The development of bladder cancer (BLCA) is associated with mitochondrial dysfunction and neutrophil extracellular traps (NETs); however, the relationship between mitochondrial function and NET formation in BLCA remains poorly understood. In this study, BLCA datasets, along with mitochondria- and NET-related genes, were retrieved from public databases and existing literature. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) networks were applied to identify prognostic genes. A prognostic model incorporating six key genes (CCDC80, NIBAN1, CSPG4, PDGFRA, MAP1A, and PCOLCE2) was established through machine learning methods and univariate Cox regression analysis. This model demonstrated strong prognostic accuracy for BLCA, further validated by a nomogram exhibiting excellent predictive performance. Using the established prognostic model, patient samples were stratified into high-risk (HRG) and low-risk groups (LRG). Significant differences in immune cell infiltration-including eosinophils and 24 other immune cell types-were observed between these groups. The risk scores strongly correlated with multiple immune cells, notably natural killer cells. Furthermore, immune checkpoint analysis revealed significant upregulation of only three checkpoint genes (TNFRSF14, TNFRSF25, and VEGFA) in the LRG. Additionally, fibroblasts were identified as key cells through analysis of the GSE222315 dataset, with prognostic gene expression varying significantly during fibroblast differentiation. Experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR) confirmed that all six prognostic genes were significantly downregulated in BLCA clinical samples collected for this study. Overall, the study highlights six novel prognostic biomarkers and presents a robust predictive model, providing new insights into BLCA prognosis.

This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transcriptomic and gene expression data from TCGA and GEO databases, 72 immune-related lncRNAs were identified through weighted gene co-expression network analysis (WGCNA) and ImmuLncRNA algorithms. The model was further optimized using 101 combinations of 10 machine learning approaches, ultimately constructing an immune-related lncRNA signature(IRLS) scoring system comprising nine key lncRNAs. Validated across 17 independent cohorts, the model demonstrated that high-risk patients had significantly shorter overall survival (OS) (P < 0.05), with predictive performance surpassing 95 published models (P < 0.05). Additionally, the IRLS score predicted responses to paclitaxel chemotherapy, and the low-risk group exhibited higher immune cell infiltration (P < 0.05), showing significant negative correlations with CD8A, PD-L1, tumor mutational burden (TMB), and neoantigen load (NAL). In immune checkpoint inhibitor (ICI) treatment cohorts, low IRLS scores were associated with improved response rates to atezolizumab. Our findings suggest that the IRLS model serves as a novel biomarker for prognostic stratification and personalized therapeutic decision-making in breast cancer.

Tumour associated neutrophils (TANs) promote metastasis through interactions of Neutrophil Extracellular Traps (NETs) with tumour cells. However, molecular details surrounding the interactions between NETs and Pancreatic Ductal Adenocarcinoma (PDAC) cells are poorly understood. Here, we examine the contribution of NETs in the progression of PDAC, which is characterized by high metastatic propensity. We carry out consensus clustering and pathway enrichment analysis of NET-related genes in an integrated cohort of 369 resectable and metastatic PDAC patient tumour samples, and compile two gene expression signatures comprising of either, integrin-actin cytoskeleton and Epithelial to Mesenchymal Transition (EMT) signaling, or cell death signaling, which identifies patients with very poor to better overall survival, respectively. Tumour Infiltrating neutrophils and NETs associate with ITGB1, CCDC25 and ILK, within clinical and experimental PDAC tumours. Functionally, exposure of PDAC cells to NETs identifies a cytoskeletal dynamic-associated CCDC25-ITGB1-ILK signaling complex which stimulates EMT and migration/invasion. NETosis-driven experimental metastasis to the lungs of PDAC cells delivered through the tail vein of female non-obese diabetic (NOD) scid gamma (NSG) mice is significantly inhibited by ILK knock down. Our data identify novel NET-related gene expression signatures for PDAC patient stratification, and reveal targetable signaling axes to prevent and treat disease progression.

Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer types, creating the most comprehensive analysis of the TME. In total, we found 41 patterns of immune infiltration and stroma profiles, identifying heterogeneous yet unique TME portraits for each cancer and several new findings. Our findings indicate that leukocytes play a major role in distinguishing various tumor types, and that a shared immune-rich TME cluster predicts better survival in bladder cancer for luminal and basal squamous subtypes, as well as in melanoma for RAS-hotspot subtypes. Our detailed deconvolution and mutational correlation analyses uncover 35 therapeutic target and candidate response biomarkers hypotheses (including CASP8 and RAS pathway genes).

