The risk score established by m6A and autophagy can be used to predict prognosis and susceptibility to anticancer drugs in patients with ovarian cancer.
Abstract
The most prevalent RNA alteration in the mammalian genome is N-6-methylenediosine (m6A). There is mounting evidence linking dysregulation of m6A regulatory factors and alterations in m6A levels to the development, course, or prognosis of ovarian cancer. Genes having prognostic value were screened using the univariate, multifactorial, and Least Absolute Shrinkage Selection Operator (LASSO) Cox regression analyses. Important genes' m6A expression in clinical material was verified by real-time fluorescent quantitative polymerase chain reaction (RT-qPCR). In present study, all 23 regulators were significantly differentially expressed in ovarian cancer tissues. LASSO regression analysis screened for 10 key genes associ-ated with both autophagy and m6A. A risk score was constructed and nomogram was developed to forecast the prognosis of ovarian cancer patients. Additionally, individuals with ovarian cancer were classified as high-risk or low-risk; and the low-risk group might be more likely to benefit from im-munotherapy. RT-qPCR was used for the bioinformatics study of human ovarian cancer and normal tissues. Lastly, PLK2 and LEPR were confirmed to be associated with tumorigenesis in scRNA-seq. The risk score established by m6A and autophagy can be used to predict prognosis and susceptibility to anticancer drugs in patients with ovarian cancer.
The expression landscape and prognostic value of N6-methyladenosine (m6A)-related genes remain largely uncharacterized in breast cancer. Here, we performed an integrative analysis of their expression profiles and clinical relevance. Utilizing multi-omics datasets and experimental validation, we conducted a systematic investigation of m6A-related genes in breast cancer. These genes exhibited pronounced differential expression in breast cancer, yet their correlation with molecular features such as promoter methylation and copy number alterations was limited. Univariate survival analysis indicated that aberrant expression of RBM15B, METTL16, HNRNPC, YTHDF1, YTHDF3, and IGF2BP1 was significantly linked to patient prognosis. Multivariate Cox regression further identified elevated YTHDF3 expression as an independent prognostic factor. Functional network analysis indicated YTHDF3 is potentially involved not only in RNA processing and metabolism but also in DNA repair, pri-miRNA processing, telomere stability, and immune infiltration. Moreover, upregulation of YTHDF3 mRNA was confirmed in clinical breast cancer specimens. Collectively, m6A-related genes are dysregulated in breast cancer and correlate with patient outcomes, highlighting their biomarker potential, with YTHDF3 warranting in-depth investigation.
Pu Jin, Yue-Tsz Fan· Journal of Visualized Experi...· 0 citations
BACKGROUND
Multiple myeloma (MM) remains an incurable hematological malignancy with heterogeneous outcomes, necessitating the identification of novel prognostic markers for improved patient stratification and treatment.
METHODS
This study investigated the prognostic significance of disulfidptosis-related genes (DRGs) in MM, analyzing 1,272 samples from TCGA_MMRF and GSE4581 datasets. To explore the biological functions of key DRGs, si-RNA was used to conduct in vitro experimental verification.
RESULTS
Through comprehensive bioinformatic analyses, we identified four genes (FLNA, GYS1, LRPPRC, and MYH10) significantly associated with MM prognosis. These DRGs linked the signature to disulfidptosis biology in MM. A novel four-gene signature effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes, remained an independent prognostic factor, and varied significantly across disease stages. High-risk scores were associated with pathways involving mRNA processing, DNA replication, and cell cycle progression, as well as immune shifts in neutrophil and plasma-cell fractions. Drug-sensitivity modeling showed distinct IC50 patterns between risk groups, with LRPPRC and GYS1 negatively correlated with sensitivity to selected agents. In vitro, LRPPRC knockdown in RPMI 8226 and U266 reduced viability and colony formation while increasing apoptosis, supporting the biological relevance of the signature.
CONCLUSION
This novel DRGs signature not only provides a valuable tool for patient stratification but also offers insights into the biological processes driving MM progression and potential therapeutic targets.
