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PubMed1997

Temperature-sensitive lesions in the Francisella novicida valA gene cloned into an Escherichia coli msbA lpxK mutant affecting deoxycholate resistance and lipopolysaccharide assembly at the restrictive temperature.

The valAB locus of Francisella novicida has previously been found to be highly similar at the deduced amino acid level to msbA lpxK of Escherichia coli. Both ValA and MsbA are members of the superfamily of ABC transporters, and they appear to have similar functions. In this study we describe the isolation of a temperature-sensitive valAB locus. DNA sequence analysis indicates that the only changes to the ValAB deduced amino acid sequence are changes of S453 to an F and T458 to an I in ValA. E. coli strains defective in msbA and expressing temperature-sensitive ValA rapidly ceased growth when shifted from a permissive temperature to a restrictive temperature. After 1 h at the restrictive temperature, cells were much more sensitive to deoxycholate treatment. To test the hypothesis that ValA is responsible for the transport or assembly of lipopolysaccharide, we introduced gseA, a Kdo (3-deoxy-D-manno-octulosonic acid) transferase from Chlamydia trachomatis, into a strain with a temperature-sensitive valA allele and a nonfunctional msbA locus. These recombinants were defective in cell surface expression of the chlamydial genus-specific epitope within 15 min of a shift to the nonpermissive temperature. Also, there was enhanced association of the epitope with the inner membrane after a shift to the nonpermissive temperature. Thus, we propose that ValA is involved in the transport of lipopolysaccharide to the outer membrane.

ATP-Binding Cassette Transporters↗

PubMed2026

Transcriptomic profiling across stages of non-muscle-invasive bladder cancer identifies fibroblast activation protein-alpha as a stromal biomarker associated with progression.

BACKGROUND: T1 non-muscle-invasive bladder cancer (NMIBC) represents a biologically aggressive subgroup with substantial heterogeneity in recurrence and progression risk. Current clinicopathological risk stratification tools lack sufficient precision to identify patients at the highest risk of progression to muscle-invasive bladder cancer (MIBC). OBJECTIVE: To characterize transcriptomic differences between T1 and < T1 (Ta/Tis) NMIBC and to explore the association of fibroblast activation protein-α (FAP) gene expression with disease progression. METHODS: Transcriptomic profiling was performed on formalin-fixed paraffin-embedded (FFPE) tumor tissue from 66 patients with primary, treatment-naïve NMIBC and 5 patients with T2 disease (included for exploratory comparisons). Analyses included differential gene expression, gene set enrichment analysis (GSEA), molecular subtyping, immune cell deconvolution, and evaluation of FAP expression in relation to recurrence and progression. External validation of FAP was conducted in three independent NMIBC cohorts. RESULTS: T1 tumors demonstrated a distinct transcriptomic profile compared with < T1 tumors, characterized by enrichment of cell cycle-related and metabolic pathways and a higher prevalence of aggressive molecular subtypes. Despite these molecular differences, no statistically significant differences in recurrence-free, progression-free, cancer-specific, and overall survival were observed, likely reflecting limited event numbers. Among recurrent tumors, early recurrences (≤ 24 months) were associated with epithelial-mesenchymal transition signatures. FAP expression increased with tumor stage (p = 0.0005) and was associated with progression (p = 0.002) and mortality (p = 0.01). Patients with tumors in the highest quartile of FAP expression had worse progression-free survival. This association was consistently observed in three external NMIBC cohorts. CONCLUSIONS: T1 NMIBC exhibits distinct transcriptomic features suggestive of increased biological aggressiveness. Elevated FAP expression is reproducibly associated with progression risk across multiple cohorts, supporting its potential role as a biomarker of aggressive disease. Given the limited number of progression events, these findings should be considered hypothesis-generating and warrant prospective validation before clinical implementation.

Humans↗

PubMed2026

Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.

