Hu Zhang

Gut–Vaginal Microbiome Crosstalk in Ovarian Cancer: Implications for Early Diagnosis

Ovarian cancer remains a formidable global health burden, characterized by frequent late-stage diagnosis and elevated mortality rates attributable to its elusive pathogenesis and the critical lack of reliable early-detection biomarkers. Emerging investigations into the gut–vaginal microbiome axis have unveiled novel pathogenic mechanisms and potential diagnostic targets in ovarian carcinogenesis. This comprehensive review systematically examines the compositional alterations in and functional interplay between vaginal and intestinal microbial communities in ovarian cancer patients. We elucidate three principal mechanistic pathways through which microbial dysbiosis may drive oncogenesis: (1) estrogen-mediated metabolic reprogramming via β-glucuronidase activity; (2) chronic activation of pro-inflammatory cascades (particularly NF-κB and STAT3 signaling); (3) epigenetic silencing of tumor suppressor genes through DNA methyltransferase modulation. We propose an integrative diagnostic framework synthesizing multi-omics data—incorporating microbial profiles, metabolic signatures, pathway-specific molecular alterations, established clinical biomarkers, and imaging findings—within a multifactorial etiological paradigm. This innovative approach aims to enhance early-detection accuracy through machine learning-enabled multidimensional pattern recognition. By bridging microbial ecology with tumor biology, this review provides novel perspectives for understanding ovarian cancer etiology and advancing precision oncology strategies through microbiome-targeted diagnostic innovations.

m6A‐related long noncoding RNAs predict prognosis and indicate therapeutic response in endometrial carcinoma

AbstractBackgroundN6‐methyladenosine (m6A) has been identified as the most common, abundant, and conserved internal transcriptional modification. Long noncoding RNAs (lncRNAs) are noncoding RNAs consisting of more than 200 nucleotides, and the expression of various lncRNAs may affect cancer prognosis. The impact of m6A‐associated lncRNAs on uterine corpus endometrial carcinoma (UCEC) prognosis is unknown.MethodsIn this study, UCEC prognosis‐related m6A lncRNAs were screened, bioinformatics analysis was performed, and experimental validation was conducted. Endometrial carcinoma (EC) and normal tissue samples were obtained from The Cancer Genome Atlas. The prognosis‐related m6A lncRNAs screened by the least absolute shrinkage and selection operator method were used for multivariate Cox proportional risk regression modeling. Principal component analysis and Gene Ontology, immune function difference, and drug sensitivity analyses of the prognostic models were performed. Prognostic analysis was conducted for m6A‐associated lncRNAs. The immune infiltration relationship of m6A‐associated lncRNAs in EC was identified using the ssGSEA immune infiltration algorithm. A competing endogenouse RNA network was constructed using the LncACTdb database. Finally, quantitative real‐time polymerase chain reaction (qRT‐PCR) assays were used to validate the differences in m6A‐related lncRNA expression in normal and EC cells.ResultsCDKN2B‐AS1 and MIR924HG were found to be risk factors for EC. RAB11B‐AS1 was a protective factor in EC patients. MIR924HG expression was upregulated in KLE and RL95‐2 endometrial cancer cell lines. Prognostic models involved RAB11B‐AS1, LINC01812, HM13‐IT1, TPM1‐AS, SLC16A1‐AS1, LINC01936, and CDKN2B‐AS1. The high‐risk group was more sensitive to five compounds (ABT.263, ABT.888, AP.24534, ATRA, and AZD.0530) than the low‐risk group.ConclusionThese findings contribute to understanding of the function of m6A‐related lncRNAs in UCEC and provide promising therapeutic strategies for UCEC.

[Retracted] Ferroptosis‐Related lncRNA for the Establishment of Novel Prognostic Signature and Therapeutic Response Prediction to Endometrial Carcinoma

Background. Ferroptosis is a recently described form of intentional cellular damage that is iron‐dependent and separate from apoptosis, cellular necrosis, and autophagy. It has been demonstrated to be adequately regulated by long noncoding RNAs (lncRNAs) in various cancers. However, the predictive profile of ferroptosis‐related lncRNAs (FRLs) in endometrial carcinoma (EC) is unknown. Herein, FRLs associated with uterine corpus endometrial carcinoma (UCEC) prognosis were screened to predict treatment response in EC. Methods. Samples of EC and adjacent normal tissues were obtained from The Cancer Genome Atlas (TCGA) dataset repository. Limma and survival packages in R software were used to screen FRLs associated with the prognosis of EC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) chord and circle plots of FRLs were also plotted. Next, FRLs screened by the least absolute shrinkage and selection operator (LASSO) method were applied to construct and validate a multivariate Cox proportional risk regression model. Nomogram plots were created to forecast the outcome of UCEC patients, and gene set enrichment analysis (GSEA), principal component analysis (PCA), and immunoassays were performed on the prognostic models. Finally, limma, ggpubr, pRRophetic, and ggplot2 programs were used for drug sensitivity analysis of the prognostic models. Results. A signature based on nine FRLs (CFAP58‐DT, LINC00443, EMSLR, HYI‐AS1, ADIRF‐AS1, LINC02474, CDKN2B‐AS1, LINC01629, and LINC00942) was constructed. The developed FRL prognostic model effectively discriminated UCEC patients into low‐risk and high‐risk groups. Immunological checkpoints CD80 and CD40 were strongly expressed in the high‐risk group. In addition, the nine FRLs were all more expressed in the high‐risk group compared to the low‐risk group. Conclusion. These findings significantly contribute to the understanding of the function of FRLs in UCEC and provide promising therapeutic strategies for UCEC.