Compound mylabris capsules (CMC) are widely used in the treatment of various malignant tumors; however, their mechanisms of action in diffuse large B-cell lymphoma (DLBCL) remain poorly understood. To address this, we analyzed DLBCL-related datasets from GEO and TCGA, identifying 943 key genes through differential expression gene analysis and weighted gene co-expression network analysis. By constructing a drug-active ingredient-target network, we determined the top five active CMC ingredients and their associated 1053 gene targets. Intersection analysis between CMC targets and key DLBCL genes revealed 50 overlapping targets. Enrichment analysis highlighted critical pathways, including the cell cycle and p53 signaling. Single-cell RNA sequencing further demonstrated that these targets are predominantly expressed in DLBCL tumor cell subsets. Protein-protein interaction network analysis identified pivotal genes such as CDK1 and CDK2, which showed robust diagnostic and prognostic value, as confirmed by receiver operating characteristic and survival analyses. Molecular docking and dynamics simulations validated the high binding affinity of sitosterol (a primary CMC component) to these targets. In vitro experiments demonstrated that sitosterol significantly inhibits DLBCL cell proliferation and induces apoptosis. In conclusion, our study elucidates the anti-tumor effects of CMC (mainly sitosterol) in DLBCL, mediated through the regulation of targets like CDK1 and CDK2. These findings provide critical evidence supporting the therapeutic potential of CMC in DLBCL treatment.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex molecular and immune landscape of HCC makes accurate patient stratification and personalized treatment essential. In this study, we utilized large-scale gene expression data from TCGA and GSE54236, alongside eQTL GWAS data, to identify key genes that influence HCC prognosis. Machine learning analysis was performed on the genes identified through Mendelian randomization (MR) and survival association analysis, using 101 algorithms to construct a robust prognostic model. A novel riskScore model was developed by integrating genetic, clinical, and immune cell infiltration data. The prognostic performance of model was validated through survival analysis, and its association with chemotherapy and immunotherapy sensitivity. The impact of key genes on the proliferation and invasion capabilities of HCC cells was assessed through Western blot (WB), EdU, and invasion assays. A total of 27 candidate genes associated with HCC survival were identified, with 16 genes categorized as high-risk. The riskScore model demonstrated excellent performance in stratifying patients into high-risk and low-risk groups, with C-index exceeding 0.7 for both TCGA and GSE54236 datasets. High-risk patients exhibited poorer prognosis and higher immune cell infiltration, particularly T cells and neutrophils. The model also predicted drug sensitivity, with high-risk patients showing greater sensitivity to chemotherapy agents like 5-Fluorouracil and Paclitaxel. Mutation analysis revealed that TP53 and MUC16 mutations were prevalent in high-risk groups, highlighting their role in HCC progression and therapeutic response. And the key gene SLC16A3 and STRBP can significantly promote the proliferation and invasion ability of HCC cells. Our riskScore model, integrating genetic and immune factors, provides a robust prognostic tool with potential clinical application in patient stratification and chemotherapy decision-making for HCC patients.

Aberrant activation/overexpression of RNF126 is implicated as a driving event in tumor progression. However, although some functions of RNF126 in prostate cancer (PCa) cell lines has been reported, more biological functions and in-depth mechanisms should be further clarified in PCa.

Many observational studies have identified a link between Epstein-Barr virus (EBV) and stomach conditions, such as gastric cancer (GC). This study investigated the causal relationship between EBV and GC and conditions that may lead to GC, such as gastritis and gastric ulcer. Data regarding GC were sourced from a FINN cohort; data regarding EBV were obtained from a previous study that relied on the UK Biobank cohort. Inverse-variance weighted (IVW) was used as a primary analysis; Mendelian randomization-Egger (MR-Egger), Weighted median (WM), and weighted model were applied to validate the robustness of the results. MR-Egger regression method was used to explore the presence of horizontal pleiotropy, and the MR pleiotropy residual sum and outlier (MR-PRESSO) method was applied to detect potential outliers. Cochran's Q test was used to test heterogeneity among instrumental variables (IVs). Genetic prediction linked EBV EBNA-1 antibody levels significantly with GC and gastritis, unaffected by horizontal pleiotropy. MR-PRESSO found no outliers for EBNA-1 and GC, but two for gastritis. Heterogeneity was noted in anti-EBV IgG and peptic ulcer, and EBNA-1 and VCA p18 antibodies for gastritis. The present MR analysis provides evidence supporting a causal role for genetically predicted EBNA-1 antibody levels in the etiology of GC. Taking EBV infection into account could help tailor the screening and diagnosis of GC to each patient.