BACKGROUND: The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. RESULTS: In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. We applied this workflow to a local SARS-CoV-2 cohort with 76 asymptomatic infected patients, among whom 52 (68.42%) developed symptomatic infection during a 1-week follow-up period. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 8.94% reads that did not map to the human genome across all 149 samples, among which around half (median 49.68%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment (P = 0.004), and both groups showed decreased loads one week later (symptomatic, P = 0.001; asymptomatic, P = 0.035). Compared with benchmarking 16 S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genera (median Rho = 0.90, Pmax < 0.001). A total of 670 bacteria species were identified to show a relative abundance ≥ 0.01% in at least 10% samples. Differential abundance analysis identified 76 species (DASs) from six phyla with significantly decreased abundance in samples from the symptomatic group (log2(fold change or FC) < -1 and adjusted P < 0.05) compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host's gene expression using an expression quantitative trait bacteria (eQTB) model, we found 45 symptom-associated DASs identified at enrollment were significantly associated with one to 14 genes (adjusted P < 0.05). GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to olfactory function, keratinocyte differentiation, and DNA methylation. CONCLUSIONS: In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome associations, offering insights into microbial contributions to respiratory diseases.

Humans↗

PubMed2026

A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.

BACKGROUND: Accurate prediction of postoperative recurrence in lung adenocarcinoma (LUAD) is essential for guiding clinical decision-making and improving patient outcomes. Although various predictive models have been developed, most rely on complex genomic analyses and high-dimensional clinical data. The complexity of these approaches substantially limits their feasibility for routine clinical use. To address this clinical challenge, this study aims to predict postoperative recurrence using routinely available hematoxylin and eosin (H&E)-stained images and characterize the associated biological features. METHODS: A total of 329 patients who underwent curative resection at the First Affiliated Hospital of Wenzhou Medical University (FHWMU) were retrospectively enrolled and randomly assigned to training and internal validation cohorts in a 7:3 ratio. An independent external validation cohort comprising 70 patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) was included. Three patch-level feature extractors (Inception_V3, ResNet18, and DenseNet121) were evaluated within a weakly supervised multiple-instance learning (MIL) framework incorporating automated region-of-interest (ROI) detection on segmented whole-slide images (WSIs). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Kaplan-Meier (KM) survival analysis, and multivariable Cox proportional hazards regression. Transcriptomic profiling and gene set enrichment analysis (GSEA) were conducted to investigate biological differences between risk groups. RESULTS: The model achieved AUCs of 0.923 in the training cohort, 0.891 in the internal validation cohort, and 0.847 in the external validation cohort. The model effectively stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) across all cohorts (all P < 0.001) and retained prognostic value within AJCC stages I-III. Transcriptomic analyses revealed consistent enrichment of cell cycle-related pathways and neutrophil extracellular trap (NET) formation in high-risk patients across both institutional and CPTAC cohorts, aligning with distinct biological profiles of the model-derived risk stratification. CONCLUSIONS: This weakly supervised deep learning framework enables accurate and externally validated prediction of postoperative recurrence in LUAD using routinely available histopathological images, and integration of histopathological features with molecular analyses enhances biological interpretability. This work provides a clinically accessible and cost-effective tool for postoperative risk assessment in LUAD patients.

Humans↗

PubMed2025

Lactate dehydrogenase a is a crucial biomarker that affects the prognosis, chemotherapy effect, and immune infiltration of breast cancer.

PURPOSE: Lactate dehydrogenase A (LDHA) is a key node in tumor growth, metabolism, and invasion and is upregulated across multiple cancers. However, the molecular mechanisms by which LDHA influences breast cancer (BC) remain unclear. We analyzed public datasets and an institutional cohort to clarify the relationship between LDHA and BC, with the aim of informing future therapeutic strategies. PATIENTS AND METHODS: Using The Cancer Genome Atlas (TCGA), we assessed LDHA expression in BC and examined its associations with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint molecules, and drug sensitivity. We integrated multiple databases and used Kaplan-Meier analyses to evaluate prognostic value. To explore biological functions of LDHA, we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). We then analyzed BC patients receiving neoadjuvant chemotherapy (NAC) at Harbin Medical University Cancer Hospital to test the relationship between serum lactate dehydrogenase (LDH) and pathological complete response (pCR). Finally, we used National Health and Nutrition Examination Survey (NHANES) data to examine the association between serum LDH and mortality. RESULTS: LDHA was upregulated in BC tissues, and higher expression was significantly associated with worse overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS). Functional analyses indicated enrichment of metabolic pathways, such as glycolysis. LDHA expression correlated with multiple immune cell populations, suggesting involvement in the tumor immune microenvironment. Lower LDHA expression was associated with greater sensitivity to several chemotherapeutic agents. In our institutional cohort, patients with lower serum LDH were more likely to achieve pCR, and LDH was an independent predictor of pCR. In NHANES, elevated serum LDH was linked to increased mortality risk. CONCLUSION: Our findings suggest that LDHA expression and serum LDH levels are promising prognostic biomarkers for survival and may predict chemotherapy response in BC patients. These results highlight the clinical relevance of LDHA-mediated metabolic pathways. However, as a correlational study, our findings warrant further validation through functional experiments to confirm LDHA's role as a potential therapeutic target.

