Genes & genomics最新文献

Background: Muscle-invasive bladder cancer (MIBC) is a clinically aggressive and heterogeneous disease with variable treatment responses. Transcriptome-based classifications, such as the Chemoresistance-Motility (CrM) signature, are valuable for understanding therapeutic resistance, but their clinical use is often hindered by high cost and tissue requirements. This study explores an alternative, scalable approach using deep learning analysis of whole slide images (WSIs).

Objective: We aimed to evaluate whether patch-level predictions from deep learning models applied to WSIs can accurately predict transcriptome-derived CrM subtypes and reflect tumor microenvironment (TME) characteristics in MIBC.

Methods: We analyzed 192 WSIs from 152 TCGA-BLCA patients. A pretrained deep learning model (densenet169-kather100k) was used to classify eight distinct tissue types per patch. A key histological metric, the SAM-to-DNT ratio, which represents the ratio of stromal, adipose, and smooth muscle (SAM) tissue types to debris, normal, and tumor epithelium (DNT) tissue types, was derived from these proportions. A random forest model was then trained on these features to predict CrM subtypes.

Results: The WSI-derived SAM-to-DNT ratio showed a strong positive correlation with the CrM score (R = 0.453) and transcriptome-based TME scores, such as cancer-associated fibroblasts (R = 0.398). Our random forest model successfully classified CrM subtypes with a balanced accuracy of 0.75, outperforming other algorithms. Feature importance analysis identified adipose tissue (ADI) and tumor epithelium (TUM) as the most predictive features for CrM status.

Conclusions: Deep learning analysis of routine histological WSIs can serve as a practical, low-cost surrogate for molecular profiling, effectively capturing transcriptomic subtypes associated with chemoresistance in MIBC. This approach provides a viable method for patient stratification and establishes a foundation for future multi-modal precision oncology applications.

Background: Glioblastoma (GBM) is a highly aggressive primary brain tumor associated with poor prognosis and enhanced aerobic glycolysis (the Warburg effect), which facilitates rapid tumor growth and increases malignancy. Phosphofructokinase 2 (PFK2), also referred to as 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB), is a critical regulator of glycolysis and has been linked to tumorigenesis; however, the specific contributions of its isoforms to GBM pathobiology remain insufficiently defined.

Objective: This study aimed to clarify the isoform-specific function of PFKFB4 in GBM by evaluating its expression pattern, clinical significance, and impact on tumor glycolytic flux, proliferation, and malignancy, as well as to investigate the therapeutic potential of selectively targeting PFKFB4.

Methods: We examined the expression profiles of PFK2 isoforms in GBM using publicly accessible datasets. The clinical relevance was determined by conducting survival analysis using The Cancer Genome Atlas (TCGA) database. Protein expression patterns were identified with the Human Protein Atlas. PFKFB4 expression in GBM cell lines was validated using quantitative real-time PCR and immunoblotting. Functional roles of PFKFB4 were further assessed by employing the selective inhibitor 5-(n-(8-methoxy-4-quinolyl)amino)pentyl nitrate (5MPN). Effects of 5MPN on cell viability, proliferation, glucose uptake, lactate production, migration, invasion, and protein expression (c-Myc, cyclin D1, MMP-2, MMP-9) were evaluated using WST-8 assay, colony formation, 2-NBDG uptake, lactate assay, wound healing, transwell assays, and immunoblotting.

Results: We found that PFK2 isoforms-notably PFKFB4-are markedly overexpressed in GBM tissues and cell lines, with elevated PFKFB4 expression associated with worse patient prognosis. Pharmacological inhibition of PFKFB4 using the selective small-molecule inhibitor 5MPN reduced the Warburg effect, cell proliferation, and colony formation in GBM cells, while having minimal effects on normal human astrocytes. Additionally, PFKFB4 inhibition diminished GBM cell migration and invasion, coinciding with a reduction in the expression of matrix metalloproteinases MMP-2 and MMP-9.

Conclusion: These results underscore the oncogenic role of PFKFB4 in enhancing the Warburg effect and promoting malignant phenotypes in GBM, and support its potential as a therapeutic target for GBM treatment.