Yuxiu Xu, Xiaoli Sun, Jing Yang et al.· Acta Haematologica· 0 citations
Based on emerging evidence implicating branched-chain aminotransferase 1 (BCAT1) in tumorigenesis from animal and cellular studies, this study aimed to systematically evaluate its oncogenic role through a pan-cancer analysis to address the lack of comprehensive pan-cancer investigations in the literature. Using datasets from The Cancer Genome Atlas and the Genotype-Tissue Expression project, we analyzed BCAT1 expression, prognosis, genetic alterations, immune infiltration, and functional enrichment across 33 cancer types. The primary endpoints were overall survival (OS) and disease-free survival (DFS). Statistical significance was primarily defined as P < .05. For analyses involving multiple correlations across cancer and immune cell types, the false discovery rate < 0.05 was applied. BCAT1 was significantly upregulated in most malignancies, and high expression was associated with worse OS and DFS in multiple cancer types. BCAT1 expression correlated with reduced CD8+ T cell infiltration and increased cancer-associated fibroblast accumulation across several tumors. Enrichment analyses suggested BCAT1 involvement in RNA metabolism and intracellular protein processing. BCAT1 was significantly upregulated in most malignancies, and high expression was associated with worse OS and DFS in multiple cancer types, including adrenocortical carcinoma (hazard ratio = 6.3) and lower-grade glioma (hazard ratio = 2.6) (all log-rank P < .05), highlighting its potential as a pan-cancer biomarker and therapeutic target. The present findings are associative and require further experimental and external validation.
BACKGROUND
Cervical cancer (CESC) is a major gynecological malignancy, and abnormalities in phosphoinositide metabolism (PPM) are closely linked to tumor progression. This study aimed to screen phosphoinositide metabolism-related genes (PPM-RGs) with prognostic value for CESC and explore their molecular mechanisms and clinical significance.
METHODS
CESC transcriptomic datasets and PPM-RGs were retrieved from public databases. Differentially expressed genes were intersected with PPM-RGs to obtain candidate genes. Prognostic genes were screened via univariate Cox regression and proportional hazards assumption test, and a risk model was constructed using random survival forest (RSF). Independent prognostic analysis and nomogram establishment were performed, followed by pathway enrichment, immune infiltration, somatic mutation and single-cell analyses to characterize the tumor microenvironment. Immunohistochemical staining was used to verify the protein expression of key genes in CESC tissues.
RESULTS
Twenty candidate genes were initially identified, and two protective prognostic genes VAC14 and INPP5K were finally determined. The risk model showed favorable predictive performance with area under the curve (AUC) over 0.7, and high-risk patients had poorer survival. The nomogram combining risk score and M stage presented high accuracy. The high-risk group exhibited distinct pathway enrichment, altered immune infiltration and low somatic mutation rates. Monocytes were identified as potential key cells with impaired communication and differentiation in CESC. Immunohistochemistry confirmed downregulated expression of the two genes in CESC epithelial cells.
CONCLUSION
VAC14 and INPP5K were PPM-related prognostic genes for CESC. Monocytes played critical roles in CESC progression. The risk model and nomogram aided prognosis prediction, providing new insights for CESC treatment.
H. Meng, Li Tan, Ying Chen et al.· BMC Cancer· 0 citations
BACKGROUND
Immunogenic cell death (ICD) is closely linked to thyroid cancer (THCA). We mined ICD-linked prognostic genes for survival predictors and unpacked their potential biology.
MATERIALS
Single-cell analysis was conducted to identify distinct cell types, and the scores of ICD-related genes (ICDRGs) were computed to identify key cell types. Furthermore, correlation, differential expression, and Kaplan-Meier survival studies were conducted to discover prognostic genes. By employing these genes alongside 101 combinations of 10 algorithms, we constructed a prognostic model and computed risk scores for THCA samples, analyzing their survival disparities. Control and THCA tissue samples were taken to assess the expression of prognostic genes via real-time reverse transcriptase-polymerase chain reaction (RT-qPCR), resulting in the identification of a key gene. Subsequently, gene set enrichment analysis was executed on this key gene, and Western blot analysis was undertaken to evaluate the expression of relevant pathways.
RESULTS
Single-cell and transcriptome analysis found CXCR6, ICOS, SLAMF7, CD80, and TNF as prognostic genes associated with ICD in THCA. Of the 101 combinations evaluated, the StepCox[both] + Random Survival Forest (RSF) model was selected as the prognostic model owing to its superior C-index. This model accurately forecasted survival outcomes for THCA samples. Expression study demonstrated a notable decrease of CXCR6 in THCA samples, consistent with bioinformatics analysis and it was designated as the key gene. Functional analysis revealed an enrichment of the inflammatory response, accompanied by reduced levels of its principal targets (Gαi) and downstream pathways (Src and PI3K-AKT) in THCA samples.
CONCLUSION
This study found five prognostic genes, with CXCR6 associated with Gαi, Src, and PI3K-AKT pathways in the modulation of THCA, indicating the essential function of CXCR6 in THCA.
Yiting Yao, Linlang Huang, R. Xia et al.· BMC Cancer· 0 citations
The findings lay the groundwork for precision oncology paradigms, facilitating the translational trajectory of these targets to optimize clinical prognosis, and underscores the potential of UMARGs in advancing HCC treatment strategies and improving patient outcomes.