Humans↗

PubMed2024

Comprehensive analysis of the expression, prognostic, and immune infiltration for COL4s in stomach adenocarcinoma.

BACKGROUND: Collagen (COL) genes, play a key role in tumor invasion and metastasis, are involved in tumor extracellular matrix (ECM)-receptor interactions and focal adhesion pathways. However, studies focusing on the diagnostic value of the COL4 family in stomach adenocarcinoma (STAD) are currently lacking. METHODS: The TCGA database was employed to retrieve the clinical features and RNA sequencing expression profiles of patients with STAD. We conducted an investigation to examine the expression disparities between STAD and adjacent normal tissues. Kaplan-Meier survival analysis was utilized to assess their prognostic significance, while Spearman correlation analysis was employed to determine their association with immune checkpoint genes and immunomodulatory molecules. Furthermore, GO and KEGG analyses were performed on the COL4s-related genes, revealing potential biological pathways through gene set enrichment analysis (GSEA). Subsequently, we explored the extent of immune infiltration of the COL4 family in STAD using the TIMER database. Lastly, the expression levels of the COL4 family in STAD were further validated through quantitative PCR (qPCR) and western blot techniques. RESULTS: The expression levels of COL4A1/2 were significantly upregulated, while COL4A5/6 were conspicuously downregulated in STAD. The survival analysis revealed that the upregulated COL4s indicated poorer overall survival, first progression and post-progression survival outcomes. Additionally, our findings demonstrated a positive correlation between the expressions of COL4A1/2/3/4 and the infiltration of immune cells, including CD8 + T cells, dendritic cells, macrophages, neutrophils and CD4 + T cells. Further correlation analysis uncovered a favorable association between the expression of COL4A1/2/3/4 and various crucial immunomodulatory molecules, immunological checkpoint molecules, and chemokines. Quantitative PCR analysis confirmed that the expression patterns of COL4A1/3/4/6 genes aligned with the finding from the TCGA database. However, gastric cancer cells exhibited downregulation of COL4A2. Consistently, the protein level of COL4A1 was elevated, whereas the protein level of COL4A2 was reduced in the gastric cancer cell lines. CONCLUSION: COL4s could potentially serve as biomarkers for diagnosing and predicting the prognosis of STAD.

Stomach Neoplasms↗

PubMed2024

Exploring the clinical and biological significance of the cell cycle-related gene CHMP4C in prostate cancer.