Background: Lung cancer (LC) is the leading cause of cancer-related deaths globally. Genetic variants in mismatch repair (MMR) genes, such as MutS homolog 2 (MSH2), MutS homolog 6 (MSH6) and MutL homolog 1 (MLH1), may influence individual susceptibility and clinical outcomes in LC.

Objective: This study investigated the associations of genetic polymorphisms in MSH2, MSH6, and MLH1 with susceptibility and survival outcomes in lung cancer patients in the Guangxi Zhuang population.

Methods: This study included a cohort of 192 LC patients and 262 healthy controls. The association of MSH2, MSH6, and MLH1 polymorphisms with susceptibility to small cell lung cancer (SCLC), lung adenocarcinoma (LUAD), and lung squamous carcinoma (LUSC) was evaluated using case-control methods, and protein expression differences were analysed by immunohistochemistry. The genotypes of genetic variations were detected using high-throughput SNP-scan technology. The Kaplan-Meier survival curve and log-rank test were used to assess the influence of individual genetic variants on prognostic outcomes in lung cancer patients.

Results: Multivariate logistic regression identified significant associations of rs2303428 and rs1042821 with increased lung cancer risk, especially in SCLC and LUSC. The GA and GG genotypes of rs1042821 were linked to higher SCLC risk (OR = 4.415 and 2.685; P = 0.019), the TC genotype of rs2303428 was associated with elevated LUSC risk (OR = 3.993; P = 0.034). No associations were found for rs1800734 or in LUAD. Immunohistochemistry showed increased MSH2 and MSH6 expression in risk genotypes, with no genotype-related changes in MLH1. In LUAD, the CC genotype of rs2300789 was associated with poorer overall survival compared to TC (P = 0.047), with no significant differences in other comparisons.

Conclusion: rs2303428 and rs1042821 polymorphisms were associated with increased lung cancer susceptibility and altered protein expression. Additionally, the CC genotype of rs2300789 was linked to poorer overall survival in LUAD.

Background: Colorectal cancer (CRC) is a common gastrointestinal tumor with a high incidence worldwide. L1 cell adhesion molecule (L1CAM) is highly expressed in CRC tissues and can promote tumor growth and metastasis. Autophagy regulates oncogenesis and tumor progression through context-dependent mechanisms and is regulated by multiple signaling pathways, including the p38 MAPK signaling pathway.

Objective: However, the relationship between L1CAM and autophagy is not known yet. In this study, we are trying to dissect the role of L1CAM-autophagy interaction in the development and progression of CRC and its underlying mechanism.

Methods: Stable L1CAM knockdown was achieved in SW480 and HT29 cells via lentiviral transduction of specific shRNAs. Autophagic flux was detected using western blotting and Tandem mRFP-GFP-LC3 assay. The protein levels were measured via western blotting. Cell proliferation was tested via CCK-8 proliferation assay and Edu staining. Apoptosis was evaluated using flow cytometry.

Results: We found that upon L1CAM knockdown, the phosphorylation of p38 was enhanced, and autophagy was weakened. When p38 phosphorylation was inhibited, the inhibitory effect of L1CAM knockdown on autophagy was restored. And L1CAM can promote cell proliferation and inhibit cell apoptosis.

Conclusions: Taken together, these findings indicate that L1CAM promotes autophagy by inhibiting the phosphorylation of p38, and that it also promotes cell proliferation and inhibits cell apoptosis in colon cancer cells. L1CAM might be as a new target in CRC therapy.

Background: Cuproptosis and oxidative stress (COS) are emerging regulators in cancer biology. However, their link to long non-coding RNAs (lncRNAs) and clinical outcomes in ovarian cancer remains unclear.

Objective: This study aimed to construct and validate a prognostic signature based on COS-related lncRNAs to improve risk stratification and provide insights into therapeutic strategies for ovarian cancer.

Methods: RNA-seq and clinical data from TCGA and GEO were analyzed to identify COS-related lncRNAs via WGCNA and Pearson correlation. Prognostic lncRNAs were screened using Cox regression and modeled using multiple machine learning algorithms. Immune profiles, mutation patterns, drug sensitivity, and experimental validation were performed.

