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Hypothyroidism reduces the risk of lung cancer through oxidative stress response and the PI3K/Akt signaling pathway: An RNA-seq and Mendelian randomization study.
Hypothyroidism has been suggested to play a role in tumor progression. However, the causal association between hypothyroidism and lung cancer remains unknow. To elucidate the potential association between hypothyroidism and lung cancer risk, we employ a Mendelian randomization (MR) approach. MR was performed to analyze pooled data from the International Lung Cancer Consortium (11,348 cases and 15,861 controls; European ancestry) to determine the causal relationship between hypothyroidism and lung cancer. We used 36, 83, and 14 single nucleotide polymorphisms as instrumental variables for hypothyroidism/myxoedema, hypothyroidism, and exercise, respectively. We further investigated the mechanisms involved in transcriptome analysis using data from The Cancer Genome Atlas and Genotype-Tissue Expression database. We conducted an initial validation of intermediary factor using a two-step MR analysis. Genetically predicted hypothyroidism was significantly related to the risk of overall lung cancer, specifically the risk of lung squamous cell cancer (LSCC) but not with the risk of lung adenocarcinoma (LUAD) as assessed using the inverse-variance weighted (IVM) method. A similar causal association was found between hypothyroidism/myxoedema and the risk of lung cancer, LSCC, and LUAD. Transcriptome analysis showed that genes associated with hypothyroidism, lung cancer, and LSCC were enriched in the PI3K/Akt signaling pathway and oxidative stress response. However, genes related to hypothyroidism and LUAD did not exhibit enrichment in these pathways. Hypothyroidism was significantly associated with strenuous sports or other exercises. Moreover, genetically predicted exercise was significantly related to the risk of overall lung cancer, and LSCC, but not LUAD. We detected no horizontal pleiotropy using the MR-PRESSO and MR Egger regression intercept. Hypothyroidism was causally associated with a lower risk of lung cancer, and these effects might be mediated by the oxidative stress response and the PI3K/Akt signaling pathway. Therefore, our study suggests that the potential factors and viable etiologies of hypothyroidism that contributed to lung cancer risk deserve further investigation.
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iSoMAs: Finding isoform expression and somatic mutation associations in human cancers.
Aberrant alternative splicing, prevalent in cancer, impacts various cancer hallmarks involving proliferation, angiogenesis, and invasion. Splicing disruption often results from somatic point mutations rewiring functional pathways to support cancer cell survival. We introduce iSoMAs (iSoform expression and somatic Mutation Association), an efficient computational pipeline leveraging principal component analysis technique, to explore how somatic mutations influence transcriptome-wide gene expression at the isoform level. Applying iSoMAs to 33 cancer types comprising 9,738 tumor samples in The Cancer Genome Atlas, we identified 908 somatically mutated genes significantly associated with altered isoform expression across three or more cancer types. Mutations linked to differential isoform expression occurred through both cis- and trans-acting mechanisms, involving well-known oncogenes/suppressor genes, RNA binding protein and splicing factor genes. With wet-lab experiments, we verified direct association between TP53 mutations and differential isoform expression in cell cycle genes. Additional iSoMAs genes have been validated in the literature with independent cohorts and/or methods. Despite the complexity of cancer, iSoMAs attains computational efficiency via dimension reduction strategy and reveals critical associations between regulatory factors and transcriptional landscapes.
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Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma.
Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes. SIGNIFICANCE: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.
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SUMOylation substrate encoding genes as prognostic biomarkers in pancreatic ductal adenocarcinoma with functional assessment of SAF-B2.
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is highly malignant with a poor prognosis, posing significant clinical challenges. SUMOylation, a reversible post-translational modification, plays a critical role in tumor progression, yet its prognostic significance in PDAC remains unclear. METHODS: We assessed SUMOylation expression patterns and function in PDAC using Western blot and the SUMOylation inhibitor TAK-981. Differentially expressed SUMOylation substrate encoding genes (DE-SSEGs) were identified from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) datasets. A SUMOylation-based prognostic model, Sscore, was constructed using LASSO and Cox regression. Additional analyses included somatic mutation, immune infiltration, TIDE, drug sensitivity, and single-cell RNA sequencing. The role of SAFB2 in PDAC was validated in vitro. RESULTS: PDAC cells showed elevated SUMOylation, and its inhibition reduced cell proliferation. The Sscore model, based on DE-SSEGs (CDK1, AHNAK2, SAFB2), predicted overall survival and correlated with genome variation, immune infiltration, and drug sensitivity. Single-cell analysis further confirmed a link between high Sscore and malignancy. SAFB2, identified as a pivotal gene within the Sscore model, was significantly downregulated in PDAC tissues and cell lines; its overexpression was shown to inhibit PDAC cell proliferation, migration, and invasion by suppressing the Wnt/β-Catenin signaling pathway. CONCLUSION: This study underscores the role of SUMOylation in PDAC and introduces the Sscore as a prognostic tool. SAFB2 is identified as a potential tumor suppressor, offering new therapeutic targets for PDAC.
