Background: The ferroptosis inhibitory gene Solute carrier family 7 member 11 (SLC7A11) provides a new strategy for anticancer treatment. However, its function in renal cell carcinoma (RCC) remains elusive.Methods: The expression and somatic mutation information of SLC7A11 in RCC samples were determined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Oncomine, and cBioPortal databases. The prognostic value of SLC7A11 was assessed through survival analysis, Receiver operating characteristic curve (ROC) analysis, independent prognostic analysis, clinical subgroup analysis, and nomogram. Its prognostic value was also validated in the ICGC and GSE29607 cohorts. Gene set enrichment analysis (GSEA) was employed to investigate the effects of SLC7A11 on multiple metabolic pathways. The CIBERSORT algorithm and single‐sample gene set enrichment analysis (ssGSEA) method were applied to evaluate the effects of SLC7A11 on the tumor immune microenvironment (TIM). SLC7A11’s therapeutic correlations were analyzed using the GSE87121, GSE67501, and GSDC datasets. Finally, the biofunctions of SLC7A11 in renal cancer cells and ferroptosis were ascertained by MTT, wound healing, transwell, and western blot assays.Results: Through multiple datasets, SLC7A11 was found to be markedly upregulated in RCC. In terms of prognosis, SLC7A11 overexpression conferred a worse prognosis and was identified as an independent prognostic factor. Its prognostic value was validated in ICGC cohort. Moreover, high SL7CA11 expression could stimulate nucleotides, fatty acids, and amino acid metabolism to meet the proliferative consumption of tumor cells. As for the immune effect, SLC7A11 suppressed antitumor immunity by reducing the abundances of CD8+ T and NK cells. Regarding the therapeutic response, SLC7A11 expression was not correlated with the sensitivities of most chemotherapy and targeted drugs. Finally, SLC7A11 promoted the proliferation, migration, and invasion of renal cancer cells by enhancing GPX4 output, which in turn inhibits ferroptosis.Conclusions: SLC7A11 not only deeply influences RCC prognosis and TIM, but also promotes RCC progression by inhibiting ferroptosis and inducing metabolic reprogramming. In addition, SLC7A11 weakly affects the therapeutic effect and sensitivities of multiple chemotherapy and targeted drugs.

The specific roles of N6-methyladenosine (m6A) regulatory genes in pancreatic adenocarcinoma (PAAD) have not been fully elucidated. In present study, a novel risk signature was constructed by five m6A-related genes (including METTL3, METTL14, KIAA1429, ALKBH5 and YTHDF1) and was identified as an independent prognostic factor (HR = 13.192) via TCGA (185 samples) databases. The immune abundances of 22 leukocyte subtypes in each PAAD sample were exhibited via the CIBERSORT algorithm. High risk group promoted infiltration levels of Macrophages M0 and M2 cells and decreased that of B cells naive, T cells CD8 and T regulatory cells.

Pyroptosis is an inflammatory programmed cell death, showing potentials to be a novel anti-cancer approach. However, the roles of pyroptosis-related (PR) genes (PRGs) in pancreatic adenocarcinoma (PAAD) remain elusive. In the present study, we constructed a novel PR risk signature through the lasso regression analysis. The risk signature was greatly conducive to PAAD prognostic assessment. PR risk score was identified as an independent prognostic factor and could distinguish the prognostic differences of most clinical subgroups. Meanwhile, it could improve the traditional prognostic models based on TNM-staging. Next, its prognostic value was also tested in five validation cohorts. Using CIBERSORT, ESTIMATE, and ssGSEA algorithms, the effects of PR risk signature on tumor immune microenvironment (TIM) were explored. High PR risk suppressed antitumor immune through decreasing the infiltrating levels of CD8 T and NK cells. The genomic information and histological expression of risk PRGs were uncovered by USCA and HPA databases. Somatic mutation, methylation alteration, and homozygous CNV of eight PRGs barely occurred in PAAD samples. As for therapeutic correlation, PR risk score may not predict the efficacy of PD-1/L1 inhibitors and was weakly associated with multiple drug susceptibilities. Finally, the biofunctions of toll like receptor 3 (TLR3) in pancreatic cancer (PC) cells were investigated through qPCR, MTT, colony formation, and Transwell assays. Overexpression of TLR3 could promote the proliferation, migration, and invasion of PC cells. In conclusion, PRGs play crucial roles in prognosis, progression, and immune microenvironment of PAAD. TLR3 is expected to be a promising therapeutic target.