MAPK inhibitors (MAPKi) have revolutionized the treatment of patients with advanced melanoma. However, primary and acquired resistance mechanisms limit their efficacy. Predicting MAPKi response from the tumor baseline features remains challenging due to the limited size of patient cohorts. Therefore, we collected data from nine different patient cohorts (total n = 417 patients with advanced melanoma treated with MAPKi) to identify clinical and molecular features. Our curated dataset, named MelanoDB, includes whole or partial exome sequencing data for 191 patients, copy number alteration information for 66 patients, and gene expression data for 132 patients. We provide a web application to explore the integrated dataset and data distribution across the collected studies, and we share this dataset with the scientific community according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.

HER2-targeted therapies have improved outcomes in metastatic gastric cancer (mGC), yet assessment of HER2 status in tumor tissues remains limited by heterogeneity and temporal changes. This study aimed to evaluate real-time HER2 expression on circulating tumor cells (CTCs) using the On-chip Sort system. CTCs were enriched from blood samples of 27 mGC patients, identified by cytokeratin staining, and assessed for HER2 expression via fluorescent labeling. The epithelial-mesenchymal transition (EMT) index was calculated based on co-expression of vimentin and cytokeratin. CTCs also underwent whole-genome amplification and targeted sequencing using a cancer gene panel. Patients were stratified into three groups: Group A (n = 13), HER2-positive in tissue; Group B (n = 8), tissue HER2-negative but CTC HER2-positive; and Group C (n = 6), HER2-negative in both tissue and CTCs. All patients received cytotoxic chemotherapy; only Group A received additional HER2-targeted therapy. Group B showed the poorest progression-free survival (PFS: 7.0 months), compared to Group A (15.7 months) and Group C (not reached). CTC HER2 expression correlated with EMT index; Groups A and B also exhibited higher EMT indices and shared EMT-related mutations. These findings suggest that CTC-based HER2 monitoring reflects tumor aggressiveness and may help identify patients who could benefit from HER2-targeted therapy despite negative tissue HER2 status.

Prohibitin2 (PHB2) is a mitochondrial endosomal protein that is closely linked to tumors; however, its specific molecular role remains unclear. Therefore, this study investigates the role of PHB2 in cancer. Multiple databases, including cBioPortal, PhosphoNET, and AlphaFold, were accessed to investigate this issue. The findings indicated that PHB2 expression in tumor tissues exceeded that in normal tissues, with PHB2 localized in mitochondria. Patients diagnosed with bladder and lung adenocarcinomas exhibiting elevated PHB2 expression demonstrated an increased survival rate compared to those with reduced expression levels. Conversely, the opposite was observed in patients with renal clear cell carcinoma and breast cancer. In pan-cancer, the mutation rate of PHB2 was elevated, with several overlapping amino acid sites exhibiting tumor mutations and post-translational modifications, including phosphorylation at tyrosine 121, acetylation at threonine 263, and ubiquitination at lysine 296. Multiple overlapping sites of amino acid phosphorylation modifications and mutations were identified in PHB2, specifically at threonine 42, tyrosine 121, and threonine 263. The three-dimensional predicted structure of PHB2 was analyzed utilizing the AlphaFold database. The pathogenicity mutation heatmap indicated that the overlapping sites of amino acid mutations and post-translational modifications, including phosphorylation (threonine 42, tyrosine 121, and threonine 263), acetylation, and ubiquitination (lysine 296), were significantly pathogenic. In conclusion, PHB2 exhibits cancer-type-specific prognostic associations. Its overlapping mutation and modification sites suggest potential functional importance, supporting its role as a candidate biomarker for further validation in pan-cancer contexts.

Pancreatic cancer stem cells (PCSCs) are a small population of cells in tumours that exhibit enhanced self-renewal and differentiation capabilities. CSCs proactively remodel the tumour microenvironment to maintain CSC stemness, which contributes to chemotherapy resistance. Compared with targeting PCSCs themselves, targeting the PCSC niche may be a novel strategy for pancreatic cancer (PC) therapy. Here, we found that DSG2, a member of the desmosomal cadherin family, is highly expressed in PCSCs. DSG2 upregulation is correlated with adverse outcomes in PC patients. DSG2 knockdown suppressed IL-4 and GM-CSF expression, which promoted the enrichment of tumour-associated macrophages to establish a supportive PCSC niche. Furthermore, we found that the IL-8/CXCR2 axis interacts with DSG2 to promote PCSC stemness and gemcitabine resistance by activating the Wnt/β-catenin pathway. These findings highlight the novel regulatory mechanism of DSG2 in PC, providing new targets for the development of therapeutics targeting PCSC niches.

