Cancer Biomarkers

The association between Epstein-Barr virus (EBV) and lung cancer risk remains controversial. Here, we used a two-sample Mendelian randomization (MR) analysis to test the causal relationship between EBV and lung cancer.

Data regarding lung cancer (outcomes) were collected from the Finnish database; the Genome-Wide Association Study (GWAS) summary-level dataset for EBV was obtained from the Open Forum Infect Dis. The inverse-variance weighted (IVW) method was used as a primary analytical approach; weighted median, MR-Egger, and weighted mode methods were used to ensure the robustness of the data. The MR-Egger regression assessed horizontal pleiotropy, and the MR pleiotropy residual sum and outlier (MR-PRESSO) method identified potential outliers. Cochran’s Q test evaluated heterogeneity among instrumental variables (IVs).

IVW analysis indicated several significant causal effects. Genetically elevated levels of EBV ZEBRA and EBNA-1 antibodies increased the risk of SQC (OR=1.26 and OR=1.33, respectively). Increased EBNA-1 antibodies also raised the risk of overall lung cancer and small cell lung cancer. Conversely, higher VCA p18 antibody levels were associated with a decreased risk of lung adenocarcinoma (OR=0.70). Sensitivity analyses suggested these findings were robust, with no significant evidence of horizontal pleiotropy or heterogeneity.

Our data suggests a causal effect between EBV and the progression of lung cancer.

The main aim of this study is to identify prognostic biomarkers through integrating bioinformatics analysis in gastric cancer, which is a significant global health challenge.

Gene expression datasets related to blood, tissue, and saliva in gastric cancer were downloaded from the Gene Expression Omnibus (GEO) database and analyzed. The bioinformatics approaches included the identification of differentially expressed genes (DEGs) and enrichment analysis, as well as Kaplan-Meier Plotter survival analysis. The DEGs were also validated through The Cancer Genome Atlas (TCGA) database. Additionally, DEGs-associated lncRNAs and microRNAs were identified. Subsequently, Tumor and Immune System Interaction Database (TISIDB) was utilized to examine the correlation of the genes of interest with immune and molecular subtypes.

Twenty-six common DEGs were identified across blood, tissue, and saliva samples. Among them, 17 genes showed significant expression based on TCGA data. RAB23, LOX, ELL2, ELK3, CENPF, CD44, ANP32E, AKR1C2, and SMAD5 displayed significant association with patient survival. Particularly, ELL2 exhibit decreased expression in all specimens. The results indicated that ELL2 has a significant correlation with the immune system. The ELL2 gene regulates immune cell functions in gastric cancer, potentially influencing cancer immune responses, and tumor progression.

ELL2 downregulated expression and its correlation with survival across blood, tissue, and saliva samples using bioinformatics analysis underscores the necessity of more investigation to fully comprehend its function in cancer immunology.

Integrated bioinformatics approaches were used to identify stage-specific candidate genes and potential drug targets in triple-negative breast cancer (TNBC).

Microarray (164 early-stage, 33 advanced-stage, and 53 normal samples) and RNA-seq (113 normal, 163 early-stage, and 30 advanced-stage TNBC samples) datasets were analyzed. Differentially expressed genes (DEGs) were identified, followed by co-expression analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and protein–protein interaction analysis using the STRING database. miRNA co-regulation was evaluated using multiMiR and TCGA correlation analyses. Candidate genes were validated using UALCAN and immunohistochemistry data. Molecular docking assessed potential therapeutic agents.

Novel stage-specific candidate biomarkers were identified, including DNAJC6, SKP2, MOCOS, and NCAPD2 in early-stage TNBC, and F11R, FOXO6, PPP4C, and TMEM51 in advanced-stage TNBC. UALCAN analysis confirmed the dysregulation of these genes across 23 additional malignancies. STRING-based network analysis revealed stage-specific protein–protein interactions, including SKP2–SKP1 in early-stage and F11R–TJP1 in advanced-stage TNBC. miRNA co-regulation distinguished early-stage TNBC through PI3K–AKT–related pathways and advanced-stage TNBC through tumor progression–associated pathways. Docking-based drug repurposing highlighted conventional agents (e.g., doxorubicin) and potential novel candidates (e.g., sunitinib).

This study identifies novel stage-specific gene candidates and suggests repurposable drugs for TNBC, supporting progression-specific targeted therapeutic strategies.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Holocytochrome c synthase (HCCS), a mitochondrial enzyme involved in apoptosis and energy metabolism, has been implicated in tumorigenesis; however, its role in lung cancer is not well defined.