IntroductionWith the ongoing advancement of bioinformatic technology, utilizing public transcriptome data to predict the clinical outcomes of cancer patients has become standard. A recent study by Ma B et al., titled ‘Immune cell infiltration and prognostic index in cervical cancer: insights from metabolism-related differential genes,’ served as an inspiration for us. This study introduced a novel metabolism-related (MR) model designed to enhance the prognostic and therapeutic evaluations of patients with cervical cancer (CC). The model demonstrated significant prognostic value and was closely linked to the levels of immune cell infiltration and the response to immunotherapy. Although the study was well-designed and analyzed, it warrants further enhancements in the selection of machine learning algorithms, comprehensive bioinformatic evaluations, and validation through extensive clinical cohorts.Selection of machine learning algorithmsCurrently, lasso regression is the most frequently used machine learning approach for clinical modeling (2). Similarly, the MR model was also reliant on lasso regression for construction (1). In 2024 alone, more than 800 studies have utilized this method to establish clinical assessment models in various cancers. The reasons for its wide application are predominantly due to that lasso regression effectively handles the dilemma of variable selection and model overfitting through creating a penalty function λ (3).At present, other machine learning algorithms in exception with lasso regression have also been widely applied in the clinical modeling process owing to their particular advantages. For instance, the support vector machine (SVM) is a classical supervised learning algorithm, which has expertise in addressing the issues referring to multi-classification and sample disequilibrium (4). Yu Y et al. have utilized this method to characterize heart failure (5). As for random forest, it has excelled when dealing with non-linear data and complex relational data (6), by which Zhang Y et al. constructed a prediction model for deep vein thrombosis in patients with digestive system tumors (7). Clearly, lasso regression is not the only option in clinical modeling. Notably, the original intention of modeling is to accurately predict survival outcome or therapeutic response of cancer patients, which can be evaluated by C-index or area under curve (AUC) value (8). This elicits a question: Does the models constructed by lasso regression have the best predictive performance, or is lasso regression the optimal solution for modeling?To address this point, Liu Z et al. demonstrated a remarkable research strategy, termed comparison of multiple machine learning algorithms (9). In their study, 101 kinds of prediction models were fitted based on various machine learning algorithms and their combinations, such as support vector machine-recursive feature elimination (SVM-RFE), random survival forest (RSF), and Ridge method. They then calculated the C-index of each model in all validation cohorts and found the optimal solution for immune lncRNA signature in colorectal cancer (CRC), which was the combination of lasso regression and step cox regression. Therefore, there may be better approaches for fitting the MR model, which awaits verification by comparing multiple machine learning algorithms.Nonetheless, the advantages of lasso regression over other algorithms should not be overlooked. First, the model fitted by lasso regression offers significant interpretability (10). In contrast, models such as random forest, SVM, XGBoost, and artificial neural network (ANN) often result in 'black box' models, which are challenging to interpret internally. This difficulty arises because the predictive results of black box models can typically only be explained by the correlations between input and output, without providing a specific interpreting or reasoning process (11). Furthermore, black box models are often nonlinear, making their decision boundaries more elusive than those of the linear models well-represented by lasso regression. Second, lasso regression is adept at addressing model overfitting and high model complexity. However, other algorithms may not always be competent. For example, when no multicollinearity exists between independent variables, Ridge regression may diminish the predictive performance of the constructed model (12). Similarly, the random forest model is susceptible to overfitting on certain noisy features, and its training time is relatively long due to its dependence on training multiple decision trees simultaneously. Therefore, although lasso regression may not always excel in diagnostic performance, it remains a stable solution with low fault tolerance for most clinical analyses.Comprehensive bioinformatics evaluationPrognostic analysis is a crucial component of personalized cancer medicine and represents a primary concern for patients. In their study, Ma B et al. evaluated the survival differences between patients with high and low MR risks and the accuracy of the MR model in predicting the overall survival rate (OSR) at 1, 3, and 5 years (1). However, other prognostic properties also merit further exploration. For instance, clinical subgroup analyses could test the predictive capacities of the MR model in cancer patients at different clinical stages (13). Additionally, while the AJCC and TNM systems are established foundations for cancer prognostic assessments (14), replacing them with the MR score is impractical. It could be clinically more significant to investigate whether the MR risk score could enhance the predictive accuracy or decision-making benefits of the AJCC or TNM systems using decision curve analysis (DCA) (15).Regarding immunotherapy response, Ma B et al. explored the associations between MR risk score and Tumor Immune Dysfunction and Exclusion (TIDE) score, tumor mutation burden (TMB), and expressions of immune checkpoints...