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Construction and Validation of a Prognostic Model Based on Novel Senescence-Related Genes in Non-Small Cell Lung Cancer Patients with Drug Sensitivity and Tumor Microenvironment.
Cellular senescence contributes to cancer pathogenesis and immune regulation. Using the LASSO Cox regression, we developed a 12-gene prognostic signature for lung adenocarcinoma (LUAD) from The Cancer Genome Atlas (TCGA) and a Gene Expression Omnibus (GEO) dataset. We assessed gene expression, drug sensitivity, immune infiltration, and conducted cell line experiments. High-risk LUAD patients showed increased mortality risk and shorter survival (P < 0.001). Senescence-related gene analysis indicated differences in protein phosphorylation and DNA methylation between normal individuals and LUAD patients. The high-risk group showed a positive association with PD-L1 expression (P = 0.003). Single-cell sequencing data suggested PEBP1 might significantly impact T cell infiltration. We predicted potential sensitive compounds for 12 senescence genes and found GAPDH promoted cell line proliferation. We established a novel prognostic system based on a newly identified senescence gene. High-risk patients had elevated immunosuppressive markers, and PEBP1 might influence T cell infiltration significantly. GAPDH, expressed at higher levels in tumors, could affect cancer progression. Our drug prediction model may guide treatment selection.
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TGF-β-mediated activation of fibroblasts in cervical cancer: implications for tumor microenvironment and prognosis.
BACKGROUND: Cervical cancer (CC) is a prevalent female malignancy strongly influenced by the tumor microenvironment (TME). This study focuses on the role of TGF-β signaling in cancer-associated fibroblasts (CAFs) and its interaction with immune cells, aiming to elucidate its impact on CC progression. METHODS: The TME of CC patients was analyzed using scRNA-seq data and we identified the major cell types in the TME with a focus on the activation of the TGF-β signaling pathway in fibroblasts. Gene modules related to the TGF-β signaling pathway were identified by Weighted correlation network analysis (WGCNA). Using The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) dataset, a prognostic gene model was constructed by univariate Cox, LASSO Cox and multivariate Cox regression analyses. For cellular validation, the mRNA level of prognostic model-related genes was tested via quantitative real-time real-time polymerase chain reaction (PCR). Thereafter, the following assays, including cell counting kit-8, scratch and wound healing assays, were applied to assess the viability, migration and invasion of CC cells. RESULTS: Analysis at single-cell resolution identified nine major cell types in the TME, and significant activation of the TGF-β signaling pathway in fibroblasts was correlated with tumor proliferation and differentiation. Strong TGF-β signaling communication between fibroblasts and macrophages and NK/T cells suggested a crucial role in the shaping of the immunosuppressive microenvironment. WGCNA analysis identified gene modules significantly associated with the TGF-β signaling pathway. The prognostic model constructed based on three genes, ITGA5, SHF and SNRPN, demonstrated good predictive ability in multiple datasets, validating its potential for clinical application. Meanwhile, the cellular validation assays have revealed the higher expression of ITGA5 and SNRPN and lower expression of SHF in CC cells. Further, ITGA5 knockdown suppressed the viability, migration and invasion of CC cells. CONCLUSION: This study confirmed the important role of the TGF-β signaling pathway in CC, especially in fibroblasts on tumor microenvironment and tumor progression. The current model could effectively evaluate the prognosis of CC, providing a theoretical foundation for developing CC therapies according to the TGF-β signaling pathway. The present results provide new perspectives for further research on the pathological mechanisms and clinical management of CC.
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EMP1 as a Potential Biomarker in Liver Fibrosis: A Bioinformatics Analysis.
Liver fibrosis is a wound-healing response to chronic injury, which may result in cirrhosis and liver failure. Studies have been carried on the mechanisms and pathogenesis of liver fibrosis. However, the potential cell-specific expressed marker genes involved in fibrotic processes remain unknown. In this study, we combined a publicly accessible single-cell transcriptome of human liver with microarray datasets to evaluate the cell-specific expression patterns of differentially expressed genes in the liver. We noticed that EMP1 (epithelial membrane protein 1) is significantly active not only in CCl4 (carbon tetrachloride)-treated mouse liver fibrosis but also in BDL (bile duct ligation)-induced liver fibrosis and even in human fibrotic liver tissues such as alcoholic hepatitis, NASH (nonalcoholic steatohepatitis), and advanced stage liver fibrosis. Furthermore, we demonstrated that EMP1 is a specific fibrotic gene expressed in HSCs (hepatic stellate cells) and endothelial cells using the Protein Atlas single-cell transcriptome RNA-sequencing clustering. Its expression was significantly elevated in fibrotic HSCs or CCl4 and NASH-induced fibroblasts. Previous research revealed that EMP1 plays a role in proliferation, migration, metastasis, and tumorigeneses in different cancers via a variety of mechanisms. Because HSC activation and proliferation are two important steps following liver injury, it would be interesting to investigate the role of EMP1 in these processes. All of this information suggested that EMP1 could be used as a novel fibrotic liver marker and a possible target in the future.
