Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.

Background: Cancer of unknown primary remains a formidable diagnostic challenge due to the inability to pinpoint the primary tumor site, which restricts the use of targeted therapeutics. Although machine‐learning methods that integrate transcriptomic approaches have provided valuable insights into tumor origins, they often face challenges in distinguishing biologically similar tumors and typically lack biological interpretability.Aims: This study aims to develop a transparent and biologically interpretable machine learning framework to accurately classify tissue‐of‐origin across diverse cancer types, thereby facilitation clinical diagnosis.Methods: We designed OncoTrace‐TOO, a novel tissue‐of‐origin classification model based on gene expression profiles. The model utilizes pan‐cancer discriminative molecular features identified through one‐vs‐rest differential expression analysis and applies logistic regression as the classification algorithm.Results: OncoTrace‐TOO achieved an overall accuracy of 0.967, with perfect classification for seven cancer types (e.g., CHOL, DLBC, and LAML). The model demonstrated high predictive accuracy in both primary and metastatic cancers across TCGA and GEO validation datasets, with enhanced capability in resolving histologically related malignancies as well as classifying rare cancer subtypes. When applied to independent clinical tumor samples, the model achieved TOO prediction accuracies of 0.857, further validating its robustness. Importantly, the framework offers biologically interpretable predictions by revealing tumor‐specific molecular signatures, thus enhancing its clinical applicability.Conclusions: OncoTrace‐TOO not only offers high predictive accuracy for tissue‐of‐origin classification, but also delivers biologically meaningful insights that support clinical decision‐making. This framework holds promise for improving diagnostic precision and guiding personalized treatment in challenging cancer cases.

Background: Anti-tuberculosis drug-induced liver injury (AT-DILI) complicates 2-28% of tuberculosis treatments, limiting therapy and occasionally causing acute liver failure. Inter-individual susceptibility remains incompletely explained by known genetic factors. Methods: We performed an integrated whole-exome sequencing (WES) analysis of an AT-DILI cohort, combining common variant association analysis, rare variant burden testing at the gene and pathway levels, and exploratory genotype–phenotype analyses. A matched sub-cohort was further analyzed for cis genetic–epigenetic associations between genetic variants and DNA methylation. Results: Rare variant burden analysis prioritized PTGS1 as a candidate susceptibility gene (P SKAT-O = 4.58 × 10 ^-4 , Enrichment Ratio = 3.461), with an increased burden of rare functional variants observed among AT-DILI cases. Pathway-level burden analyses further indicated enrichment of biological processes related to xenobiotic metabolism, immune regulation, and cellular stress responses, including regulated cell death. A cis genetic–epigenetic association analysis in a matched sub-cohort (n = 28) identified a nominal association at the GPR132 locus, suggesting a potential relationship between genetic variation and DNA methylation. Furthermore, genotype–phenotype analyses showed that rare variant burden in S100A2 displayed nominal positive correlations with peak alanine aminotransferase (ALT) levels. Conclusion: These findings provide an integrated characterization of candidate rare variant signals and associated biological processes in AT-DILI. The results prioritize PTGS1 and other candidate loci for further investigation and expand our understanding of potential genetic contributors to inter-individual variability in anti-tuberculosis treatment outcomes.