Comprehensive Analysis of m6A Regulators Identifies YTHDF3 as a Promising Prognostic Biomarker for Breast Cancer.

Integrative Multi-Omics Profiling Identifies Candidate Biomarkers in Triple-Negative Breast Cancer 2258118

This study integrates multi-omics datasets to identify candidate biomarkers that may influence TNBC progression and modulate therapeutic response and nominate CCND2 and KIF1A as candidate biomarkers and potential therapeutic targets in TNBC.

Expression patterns and prognostic value of N6-methyladenosine regulators in esophageal cancer

Introduction: Aberrant m6A regulators contribute to esophageal cancer, yet few studies stratify by subtype and compare prognostic models. Objectives: N6-methyladenosine (m6A) modification broadly regulates cancer progression and prognosis, while its clinical role in esophageal cancer (EC) remains poorly defined. Methods: Based on The Cancer Genome Atlas data, we analyzed 13 m6A regulators in EC and normal tissues, and constructed a prognostic signature using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Model performance was validated using C-index and time-dependent receiver operating characteristic curves and compared with tumor–node–metastasis (TNM) staging and existing models. Results: Eight m6A regulators (METTL3, KIAA1429, YTHDC1, HNRNPC, WTAP, RBM15, YTHDF1, YTHDF2) were significantly upregulated in EC tumors, with YTHDC1 showing the strongest correlation with METTL14 (r = 0.55). A four-gene m6A-regulator expression signature was identified using LASSO Cox regression. Both univariate and multivariate Cox analyses revealed that the risk score and TNM stage were independent risk factors. High-risk patients had worse overall survival (OS) across all stages and subtypes. Low-risk stage I–II patients had better OS. Combining the risk score with TNM stage improved prognostic accuracy over TNM stage alone, though differences with the risk score model were not significant. Our risk model was particularly effective at predicting 1-year OS, whereas TNM staging and other signatures were better at predicting 3- and 5-year survival. Conclusion: We explored the role of m6A in EC and created a four-gene m6A-regulator expression signature risk model that serves as an independent prognostic biomarker, complementing TNM staging to aid in personalized postoperative care for EC patients.

Integrated multi-omics analysis and functional experiments reveals PPAP2C as a potential prognostic biomarker and therapeutic target in breast cancer

Background This study aims to systematically elucidate the clinical significance and biological function of the phospholipid phosphatase (PLPP) family member (PPAP2C) phosphatidic acid phosphatase type 2C in breast cancer, and to evaluate its potential as a prognostic biomarker and therapeutic target. Methods Gene expression data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were integrated to characterize the expression profile of PLPP family members, focusing on PPAP2C in breast cancer. The prognostic value of PPAP2C, initially identified at the mRNA level (TCGA, (METABRIC) Molecular Taxonomy of Breast Cancer International Consortium, Gene Expression Omnibus (GEO)), was confirmed at the protein level by immunohistochemistry (IHC) on tissue microarrays (TMA). The oncogenic functions of PPAP2C were investigated in triple-negative breast cancer (TNBC) cells through CRISPR-Cas9-mediated knockout and ectopic overexpression, with assessment of key phenotypes including proliferation, colony formation, migration, and invasion. In vivo validation was subsequently performed using an MDA-MB-231 xenograft model. Results PPAP2C exhibits the most significant overexpression pattern across 33 cancer types (upregulated in 16 cancers, downregulated in only 3). Compared with normal tissues, PPAP2C showed specific overexpression in breast cancer tissues and was significantly associated with advanced clinical stages and aggressive subtypes (HER2+ and TNBC). Survival analysis demonstrated that high PPAP2C expression correlated with significantly shorter overall survival and disease-free survival, which was further validated in METABRIC and GEO cohorts. Tissue microarray analysis confirmed higher PPAP2C protein positivity in tumor tissues (94.7%) than in adjacent normal tissues (59.7%), with worse OS and RFS in high-expression groups. Multivariate analysis identified PPAP2C as an independent prognostic factor for OS. Functional experiments revealed that PPAP2C knockout (via 5-bp/1-bp frameshift mutations) suppressed TNBC cell proliferation, colony formation, migration, and invasion, while overexpression enhanced these phenotypes. In vivo studies further demonstrated complete tumor regression in MDA-MB-231 xenografts upon PPAP2C knockout. Conclusion This study identifies PPAP2C as a key oncogenic driver and a robust independent prognostic biomarker in breast cancer. The findings provide compelling evidence that PPAP2C represents a promising therapeutic target, offering a new strategic avenue for precision therapy, particularly for aggressive breast cancer subtypes.

ZCCHC10 as a novel prognostic biomarker associated with immune infiltration in breast cancer

CCHC zinc finger protein 10 (ZCCHC10) has been implicated in the progression of multiple malignancies, but its prognostic value and role in immune infiltration in breast cancer remain unclear. In this study, we systematically analyzed ZCCHC10 expression using public databases and validated the findings by immunohistochemistry. Associations with clinicopathological features and patient prognosis were assessed, and a prognostic nomogram was constructed. Co-expression networks and gene set enrichment analysis were used to explore functional pathways, while immune infiltration was evaluated and potential therapeutic agents were predicted. Functional assays, including proliferation and colony formation experiments, were performed to examine the biological role of ZCCHC10 in vitro. Our results demonstrated that ZCCHC10 was significantly upregulated in breast cancer and associated with poor prognosis and adverse clinicopathological characteristics. Enrichment analysis indicated involvement in immune-related pathways, and its expression correlated with immune cell infiltration and immunomodulators. Drug sensitivity prediction suggested Ipatasertib as a candidate targeted therapy requiring experimental verification. Knockdown of ZCCHC10 markedly inhibited breast cancer cell proliferation. Collectively, these findings suggest that ZCCHC10 serves as a prognostic biomarker and a key regulator of immune infiltration in breast cancer.

A pan-cancer analysis reveals the multifaceted role of PABPC1L in immune regulation and therapy response

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma

Abstract Background/Aim: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. Materials and Methods: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. Results: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. Conclusion: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.