor-specific regulatory patterns remain incompletely elucidated. This narrative review aims to systematically dissect the mechanistic interplay of this axis across diverse cancer types and synthesize current microbiota-targeted therapeutic strategies for improved immunotherapy outcomes. We conducted a systematic literature search of PubMed for studies published between January 2019 and December 2025, with a particular focus on basic and translational research elucidating the roles of gut microbiota and their metabolites in cancer immunotherapy across various malignancies. This review elucidates the molecular mechanisms by which core metabolites regulate antitumor immunity, synthesizes characteristic microbial signatures across gastrointestinal and non-gastrointestinal cancers. Furthermore, we evaluate the translational potential of intervention strategies, including fecal microbiota transplantation, probiotic supplementation, and engineered microbes, as adjuvants to enhance ICI efficacy and overcome resistance. Microbiota-based personalized therapeutic strategies are increasingly recognized as promising tools to modulate antitumor immunity and improve immunotherapy outcomes. Systematic profiling of the gut microbiota-metabolite-immune axis across cancer types will facilitate the development of precision interventions to overcome ICI resistance. Future research integrating multi-omics approaches and large-scale clinical trials is pivotal to translate mechanistic insights into clinically viable therapies, ultimately revolutionizing the landscape of cancer immuno-oncology.

Oral squamous cell carcinoma (OSCC) is the most common oral malignancy, affecting the tongue, palate, mouth floor, alveolar ridge, and buccal mucosa. Kaempferol, a natural flavonoid, exhibits anti-oral cancer properties, but its mechanisms remain unclear. This study aimed to investigate kaempferol's therapeutic potential and molecular mechanisms in OSCC using network pharmacology, molecular docking, and experimental validation. Kaempferol's components and targets were identified via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). OSCC-related genes were sourced from GeneCards and Gene Expression Omnibus (GEO) databases. Network and molecular docking analyses were performed using Cytoscape and Autodock, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using Database for Annotation, Visualization and Integrated Discovery (DAVID). In vitro and in vivo experiments validated kaempferol's effects. Kaempferol induced ferroptosis, reducing OSCC cell viability and increasing apoptosis dose-dependently. Carbonic anhydrase IX (CA9) emerged as a core gene mediating kaempferol's anti-OSCC effects via ferroptosis. Molecular docking confirmed strong binding affinity between kaempferol and CA9 (-6.24 kcal/mol). GO and KEGG analyses revealed kaempferol's modulation of p53/FoxO/cellular senescence pathways. Experiments confirmed kaempferol's inhibition of OSCC proliferation and migration through CA9/ferroptosis induction. Kaempferol exerts anti-OSCC effects via CA9/ferroptosis, highlighting its potential as a therapeutic agent for OSCC.

Esophageal squamous cell carcinoma (ESCC) is a leading cause of cancer-related mortality globally, characterized by high incidence, poor prognosis, and limited therapeutic options. While long non-coding RNAs (lncRNAs) and microRNAs have been implicated in ESCC pathogenesis through competing endogenous RNA (ceRNA) networks, the regulatory mechanisms underlying ferroptosis-related ceRNA interactions remain poorly defined. This study aimed to elucidate the functional role and molecular mechanism of the HOX transcript antisense intergenic RNA (HOTAIR)/miR-129-5p/brain and acute leukemia, cytoplasmic (BAALC) axis in ESCC progression. The regulatory relationship within the HOTAIR/miR-129-5p/BAALC axis was validated using dual-luciferase reporter assays. Intracellular iron concentration and ferroptosis were measured following BAALC knockdown. Phenotypic effects on ESCC cell proliferation and migration were assessed through functional assays. Rescue experiments were conducted to determine BAALC dependency in HOTAIR-mediated malignant progression. Inhibition of BAALC expression significantly increased intracellular iron levels and promoted ferroptosis. The HOTAIR/miR-129-5p/BAALC axis markedly enhanced ESCC cell proliferation and migration. Mechanistically, HOTAIR functioned as a ceRNA by sequestering miR-129-5p, thereby alleviating its repression of BAALC and upregulating BAALC expression. Rescue experiments further demonstrated that the oncogenic effects of HOTAIR were partially mediated through BAALC. This study unveils a novel ferroptosis-associated ceRNA regulatory axis in ESCC, demonstrating that HOTAIR promotes malignant progression by modulating the miR-129-5p/BAALC pathway. These findings provide new mechanistic insights and potential therapeutic targets for ESCC diagnosis and treatment.