mRNA modifications are vital in regulating cellular processes. Beyond N6-methyladenosine (m6A), most other internal mRNA modifications lack dedicated catalytic machinery and are typically introduced by tRNA-modifying enzymes. The distribution and stoichiometry of these modifications on mRNAs remain debated and require further validation. Furthermore, their precise function remains controversial due to the challenges of excluding the intricate combinational effects of tRNA modifications. Here, we biochemically validate that NSUN6, a tRNA structure-dependent methyltransferase, independently catalyzes 5-methylcytidine (m5C) formation with robust activity on mRNA by recognizing the CUCCA motif in a certain stem-loop structure. NSUN6 employs different strategies to recognize tRNA and mRNA substrates. By introducing mutations, we further separate its catalytic capabilities toward mRNA and tRNA revealing that NSUN6 promotes breast cancer cell migration depending on mRNA m5C modification. Mechanistically, a cohort of mRNAs involved in cell migration carries high levels of NSUN6-mediated site-specific m5C modification, thus being stabilized by the preferential binding of m5C readers YBX1 and YBX3. Moreover, introducing a single-site high-level m5C can significantly increase the stability of therapeutic mRNAs in cells. Our findings underscore the pivotal role of m5C-modified mRNAs in promoting breast cancer cell migration and their potential for therapeutic applications.

Cholangiocarcinoma (CCA) is a highly aggressive cancer that arises from the bile duct and has an extremely poor prognosis. Pemigatinib is the only Food and Drug Administration (FDA)-approved CCA-targeted drug. The CCA treatment options are insufficient considering its poor prognosis and increasing morbidity. Recently, Rho-associated coiled-coil containing protein kinase 2 (ROCK2) has been reported to promote resistance to chemotherapy. In this study, we investigated the role that ROCK2 plays in the development of resistance of CCA cells to Pemigatinib. Here, we developed Pemigatinib-resistant CCA cells, performed mRNA sequencing, retrieved The Cancer Genome Atlas (TCGA) data, and analysed ROCK2 expression in a large CCA cohort. The expression level of ROCK2 in CCA cells was significantly higher than that in adjacent noncancerous tissues. Increased expression of ROCK2 in CCA was related to a late TNM stage and decreased overall survival. Functional experiments revealed that downregulating the expression of ROCK2 promotes the ferroptosis of CCA cells, and enhances sensitivity to Pemigatinib. Moreover, upregulation of ROCK2 increased the expression of Drp1 protein. The effect of downregulating ROCK2 was reversed by Drp1 overexpression, and Drp1 knockdown inhibited Ferroptosis driven by ROCK2 overexpression. Mechanistically, ROCK2 stabilized the expression of Drp1 by competing with UBA52 to bind Drp1 and inhibiting the ubiquitination-mediated degradation of Drp1. Blocking of the UBA52- Drp1 axis inhibited the antitumour effect of Pemigatinib in ROCK2-knockdown cells both in vitro and in vivo. In conclusion, the ROCK2/UBA52/Drp1 axis is a pivotal driver of Pemigatinib resistance in CCA cells. These results provide novel insights into Pemigatinib resistance in CCA cells, suggesting that ROCK2 is a promising therapeutic target for the treatment of CCA.

Nasopharyngeal carcinoma (NPC) is the fourth-most-prevalent cancer in both Indonesia and Asia. Globally, an estimated 133,354 cases and 80,008 deaths were attributed to NPC in 2020. Early diagnosis plays a key role in managing NPC. Molecules found in bodily fluids, such as saliva, contain compounds (including microRNAs [miRNAs]) that can aid in detecting diseases like NPC. More studies on the expression, role, use, and accuracy of salivary miRNAs as potential diagnostic biomarkers of NPC are needed.

In ~70% of patients with pancreatic ductal adenocarcinoma, the TP53 gene acquires gain-of-function (GOF) mutations leading to rapid disease progression. Specifically, missense p53 (misp53) GOF mutations associate with therapy resistance and worse clinical outcomes. However, the molecular functions of distinct misp53 mutants in plasticity and therapy response remain unclear. Integrating multicenter patient data and multi-omics, we report that the misp53R273H/C mutant is associated with cell cycle progression and a basal-like state compared to the misp53R248W/Q mutant. Loss of misp53R273H/C decreased tumor growth and liver metastasis while prolonging survival in preclinical models. We found that misp53R273H/C specifically regulated the Rb/DREAM axis involved in cell cycle regulation. Notably, a clinical CDK4/6 inhibitor reduced misp53R273H/C mutant expression. However, it triggered MAPK/ERK-mediated resistance mechanisms, enhancing cell survival and resistance to CDK4/6 inhibitors. Combining MAPK/ERK and CDK4/6 inhibitors reduced misp53R273H/C-associated oncogenic functions. Thus, distinct misp53 mutants show unique cell-intrinsic plasticity, therapeutic vulnerabilities, and resistance mechanisms.