BACKGROUND: Prostate cancer (PCa) stands as the second most prevalent malignancy impacting male health, and the disease's evolutionary course presents formidable challenges in the context of patient treatment and prognostic management. Charged multivesicular body protein 4 C (CHMP4C) participates in the development of several cancers by regulating cell cycle functions. However, the role of CHMP4C in prostate cancer remains unclear. METHODS: In terms of bioinformatics, multiple PCa datasets were employed to scrutinize the expression of CHMP4C. Survival analysis coupled with a nomogram approach was employed to probe into the prognostic significance of CHMP4C. Gene set enrichment analysis (GSEA) was conducted to interrogate the functional implications of CHMP4C. In terms of cellular experimentation, the verification of RNA and protein expression levels was executed through the utilization of qRT-PCR and Western blotting. Upon the establishment of a cell line featuring stable CHMP4C knockdown, a battery of assays, including Cell Counting Kit-8 (CCK-8), wound healing, Transwell, and flow cytometry, were employed to discern the impact of CHMP4C on the proliferation, migration, invasion, and cell cycle function of PCa cells. RESULTS: The expression of CHMP4C exhibited upregulation in both PCa cells and tissues, and patients demonstrating elevated CHMP4C expression levels experienced a notably inferior prognosis. The nomogram, constructed using CHMP4C along with clinicopathological features, demonstrated a commendable capacity for prognostic prediction. CHMP4C knockdown significantly inhibited the proliferation, migration, and invasion of PCa cells (LNcaP and PC3). CHMP4C could impact the advancement of the PCa cell cycle, and its expression might be regulated by berberine. Divergent CHMP4C expression among PCa patients could induce alterations in immune cell infiltration and gene mutation frequency. CONCLUSIONS: Our findings suggest that CHMP4C might be a prognostic biomarker in PCa, potentially offering novel perspectives for the advancement of precision therapy for PCa.

Humans↗

PubMed2024

Integrated bioinformatics analysis and experimental validation reveal the relationship between ALOX5AP and the prognosis and immune microenvironment in glioma.

BACKGROUND: Treatment of gliomas, the most prevalent primary malignant neoplasm of the central nervous system, is challenging. Arachidonate 5-lipoxygenase activating protein (ALOX5AP) is crucial for converting arachidonic acid into leukotrienes and is associated with poor prognosis in multiple cancers. Nevertheless, its relationship with the prognosis and the immune microenvironment of gliomas remains incompletely understood. METHODS: The differential expression of ALOX5AP was evaluated based on public Databases. Kaplan-Meier, multivariate Cox proportional hazards regression analysis, time-dependent receiver operating characteristic, and nomogram were used to estimate the prognostic value of ALOX5AP. The relationship between ALOX5AP and immune infiltration was calculated using ESTIMATE and CIBERSORT algorithms. Relationships between ALOX5AP and human leukocyte antigen molecules, immune checkpoints, tumor mutation burden, TIDE score, and immunophenoscore were calculated to evaluate glioma immunotherapy response. Single gene GSEA and co-expression network-based GO and KEGG enrichment analysis were performed to explore the potential function of ALOX5AP. ALOX5AP expression was verified using multiplex immunofluorescence staining and its prognostic effects were confirmed using a glioma tissue microarray. RESULT: ALOX5AP was highly expressed in gliomas, and the expression level was related to World Health Organization (WHO) grade, age, sex, IDH mutation status, 1p19q co-deletion status, MGMTp methylation status, and poor prognosis. Single-cell RNA sequencing showed that ALOX5AP was expressed in macrophages, monocytes, and T cells but not in tumor cells. ALOX5AP expression positively correlated with M2 macrophage infiltration and poor immunotherapy response. Immunofluorescence staining demonstrated that ALOX5AP was upregulated in WHO higher-grade gliomas, localizing to M2 macrophages. Glioma tissue microarray confirmed the adverse effect of ALOX5AP in the prognosis of glioma. CONCLUSION: ALOX5AP is highly expressed in M2 macrophages and may act as a potential biomarker for predicting prognosis and immunotherapy response in patients with glioma.

Humans↗

PubMed2025

Study on the mechanism of BGN in progression and metastasis of ccRCC.

PURPOSE: To investigate the role of Biglycan(BGN) in the progression and metastasis of clear cell renal cell carcinoma(ccRCC). METHODS: Based on multiple public databases, we investigated the expression level of BGN in ccRCC, its clinical significance, and its association with immune cells. Real-time fluorescence quantitative polymerase chain reaction(PCR) was employed to validate BGN expression in tumor and adjacent normal tissues from ten patients. We utilized RNA sequencing results for further analysis, including differential gene analysis, GO-KEGG analysis, and GSEA analysis, to identify the signaling pathways through which BGN exerts its effects. BGN knockdown cells(786-0 and Caki-1) were generated through lentiviral transfection to examine the impact of BGN on ccRCC. Cell proliferation, migration, and invasion were assessed using CCK8, colony formation, wound healing, Transwell migration, and invasion assays, respectively. RESULTS: Our findings from database analysis and PCR revealed a significant upregulation of BGN expression in kidney cancer tissues compared to normal tissues. Further analysis demonstrated a correlation between high BGN expression and ccRCC progression and immune infiltration. In vitro experiments confirmed that BGN silencing effectively inhibited cell proliferation, migration, and invasion of ccRCC. Mechanistically, these effects may be mediated through the MAPK signaling pathway. CONCLUSION: BGN potentially plays a pivotal role in the progression and metastasis of ccRCC, possibly acting through the MAPK signaling pathway. Therefore, BGN holds promise as a potential therapeutic target for ccRCC.