This study aimed to elucidate the prognostic and therapeutic potential of HCCS in lung cancer through integrative bioinformatics analyses.

Transcriptomic, methylation, and clinical data from TCGA were analyzed using TNMplot, UALCAN, and TIMER2.0 to evaluate HCCS expression, promoter methylation, immune infiltration, and prognostic relevance in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Meta-analysis of 18 independent cohorts from LUNG CANCER EXPLORER and four GEO datasets validated expression patterns, while Kaplan–Meier analysis assessed survival outcomes.

HCCS was significantly upregulated in LUAD and LUSC and showed promoter hypermethylation in LUAD. Meta-analysis and external validation confirmed its overexpression. Patients with low HCCS expression exhibited poorer overall survival, suggesting a potential tumor-suppressive effect. HCCS expression positively correlated with immune cell infiltration and co-expressed genes enriched in mitochondrial and apoptotic pathways.

HCCS may serve as a prognostic and therapeutic biomarker in lung cancer, linking mitochondrial regulation, epigenetic modification, and immune interactions.

miRNAs are of interest due to have been involved in cancers such as colorectal cancer (CRC).

To explore the expression levels of miRNA-34a and miRNA-135b and whether they can be potential biomarker for early diagnosis of CRC.

Fifteen pairs newly diagnosed colorectal cancer and normal involved in the measurement of tissue and serum levels of miRNAs 34a and 135b and their target genes of SIRT1 and DAPK-1. We assessed the predictive value of these miRNAs utilizing the receiver operating characteristic curve.

The findings have shown that the miRNA-34a expression was significantly downregulated and miRNA-135b was upregulated in CRC tissues in comparison to their expression in adjacent normal tissue. This was additionally observed in the serum of the same CRC cases when compared to individuals without CRC. Furthermore, we found a significant increase in the level of SIRT-1 and a significant reduction in the expression level of DAPK-1 in CRC tissues compared to the adjacent normal tissue. The value of AUC was considerably high for the timely detection of CRC.

We found these miRNAs acting as potential non-invasive biomarkers for the early detection of CRC and in assessing the CRC risk within the general population.

Colorectal cancer (CRC) remains a leading cause of cancer-related death worldwide. Although colonoscopy is effective screening method, its widespread adoption is hampered by poor compliance. Development of patient-friendly screening methods that are minimally invasive is of paramount importance to improve CRC screening participation.

A single-center case-control study was conducted. Blood samples were collected from 119 participants (79 CRC patients, 20 patients with colorectal adenomas, and 20 healthy colonoscopy-negative controls) during May 2023 and March 2025. The methylated SEPT9 (mSEPT9) DNA in peripheral blood was analyzed by droplet digital polymerase chain reaction (ddPCR).

The sensitivity and specificity of mSEPT9 detection in peripheral blood for diagnosis of all stages of CRC patients was 68.35% and 95.00%, respectively. The positive rate were similar between left-sided CRC and right-sided CRC (65.31% vs 72.41%，p ＞ 0.05). mSEPT9 detection in all healthy people with normal colonoscopy were negative. When mSEPT9 detection was combined with CEA and CA199, positive rate could be raised to 82.67%.

Blood-based mSEPT9 detection by ddPCR method is a sensitive and specific method for non-invasive diagnosis of CRC. Combination with CEA and CA19-9 could improve its performance. It may serve as a viable alternative for individuals unwilling or unable to undergo invasive procedures.

This study aimed to identify DNA repair–related genes causally affecting OCC risk by integrating multi-omics data using summary data–based Mendelian randomization (SMR).

SMR analyses were performed using GWAS, mQTL, eQTL, and pQTL data. Causal associations were assessed via SMR and HEIDI tests, and colocalization analysis identified shared genetic variants. Findings were validated in the FinnGen_OCC cohort.

This study identified 160 methylation sites (96 genes), 30 eQTLs, and 2 pQTLs linked to OCC. Colocalization confirmed 96 mQTLs, 20 eQTLs, and 2 pQTLs (GPN1, HNRNPAB). Five mQTLs (ANK1, HLA C, TERT, TUBB, VHL) replicated in FinnGen. Integration of mQTL and eQTL data highlighted VHL, TP73, and JMJD1C, implicating DNA methylation–mediated regulation. Multi-omics and tissue analyses identified VHL as the most consistent risk gene, supported by colocalization, TCGA HNSC transcriptomic validation (P < 0.01), and DepMap functional data. GEN1 and POU5F1 expression in salivary gland tissue also associated with OCC risk.