Neoadjuvant therapy has been widely used in breast cancer, but treatment response varies among individuals. We conducted multiomic profiling on tumor samples from 149 Chinese patients with breast cancer across ER-HER2+, ER+HER2+, and ER-HER2- subtypes, categorizing outcomes as pathologic complete response (pCR; n = 81) or residual disease (RD; n = 68). We identified distinct molecular features linked to pCR in each subtype: elevated cell proliferation in patients with ER-HER2- pCR, higher CDKN2A methylation in patients with ER-HER2- RD, increased KIT methylation in patients with ER-HER2+ RD, and MAP4K1 hypermethylation in patients with ER+HER2+ RD. These findings were subsequently validated in independent datasets. By integrating clinical and multiomic data, we developed MOPCR, a subtype-specific machine learning model that outperformed single-omic approaches in predicting treatment response. MOPCR demonstrated potential generalizability across cohorts and provided preliminary stratification of patient subgroups with higher pCR probability, offering valuable insights for precision cancer management.

Aberrant glycosylation is a hallmark of cancer biology, and altered glycosylation influences multiple facets of melanoma progression. To identify glycosyltransferases, glycans, and glycoproteins essential for melanoma maintenance, we conducted an in vivo growth screen with a pooled short hairpin RNA library of glycosyltransferases, lectin microarray profiling of benign nevus and melanoma samples, and mass spectrometry-based glycoproteomics. We found that α-2,3-sialyltransferases ST3GAL1 and ST3GAL2 and corresponding α-2,3-linked sialosides are up-regulated in melanoma compared to nevi and are essential for melanoma growth. Glycoproteomics revealed that glycoprotein targets of ST3GAL1 and ST3GAL2 are enriched in transmembrane proteins involved in growth signaling, including the amino acid transporter SLC3A2/CD98hc. CD98hc suppression mimicked the effect of ST3GAL1 and ST3GAL2 silencing, inhibiting melanoma cell proliferation. We found that both CD98hc protein stability and its prosurvival effect on melanoma are dependent upon α-2,3-sialylation mediated by ST3GAL1 and ST3GAL2. Our studies reveal α-2,3-sialosides functionally contributing to melanoma maintenance, supporting ST3GAL1 and ST3GAL2 as therapeutic targets in melanoma.

Malignant pleural effusion (MPE) is a common complication of advanced malignancies, requiring differentiation from benign pleural effusion for appropriate management. Cytology and biopsy have limitations, necessitating more sensitive, less invasive diagnostic techniques. The objective of this study was to evaluate the diagnostic accuracy of methylated SHOX2 (short-stature homeobox 2) and RASSF1A (Ras association domain family member 1A) genes in detecting MPE.

Colorectal cancer (CRC) is the second most common cancer type that results in significant mortality, the primary cause of which is associated with metastasis. Epithelial-mesenchymal transition (EMT) is a biological process that converts polarized epithelial cells into migratory mesenchymal states, which is positively correlated with the dissemination of the tumor cells from the primary tumor site and is thus linked to metastasis. Mesenchymal markers are the proteins that are up-regulated upon initiation of EMT. This review aims to provide a comprehensive overview of the role of mesenchymal markers in CRC metastasis. Upon thorough data mining, fibronectin, vimentin, N-cadherin, and β-catenin were defined as the distinguished mesenchymal markers that are well-studied in the context of CRC metastasis. Expression of these markers was positively correlated with aggressive CRC stages. However, the underlying molecular mechanisms through which they facilitate CRC progression are partly explored. Fibronectin was reported to affect cell migration and invasion via NF-kB/p53 and ITGA5/FAK-P/RhoGTPase axis, while its Extra Domain A (EDA) regulates the lymphangiogenesis via VEGF-C/PI3K/AKT axis. For vimentin and N-cadherin, few upstream regulators have been reported; however, the downstream pathways via which they affect migration and invasion remain to be explored. β-catenin, a well explored molecule for CRC onset, has limited reports in relation to metastatic progression. Wnt/β-catenin signalling has been reported to promote migration and invasion in primary CRC, while it impedes the same in advanced CRC background. There are future scopes for mechanistic research on the underexplored mesenchymal markers, whereas the mechanistically explored molecules need to be tested clinically.