Humans↗

PubMed2026

Targeting RELA and STAT3 regulates TNFRSF10A-mediated apoptosis in a novel apoptosis-based prognostic model for clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal malignancy and remains a major cause of cancer-related mortality worldwide. Although advances in surgery, targeted therapy, and immunotherapy have improved outcomes for patients, reliable biomarkers for predicting prognosis remain limited. Therefore, robust gene-based prognostic models are urgently needed to improve risk stratification and guide individualized treatment strategies. METHODS: We developed a novel prognostic model integrating apoptosis and immune - related genes (AIRGs) to predict overall survival (OS) in patients with ccRCC. RESULT: Using Gene Set Enrichment Analysis (GSEA) combined with least absolute shrinkage and selection operator (LASSO) Cox regression, we identified 7 key prognostic genes, namely, CCR4, TNFRSF10A, TEK, TGFA, CD14, IFITM1, and SEMA3G, that collectively demonstrated strong predictive performance in TCGA cohort with c-index = 0.711. Functional enrichment analyses revealed that apoptosis, immune regulation, and multiple oncogenic signaling pathways were significantly associated with the risk score, highlighting the critical role of the tumor microenvironment in ccRCC progression. Transcription factor binding analysis based on the JASPAR database suggested that RELA and STAT3 with scores of 0.829 and 0.951, respectively are potential upstream regulators within the prognostic network, particularly influencing TNFRSF10A expression. External validation using the International Cancer Genome Consortium (ICGC) dataset confirmed the robustness of the prognostic model with c-index = 0.612 Furthermore, in vitro experiments demonstrated that RELA and STAT3 regulate TNFRSF10A-mediated apoptotic signaling in ccRCC cells, providing mechanistic support for the bioinformatic findings. CONCLUSION: This study establishes a biologically informed and clinically relevant prognostic framework for ccRCC. Our findings highlight the therapeutic potential of targeting the RELA/STAT3-TNFRSF10A axis and contribute to the advancement of precision medicine in ccRCC.

Humans↗

PubMed2026

Increased IL4I1 expression predicts poor survival and modulates the immune microenvironment in acute myeloid leukemia.

BACKGROUND: The immunometabolic enzyme Interleukin-4-induced-1 (IL4I1) is implicated in cancer pathogenesis, yet its specific function and clinical relevance in acute myeloid leukemia (AML) remain unclear. METHODS: Comparative analysis of IL4I1 mRNA levels between AML patients and normal controls was performed using the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of IL4I1. Functional insights were derived from analyses of differentially expressed genes (DEGs), Gene Set Enrichment Analysis (GSEA), and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Immune infiltration was evaluated using the ssGSEA, ESTIMATE, quanTIseq and single-cell RNA sequencing (scRNA-seq) analysis. Finally, in vitro and in vivo functional experiments were perfromed to explore the impact of IL4I1 on AML progression and immunoregulation. RESULTS: IL4I1 expression was significantly elevated in AML compared to normal controls (p = 0.0004) and associated with poorer overall survival (p = 0.003). Bioinformatic analysis revealed that IL4I1 was linked to immune-related pathways—including humoral immune response, leukocyte interactions, and chemokine signaling—and to cellular amino acid metabolism. Its expression correlated with immune cell infiltration and checkpoint molecule expression. Experimentally, IL4I1 promoted leukemia cell proliferation in vitro and in vivo (p < 0.05). Furthermore, silencing IL4I1 suppressed M2 macrophage polarization and reduced secretion of inflammatory factors (p < 0.05). CONCLUSIONS: IL4I1 may serve as a potential biomarker for poor prognosis and an attractive target for immune-based therapeutic interventions in AML.