This multilayer SMR study reveals causal links between DNA repair–related genes and OCC, highlighting VHL as a key driver and suggesting targets for early detection and precision therapy.

This study aimed to evaluate the discriminatory performance of routinely accessible hematological, inflammatory, and nutritional biomarkers in differentiating patients with gastric cancer (GC) from healthy controls, and to examine their contribution within multivariable models after adjustment using Propensity Score Matching (PSM).

A retrospective analysis was conducted using laboratory and demographic data from 109 GC patients and 152 healthy controls between 2020 and 2025. To reduce age- and sex-related imbalance between groups, 1:1 PSM was performed. Receiver Operating Characteristic (ROC) curve analysis was used to assess discriminatory performance, and exploratory multivariable logistic regression models were constructed to identify independent marker combinations associated with GC.

After PSM, GC patients exhibited significantly lower hemoglobin, hematocrit, albumin, lymphocyte counts, and PNI values, alongside significantly higher platelet counts and inflammatory indices (SII, PLR, MLR, NLR, PIV) (all p<0.001). Among individual biomarkers, hemoglobin (AUC=0.830) and PLR (AUC=0.806) demonstrated the highest discriminatory performance. In exploratory multivariable models, reduced Hb, elevated PLT, low ALB, and low LYM were associated with GC in Model 1 (Nagelkerke R2=0.597; AUC=0.89), while PLR, PNI, and SII were associated with GC in Model 2 (Nagelkerke R2=0.492; AUC=0.839).

Routinely available hematologic, inflammatory, and nutritional markers may serve as practical adjunctive tools for pre-endoscopic risk stratification in gastric cancer. Prospective validation in clinically representative populations is needed before broader clinical integration.

Biopsy is the only definitive method for diagnosing pancreatic ductal adenocarcinoma(PDAC) in the preoperative setting. With the increasing use of neoadjuvant therapy, alternative diagnostic modalities would aid in cases where fine-needle aspiration(FNA) is inconclusive or challenging. We hypothesized that radiomic signatures generated from computed tomography(CT) scans can help diagnose PDAC with high fidelity and radiologic/radiomics features have utility in predicting clinical outcomes.

Patients with PDAC who underwent oncologic pancreatectomy from 2017–2019 were selected. Preoperative CT scans were reviewed by two body radiologists and two pancreatic surgeons. Tumors were segmented using two radiomics software platforms(Platform A&B).

Twenty-five patients were included (n = 20 PDAC,n = 5 pancreatic neuroendocrine tumors(pNET)). Neoadjuvant chemotherapy was administered in 80% of PDAC patients, and 60% underwent pancreaticoduodenectomy. Platform A extracted 455 independent radiomics features and Platform B extracted 2078 radiomics features corresponding to shape, texture, filter, and intensity. One principal component from the shape feature group showed significant discrimination between PDAC and pNET, within both Platform A and B(area under the curve(AUC) = 0.81,p = 0.038 and AUC=0.89,p = 0.017, respectively). Additionally, a gray-level co-occurrence matrix sum entropy feature identified with Platform B differentiated malignant pancreatic tissue from normal parenchyma and PDAC from normal gland(p < 0.05).

Radiologic/radiomic features are reliable tools for PDAC diagnosis and associate with clinical outcomes. An improved understanding of PDAC radiomics may have implications for prognosis, margin status, and treatment response assessments.

Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to delayed diagnosis and limited effectiveness of current biomarkers. Identifying candidate biomarkers through integrated gene expression analysis may enhance understanding of OC biology and support future diagnostic and prognostic investigations.

Four microarray datasets were retrieved from the Gene Expression Omnibus and analyzed using the limma package in R to identify differentially expressed genes (DEGs) through a bidirectional filter (|log₂FC| ≥ 1.2). Overlapping DEGs across all four datasets were subjected to Gene Ontology and KEGG pathway enrichment analyses. Protein-protein interaction networks were constructed using the STRING database, and hub genes were identified by consensus across three centrality algorithms in Cytoscape. Prognostic significance was evaluated using Kaplan-Meier survival analysis via GEPIA2; diagnostic performance was assessed using Youden Index-based ROC analysis; and external validation was performed using an independent dataset.