Results: A 12-lncRNA signature was established that stratified patients into high- and low-risk groups with significant survival differences (HR = 22.6145, p < 0.001). The model showed strong predictive performance (AUCs: 0.91/0.967/0.974) and was validated externally. High-risk patients exhibited greater mutation burden, altered immune pathways (interferon, TGF-β), and differential predicted sensitivity to agents like Axitinib and Pazopanib. RT-qPCR confirmed the expression patterns of 11 out of 12 lncRNAs.

Conclusion: This study proposes a robust 12-lncRNA signature linking cuproptosis and oxidative stress with prognosis and therapy response in ovarian cancer, offering preliminary insights for personalized treatment guidance.

Background: Flowering time (referred to as heading date in rice) is a characteristic controlled by quantitative trait loci (QTLs). Heading date is an important agronomic trait that determines yield in rice and is precisely regulated by various exogenous and endogenous factors.

Objective: To investigate the role of the ascorbate peroxidase 9 (APX9) gene in the flowering mechanism, we examined the expression levels of flowering time regulators under long-day conditions (14 h light/10 hours dark) and natural long-day conditions.

Methods: A BC₄F₇ near-isogenic line (NIL), derived from an interspecific cross between the Korean elite japonica cultivar (cv. Hwaseong, Oryza sativa L.) and Oryza rufipogon (IRGC 105491), was used in this study to identify and characterize candidate genes associated with heading traits. The NIL contains a small O. rufipogon chromosome segment harboring the APX9 gene. In addition, overexpression and T-DNA insertion knockout transgenic lines were used to examine the function of APX9 gene.

Results: The NIL exhibited delayed flowering under both controlled and natural long-day conditions compared to Hwaseong. Real-time PCR analysis indicated that APX9 influences the upstream pathway of flowering time regulation. Over-expression lines showed delayed flowering relative to Hwaseong, whereas T-DNA mutants flowered earlier than the control cultivar, Dongjin. These findings support a role for APX9 in modulating flowering time in rice.

Conclusion: The APX9 is known for its antioxidant activity and its response to various abiotic stresses. The APX9 may play a role in the upstream regulation of the flowering pathway. These findings will be valuable in understanding the effect of APX9 during flowering stages. It would also be interesting to explore the relationship between hydrogen peroxide (H₂O₂₎ levels and the APX9 gene in signaling events related to heading.

Background: Hypoxia is a significant manifestation of severe asthma in children. An early and accurate diagnosis is crucial for enhancing treatment outcomes and mitigating long-term complications. This study aims to utilize bioinformatics analysis to investigate hypoxia-related genes (HRGs) in childhood asthma.

Objective: This study aims to develop a diagnostic model and identify key hypoxia-related biomarkers in childhood asthma based on transcriptomic data analysis.

Methods: Hypoxia-related differentially expressed genes (HRDEGs) were identified from bronchial epithelial transcriptomes (GSE27011/GSE40732 datasets) using limma analysis. A diagnostic model was developed using LASSO regression, and hub genes were identified via protein-protein interaction (PPI) networks. Asthma subtyping and immune microenvironment characterization were conducted using ConsensusClusterPlus and CIBERSORTx, respectively. Experimental validation in house dust mite (HDM)-induced asthmatic mice confirmed transcriptional changes in candidate genes.

Results: We obtained 19 HRDEGs and 11 model genes (AHR, AKR1C3, ELP3, GNAL, GZMB, LPP, MAFG, PDGFD, PPP1R12B, SYNE2, and TAF15). Regression analyses demonstrated the model's robust diagnostic performance. PPI analysis identified 10 hub genes associated with asthma, with AKR1C3 showing high diagnostic accuracy for different molecular subtypes. Immune infiltration analysis indicated significant correlations between hub genes and eight immune cell types, including B cells, effector T cells, cytotoxic T cells, regulatory T cells (Tregs), monocytes, mast cells, eosinophils, and neutrophils.

Conclusions: In this study, a hypoxia-related gene signature associated with childhood asthma was identified. These findings not only highlight potential therapeutic targets for asthma but also offer new insights into its pathogenesis.