Humans↗

PubMed2026

Analysis and validation of abnormal signaling pathways and immune cell infiltration characteristics in digestive system cancers based on peroxisome-related genes.

BACKGROUND: Although emerging evidence suggests a role for peroxisomes in tumorigenesis, their functions in digestive cancers remain unclear. This study aims to investigate the association between peroxisomes and digestive tract tumors. METHODS: To systematically investigate peroxisomal functions in digestive cancers, we first constructed and validated tumor-specific prognostic signatures based on peroxisome-related genes (PRGs) through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. We then characterized the tumor immune microenvironment (TIME) with CIBERSORT, X-CELL, and EPIC algorithms, and identified tumor-specific and common signalings via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Focusing on hepatocellular carcinoma (HCC), we experimentally validated peroxisome-related therapeutic responses by profiling signature genes in radioresistant cells and an orthotopic transarterial chemoembolization (TACE) rat model. PEX13 knockdown further assessed peroxisomal role in radiosensitivity and targeted therapy response. Clinical relevance of PEX13 was evaluated in HCC cohort. Single-cell RNA sequencing dataset and lipidomics further revealed peroxisomal mechanisms in HCC progression. Finally, peroxisomal function in colorectal cancer (CRC) was validated in vitro. RESULTS: Novel peroxisome-related prognostic signatures demonstrated strong predictive power in HCC, colon adenocarcinoma, rectal adenocarcinoma, pancreatic adenocarcinoma, gastric adenocarcinoma, esophageal adenocarcinoma, esophageal squamous cell carcinoma, and cholangiocarcinoma. High-risk patients displayed an immunosuppressive microenvironment, characterized by increased infiltration of regulatory T cells, M2 macrophages, Th2 cells, or cancer-associated fibroblasts, or Th1 cells' reduction. Peroxisomes engaged in several distinct yet convergent pathways, most notably "positive regulation of response to stimuli". HCC prognostic genes were dynamically regulated in response to therapeutic stimuli, including radiotherapy, targeted therapy, and TACE. Clinically, the expression of PEX13 was markedly upregulated in tumor tissues from therapy-resistant HCC patients. Mechanistically, peroxisomal dysfunction induced by silencing PEX13 in HCC or UBE2D2 in CRC may overcome therapeutic resistance (radiotherapy/ lenvatinib resistance in HCC, radioresistance in CRC) through reprogramming lipid metabolism. CONCLUSIONS: Peroxisomes act as pivotal regulators of digestive cancer progression by modulating signaling pathways, the TIME, therapeutic resistance, and lipid metabolism. Targeting peroxisomal function, particularly in high-risk subgroups of HCC and CRC, warrants further exploration as a promising therapeutic strategy.

Peroxisomes↗

PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗

PubMed2026

KLF5 promotes proliferation, migration, and autophagy-/EMT‑associated molecular changes in lens epithelial cells via transcriptional activation of THBS1 in traumatic cataract.

PURPOSE: Traumatic cataract is a common blinding eye disease after ocular trauma, and its pathogenesis is closely related to lens epithelial cell dysfunction, while the definite molecular regulatory mechanism between upstream transcription factor and downstream target gene remains poorly clarified. This study aimed to clarify the role and molecular mechanism of the krüppel-like factor 5 (KLF5)/ thrombosponin 1 (THBS1) axis in regulating proliferation, migration, epithelial-mesenchymal transition and autophagy of lens epithelial cells in traumatic cataract, and to explore its potential clinical therapeutic value. METHODS: The GSE295383 dataset in the gene expression omnibus (GEO) database was downloaded, and the differentially expressed genes (DEGs) were screened by linear models for microarray data (limma) package of R language. Combined with Weighted gene co-expression network analysis (WGCNA), the gene co-expression network was constructed and the key modules were screened. Gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) combined with human transcription factor target (hTFtarget) and JASPAR databases were used to predict the upstream transcription factors of THBS1. Subsequently, SRA01/04 cells were induced with transforming growth factor-beta 2 (TGF-β2) to construct a cataract cell model. RESULTS: THBS1 and KLF5 were highly expressed in LECs exposed to TGF-β2. KLF5 could activate THBS1 transcription by binding to THBS1 promoter - 174 to -165 sites. Knockdown of THBS1 inhibited TGF-β2-induced viability, proliferation, migration, and altered the expression of epithelial-mesenchymal transition (EMT)- and autophagy-related markers in LECs. Knockdown of KLF5 downregulated THBS1 expression and produced a similar inhibitory effect, while overexpression of THBS1 reversed the effect of KLF5 knockdown. CONCLUSIONS: This study demonstrated that KLF5 promoted the proliferation, migration, and EMT‑associated molecular changes of LECs in traumatic cataract through transcriptional activation of THBS1, and regulated the expression of autophagy‑related markers in LECs, suggesting that KLF5/THBS1 axis might be a potential target for the treatment of traumatic cataract.