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Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma.
Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.
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T Cell Exhaustion-Related Gene Signatures Predict Immunotherapy and Chemotherapy Response in Kidney Renal Clear Cell Carcinoma.
Background: Understanding T cell exhaustion (TEX)-related molecular characteristics can provide novel insights into treatment response prediction. This study developed a TEX-based prognostic model to predict survival outcomes and therapy responses in kidney renal clear cell carcinoma (KIRC) patients. Methods: The authors analyzed 518 KIRC patients from The cancer genome atlas (TCGA), identifying TEX-related genes via gene set variation analysis and weighted correlation network analysis. Survival random forest and Least Absolute Shrinkage and Selection Operator-Cox analyses selected eight key genes to construct a TEX risk model. Functional analyses explored TEX-related pathways and immune infiltration. The IMvigor210 dataset assessed immunotherapy response, whereas the Genomics of Drug Sensitivity in Cancer (GDSC) database predicted chemotherapy sensitivity. Single-cell RNA sequencing and quantitative real-time polymerase chain reaction validated a key TEX gene. Results: The TEX risk model demonstrated strong prognostic performance, effectively stratifying KIRC patients into high-risk (HR) and low-risk (LR) groups with significant differences in overall survival. Gene set enrichment analysis results revealed that TEX-related pathways were enriched in tumor proliferation, migration, and immune regulation. Immune cell infiltration analysis indicated that the TEX HR group exhibited distinct immune microenvironment characteristics, including increased expression of specific immune checkpoints. The model effectively predicted clinical responses to immunotherapy, with patients in the TEX HR group showing poorer immunotherapy efficacy. In addition, drug sensitivity analysis based on the GDSC database suggested that TEX features could influence chemotherapy response, highlighting potential therapeutic vulnerabilities. Experimental validation confirmed the expression pattern of a key TEX gene in KIRC samples. Conclusion: Their TEX risk model could effectively predict patient outcomes and responses to immunotherapy and chemotherapy, supporting its potential clinical utility in personalized treatment strategies.
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G-protein Coupled Receptor 34 Promotes Gliomagenesis by Inducing Proliferation and Malignant Phenotype via TGF-Beta/Smad Signaling Pathway.
Background: G-protein coupled receptor 34 (GPR34) is involved in cell motility, differentiation, and mitosis. GPR34 was reported to be highly expressed and play an oncogenic role in several solid tumors. Here, we investigated the mechanisms underlying how GPR34 promotes glioma progression. Methods: Bioinformatic analysis was performed on RNA-seq and clinical data from the gene expression omnibus (GEO), cancer genome atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. TIMER database and single-sample GSEA (ssGAEA) method were used to investigate the association between the GPR34 expression and immune infiltration level in glioma. Cox regression analysis was employed to ascertain whether the risk signature was an independent prognostic indicator for glioma. The viability and migratory/invasive potential of glioma cells were assessed using Cell Counting Kit-8, colony formation, wound healing, and Transwell assays. Results: We found that GPR34 expression was positively correlated with immune infiltration level and that high GPR34 level may be associated with poor prognosis in glioma. We further found that GPR34 may serve as an independent prognostic marker and prediction factor for the clinicopathological features of glioma. We showed that knocking down GPR34 attenuated the viability and migratory/invasive capacity of glioma cells (U251 and LN229), while GPR34 overexpression exerted the opposite effects. Additionally, core enrichment in the GSEA analysis indicated that GPR34-mediated gliomagenesis was associated with the cell cycle arrest, epithelial-mesenchymal transition (EMT), and activation of the TGF-β/Smad pathway; furthermore, inhibiting TGF-β/Smad signaling using LY2157299, a TGF-β inhibitor, reversed the oncogenic effects and malignant phenotype associated with GPR34 overexpression. Conclusion: GPR34 enhances the malignancy and carcinogenesis of glioma by promoting an EMT-like process, G1/S phase cell cycle transition, and TGF-β/Smad signaling. Accordingly, GPR34 likely functions as an oncogene in glioma and may represent a potential therapeutic target for this cancer.