A total of 209 overlapping DEGs were identified, yielding 35 hub genes. Functional enrichment analysis implicated cell cycle dysregulation, metabolic reprogramming, and extracellular matrix remodeling as central biological processes. TRIP13, FGF13, and LYVE1 were significantly associated with overall survival, while ALDH1A1, GATA6, and WNT5A were associated with disease-free survival. KDR demonstrated the highest diagnostic accuracy, and external validation confirmed the generalizability of the diagnostic findings.

This study identifies a panel of candidate diagnostic and prognostic biomarkers in ovarian cancer, providing a molecular framework for future experimental validation and translational investigation.

This study investigated causal relationships and underlying mechanisms between serum DNA repair proteins and liver cancer to identify biomarkers for clinical management.

A two-sample bidirectional Mendelian randomization (MR) design was employed. We performed inverse variance weighted (IVW) analysis, followed by protein quantitative trait loci (pQTL), colocalization, and pathway enrichment analyses to explore biological mechanisms.

MR analysis revealed significant associations: NBR1 (IVW: OR = 2.28, 95%CI: 1.17–4.45, P = 0.015) and RAD51 (IVW: OR = 2.16, 95%CI: 1.12–4.15, P = 0.021) were risk factors. PARP11 was protective for hepatocellular carcinoma (OR = 0.48, 95%CI: 0.25–0.93, P = 0.030) but a risk for intrahepatic cholangiocarcinoma (OR = 1.86, 95%CI: 1.04–3.33, P = 0.038). Mechanistically, pQTL and colocalization identified rs2793568 as a key regulator of PARP1, which was enriched in base excision repair pathways. Sensitivity analyses confirmed the robustness of these findings.

Serum NBR1, RAD51, and PARP11 are potentially causal in liver cancer pathogenesis. Specifically, the genetic regulation of PARP1 highlights a critical DNA repair mechanism, supporting their utility as predictive biomarkers.

This study aimed to investigate whether the efficacy of neoadjuvant chemotherapy differs between HER2-low and HER2-zero subgroups in hormone receptor–positive, HER2-negative breast cancer.

A total of 61 patients who received neoadjuvant chemotherapy between 2015 and 2025 were retrospectively analyzed. HER2 status was determined from pre-treatment core biopsy specimens and classified as HER2-low (IHC 1 + or 2 + and FISH-negative) or HER2-zero (IHC 0). The pathological complete response (pCR) rates were compared between the two groups.

Among 61 patients, 28 had HER2-low and 33 had HER2-zero tumors. The pCR rate was 7.1% in the HER2-low group and 27.3% in the HER2-zero group (p = 0.042). A post-hoc power analysis based on these results showed a moderate power (0.59) to detect the observed difference.

HER2-low expression may negatively influence chemosensitivity in hormone receptor–positive breast cancer treated with neoadjuvant chemotherapy. Further studies are warranted to evaluate the potential role of trastuzumab deruxtecan (T-DXd) and other antibody–drug conjugates in this newly defined subgroup.

The abnormal expression patterns of genes associated with cell cycle control are essential contributors to the initiation and progression of bladder cancer (BC).

In this research, transcriptomic profiles sourced from The Cancer Genome Atlas (TCGA) were examined to pinpoint cell cycle-related genes with significant expression differences between early-stage (I–II) and advanced-stage (III–IV) BC. The identified BC samples were then randomly split into training and validation groups. A prognostic model containing nine key genes was subsequently constructed from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression algorithm, and its predictive reliability was independently confirmed in the validation dataset. Based on individual risk scores derived from this gene signature, patients were separated into high- and low-risk groups. Functional enrichment analyses (GO and KEGG), immune cell infiltration (CIBERSORT), and gene set variation analysis (GSVA) were performed to explore the biological functions and immune landscape associated with the risk signature.

Survival analysis revealed substantially worse overall survival (OS) in patients categorized as high-risk. The prognostic efficacy of the constructed gene signature was further corroborated by receiver operating characteristic (ROC) curve analyses. Functional enrichment analyses revealed that these genes were chiefly associated with critical biological processes such as cellular differentiation, proliferation, and migration, involving key signaling pathways like PI3K-Akt signaling, positive modulation of the MAPK cascade, and cell-matrix interactions. Immune infiltration analysis showed that tumors from patients in the high-risk group had increased infiltration by M2 macrophages. Furthermore, GSVA results indicated that stromal and angiogenic functions were enriched in the high-risk group.

These results highlight the potential clinical utility of the newly developed cell cycle-associated gene signature, providing effective biomarkers to facilitate personalized therapeutic decision-making for BC patients.