Cataract↗

PubMed2026

Lipid Metabolism-related lncRNA Model Identifies AC026412.3 as a Driver of Fatty Acid β-oxidation in Hepatocellular Carcinoma.

BACKGROUND AND AIMS: Dysregulated lipid metabolism contributes to hepatocellular carcinoma (HCC) progression, but the prognostic value and mechanistic roles of lipid metabolism-related long noncoding RNAs (LRLs) remain insufficiently characterized. This study aimed to construct and validate an LRL-based prognostic model and to investigate the biological function and metabolic mechanism of AC026412.3 in HCC. METHODS: Transcriptomic and clinical data from the The Cancer Genome Atlas Liver Hepatocellular Carcinoma cohort were analyzed to identify LRLs based on their correlation with curated lipid metabolism genes. Differential expression, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a prognostic signature, which was evaluated using Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses. Functional enrichment analyses Gene Ontology [GO], Kyoto Encyclopedia of Genes and Genomes [KEGG] and gene set enrichment analysis [GSEA], mutation profiling, tumor mutational burden, immune infiltration estimation, and consensus clustering were applied to characterize associated features. A key LRL was identified through integrated bioinformatic screening and prioritization. Its biological role was assessed by quantitative reverse transcription polymerase chain reactionq (RT-PCR), western blotting, BODIPY staining, colony formation, Transwell assays, and xenograft models. RNA sequencing followed by pathway enrichment analysis was conducted to explore underlying mechanisms. RESULTS: A three-LRL signature (AL031985.3, NRAV, and AC026412.3) stratified HCC patients into distinct risk groups with significantly different survival outcomes and demonstrated independent prognostic value. AC026412.3 was markedly upregulated in HCC and associated with poor prognosis. Functional assays demonstrated that AC026412.3 promoted proliferation, invasion, and tumor growth while reducing lipid accumulation. Mechanistically, AC026412.3 upregulated solute carrier family 22 member 5 (SLC22A5), enhanced fatty acid β-oxidation, and increased adenosine triphosphate (ATP) production, thereby driving metabolic reprogramming. CONCLUSIONS: This study establishes a robust LRL-based prognostic model and identifies AC026412.3 as a key regulator of lipid metabolic reprogramming via the SLC22A5-fatty acid β-oxidation axis, highlighting its potential as a biomarker and therapeutic target in HCC.

HCC↗

PubMed2026

Predicting diagnostic gene biomarkers associated with immune infiltration in patients with diabetes.

Diabetes is a global public health problem with various complications, which can lead to disability and mortality. This study identified potential diagnostic markers for diabetes and explored the immunometabolic mechanisms in the pathological process. The gene expression of 17 diabetes cases and 16 normal controls were obtained from the Gene Expression Omnibus (GEO) database. The "limma" package was employed for screening differentially expressed genes (DEGs). Gene functions and enriched pathways of DEGs were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Candidate key genes were screened using the least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. The diagnostic effectiveness of identified markers was further verified via the receiver operating characteristic (ROC) curve. The compositional patterns of immune cell infiltration and signaling pathway enrichment associated with key genes were explored via single sample Gene Set Enrichment Analysis (ssGSEA) and GSEA analysis, respectively. Possible miRNAs interacting with key genes were predicted via miRcode database. B2M, FTL, SH3BGRL3, and SOD2 were recognized as diagnostic markers for diabetes based on LASSO regression and the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm. Analysis of immune cell infiltration demonstrated that the four key genes were related to B cells, neutrophils, macrophages, and CD8+ T cells. The diagnostic value of B2M, FTL, and SOD2 for diabetes was higher than that of SH3BGRL3 according to the ROC curve. Validation experiments indicated that the mRNA expression of B2M and FTL was increased in liver tissues of diabetic mice. B2M and FTL can act as diagnostic markers for diabetes and contribute to new understandings of the disease's molecular mechanisms.

