Cigarette smoke modulates binding of the transcription factor MZF1 to the VEGF promoter and regulates VEGF expression in dependence of genetic variation SNP 405

Cigarette smoking combined with genetic variation regulates the m6A methylation of CRNKL1 and is associated with bladder cancer risk

Cigarette smoking was known to accelerate the occurrence and development of bladder cancer by regulating RNA modification. However, the association between the combination of cigarette smoking and RNA modification-related single nucleotide polymorphisms (RNAm-SNPs) and bladder cancer risk remains unclear. In this study, 1681 participants, including 580 cases and 1101 controls, were recruited for genetic association analysis. In total, 1 287 990 RNAm-SNPs involving nine RNA modifications (m6A, m1A, m6Am, 2'-O-Me, m5C, m7G, A-to-I, m5U, and pseudouridine modification) were obtained from the RMVar database. The interactive effect of cigarette smoking and RNAm-SNPs on bladder cancer risk was assessed through joint analysis. The susceptibility analysis revealed that 89 RNAm-SNPs involving m6A, m1A, and A-to-I modifications were associated with bladder cancer risk. Among them, m6A-related rs2273058 in CRNKL1 was associated with bladder cancer risk (odds ratios (OR) = 1.35, padj = 1.78 × 10-4), and CRNKL1 expression was increased in bladder cancer patients (p = 0.035). Cigarette smoking combined with the A allele of rs2273058 increased bladder cancer risk compared with nonsmokers with the G allele of rs2273058 (OR = 2.40, padj = 3.11 × 10-9). Mechanistically, the A allele of rs2273058 endowed CRNKL1 with an additional m6A motif, facilitating recognition by m6A reader IGF2BP1, thereby promoting CRNKL1 expression under cigarette smoking (r = 0.142, p = 0.017). Moreover, elevated CRNKL1 expression may accelerate cell cycle and proliferation, thereby increasing bladder cancer risk. In summary, our study demonstrated that cigarette smoking combined with RNAm-SNPs contributes to bladder cancer risk, which provides a potential target for bladder cancer prevention.

SCAND1 Reverses Epithelial-to-Mesenchymal Transition (EMT) and Suppresses Prostate Cancer Growth and Migration

Epithelial-mesenchymal transition (EMT) is a reversible cellular program that transiently places epithelial (E) cells into pseudo-mesenchymal (M) cell states. The malignant progression and resistance of many carcinomas depend on EMT activation, partial EMT, or hybrid E/M status in neoplastic cells. EMT is activated by tumor microenvironmental TGFβ signal and EMT-inducing transcription factors, such as ZEB1/2, in tumor cells. However, reverse EMT factors are less studied. We demonstrate that prostate epithelial transcription factor SCAND1 can reverse the cancer cell mesenchymal and hybrid E/M phenotypes to a more epithelial, less invasive status and inhibit their proliferation and migration in DU-145 prostate cancer cells. SCAND1 is a SCAN domain-containing protein and hetero-oligomerizes with SCAN-zinc finger transcription factors, such as MZF1, for accessing DNA and the transcriptional co-repression of target genes. We found that SCAND1 expression correlated with maintaining epithelial features, whereas the loss of SCAND1 was associated with mesenchymal phenotypes of tumor cells. SCAND1 and MZF1 were mutually inducible and coordinately included in chromatin with hetero-chromatin protein HP1γ. The overexpression of SCAND1 reversed hybrid E/M status into an epithelial phenotype with E-cadherin and β-catenin relocation. Consistently, the co-expression analysis in TCGA PanCancer Atlas revealed that SCAND1 and MZF1 expression was negatively correlated with EMT driver genes, including CTNNB1, ZEB1, ZEB2 and TGFBRs, in prostate adenocarcinoma specimens. In addition, SCAND1 overexpression suppressed tumor cell proliferation by reducing the MAP3K-MEK-ERK signaling pathway. Of note, in a mouse tumor xenograft model, SCAND1 overexpression significantly reduced Ki-67(+) and Vimentin(+) tumor cells and inhibited migration and lymph node metastasis of prostate cancer. Kaplan-Meier analysis showed high expression of SCAND1 and MZF1 to correlate with better prognoses in pancreatic cancer and head and neck cancers, although with poorer prognosis in kidney cancer. Overall, these data suggest that SCAND1 induces expression and coordinated heterochromatin-binding of MZF1 to reverse the hybrid E/M status into an epithelial phenotype and, inhibits tumor cell proliferation, migration, and metastasis, potentially by repressing the gene expression of EMT drivers and the MAP3K-MEK-ERK signaling pathway.

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

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.