Humans↗

PubMed2026

Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. METHODS: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. RESULTS: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: PARP1, MBD4, TELO2, NSMCE3, SMUG1, and BABAM1. A nomogram incorporating risk score (RS) and clinical variables achieved strong predictive accuracy for 1- to 3-year survival [area under the curve (AUC) >0.7]. High-HRD tumors exhibited distinct mutational patterns (TP53 and TTN) and enriched glutathione metabolism and cytochrome P450 pathways. Immune infiltration analysis revealed significant differences in plasma cell and neutrophil infiltration between risk groups (P<0.05), suggesting HRD-associated immune microenvironment remodeling. CONCLUSIONS: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗

PubMed2026

A CFH- and SPINT2-based prognostic signature for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant tumor with a poor prognosis, and reliable biomarkers for postoperative risk stratification remain limited. This study aimed to develop and validate a CFH- and SPINT2-based prognostic signature to support postoperative risk stratification and inform adjuvant therapy selection in CCA through integrative machine learning and single-cell transcriptomics. METHODS: Differentially expressed genes were screened from GSE26566. Integrative machine learning (least absolute shrinkage and selection operator-Cox, random forest, and univariate Cox regression) was performed in the training cohort (GSE89749; n=115) to construct a risk model, which was externally validated in two independent cohorts: cohort 1 (E-MTAB-6389; n=75) and cohort 2 [The Cancer Genome Atlas Cholangiocarcinoma (TCGA-CHOL) data set; n=36]. Systematic analysis was conducted and included examinations of immune infiltration [via single-sample gene set enrichment analysis (ssGSEA)], pathway enrichment (via hallmark GSEA), cellular localization (via single-cell RNA sequencing), and drug sensitivity (via the Genomics of Drug Sensitivity in Cancer 2 database). RESULTS: Two genes, CFH and SPINT2, were identified and incorporated into a prognostic risk score. High-risk patients in the training cohort had a significantly worse overall survival (log-rank P=0.02). External validation was performed in two independent cohorts. In validation cohort 1, the risk group was an independent prognostic factor [hazard ratio =2.27, 95% confidence interval (CI): 1.18-4.37; P=0.01]. In validation cohort 2, the model demonstrated acceptable discriminative ability (concordance index =0.721; 3-year area under the curve =0.692). The high-risk group exhibited an immunosuppressive microenvironment characterized by increased infiltration of macrophages and myeloid-derived suppressor cells, along with the activation of epithelial-mesenchymal transition, inflammatory response, and NF-κB signaling pathways. Single-cell analysis revealed a cell-type-specific expression pattern: CFH was predominantly expressed in fibroblasts, while SPINT2 was mainly expressed in malignant cells. Drug sensitivity analysis demonstrated that the high-risk group was more sensitive to gemcitabine, cisplatin, poly(ADP-ribose) polymerase (PARP) inhibitors, and mammalian target of rapamycin (mTOR) inhibitors, whereas the low-risk group was more sensitive to lapatinib. CONCLUSIONS: The CFH- and SPINT2-based prognostic signature may serve as an independent biomarker for postoperative risk stratification in CCA. High-risk patients, characterized by fibroblast-derived CFH enrichment and malignant-cell SPINT2 loss, exhibit an immunosuppressive microenvironment and may be more suitable for gemcitabine-based chemotherapy or PARP/mTOR inhibitors, whereas low-risk patients may benefit from less intensive adjuvant strategies or HER2/EGFR-targeted lapatinib. Prospective validation is warranted before clinical implementation.

Cholangiocarcinoma (CCA)↗