Visualizing and interpreting cancer genomics data via the Xena platform

Peripheral positioning of lysosomes supports melanoma aggressiveness

Emerging evidence suggests that the function and position of organelles are pivotal for tumor cell dissemination. Among them, lysosomes stand out as they integrate metabolic sensing with gene regulation and secretion of proteases. Yet, how their function is linked to their position and how this controls metastasis remains elusive. Here, we analyze lysosome subcellular distribution in patient-derived melanoma cells and patient biopsies and show that lysosome spreading scales with melanoma aggressiveness. Peripheral lysosomes promote matrix degradation and cell invasion which is directly linked to the lysosomal and cell transcriptional programs. Using chemo-genetical control of lysosome positioning, we demonstrate that perinuclear clustering impairs lysosome secretion, matrix degradation and invasion. Impairing lysosome spreading significantly reduces invasive outgrowth in two in vivo models, mouse and zebrafish. Our study provides a direct demonstration that lysosome positioning controls cell invasion, illustrating the importance of organelle adaptation in carcinogenesis and suggesting its potential utility for diagnosis of metastatic melanoma.

Epithelial and macrophage cell interaction in cervical cancer through single-cell RNA-sequencing and spatial analysis

Background

Cervical cancer (CC) is a major global health issue, ranking sixth in cancer-related mortality. The tumor microenvironment (TME) plays a crucial role in tumor growth. This study explored the cellular composition and immunological landscape of CC using various genomic data sources.

Methods

Data from the Cancer Genome Atlas and Gene Expression Omnibus were analyzed, including single-cell RNA sequencing, spatial transcriptome analysis, and survival data. Gene set variation analysis (GSVA) identified pathways in CD8+ cells, macrophages, and epithelial cells. Immunohistochemistry assessed marker expression in CC and normal tissues. Tumor immune dysfunction and exclusion (TIDE) scores differentiated high- and low-macrophage groups. Cell-cell communication analyses highlighted interactions between macrophages and epithelial cells.

Results

Macrophage markers correlated with overall survival (OS) and disease-free survival (DFS). Epithelial cell subgroups 1 and 4, along with CD8+ T cells, were associated with OS. TIDE scores varied between groups. Specific ligand-receptor interactions were found between macrophages and epithelial cell subgroup 1. Triptolide was effective in epithelial cell subgroup 1, while memantine was more effective in macrophages.

Conclusion

Epithelial-macrophage interactions in the TME are crucial for CC progression and treatment, offering a potential immunotherapeutic strategy.

Molecular signatures bidirectionally link myocardial infarction and lung cancer

Myocardial Infarction (MI) and lung cancers are major contributors to mortality worldwide. While seemingly diverse, the two share common risk factors, such as smoking and hypertension. There is a pressing need to identify bidirectional molecular signatures that link MI and lung cancer, in order to improve clinical outcomes for patients. In this study, we identified common differentially expressed genes between MI and lung cancer. Specifically, we identified 1,496 upregulated and 1,482 downregulated genes in the MI datasets. By focusing on the 1,000 most upregulated and downregulated genes in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), we identified 35 genes that are common across MI, LUAD, and LUSC. Functional enrichment analysis revealed shared biological processes, such as "inflammatory response" and "cell differentiation." The Cox proportional hazards model demonstrated a significant association between the shared genes and overall survival in lung cancer patients, as well as with smoking history in these patients. In addition, a machine learning model based on the expression of the shared genes distinguished MI patients from controls, achieving an AUROC of 0.72 and an AUPRC of 0.86. Finally, based on drug repurposing analysis, we proposed FDA-approved drugs potentially targeting the upregulated genes as novel therapeutic options for the co-occurring conditions of MI and lung cancer. Overall, our findings highlight the similarities in molecular makeup between lung cancer and MI.

METTL16-dependent miR-146b-5p m6A modification remodeling sensitize NSCLC to osimertinib via activating PI3K/AKT signaling

BACKGROUND

Non-small-cell lung cancer (NSCLC) is one of the most common malignant tumors, with poor prognosis and increasing osimertinib therapy resistance. Revealing mechanisms of NSCLC progression and therapy resistance remains critical. The aim of this study was to elucidate the molecular mechanism of miR-146b-5b-5p m6A modification and underlying function in regulating the proliferation and osimertinib resistance of NSCLC.

METHODS

TCGA, GEO datasets were used to analyze the differential expression of miR-146b-5p in NSCLC and adjacent tissues, and its impact on prognosis. Then the effects of miR-146b-5p on the proliferation and osimertinib of A549 and HCC827 cells were investigated through proliferation experiments, colony formation assay and IC50 assay. The regulatory mechanism of miR-146b-5p on the PI3K/AKT signaling pathway and its interaction in cancer progression were investigated through Western blots, dual-luciferase reporter assay, and rescue experiments.

RESULTS

miR-146b-5p was significantly upregulated in NSCLC tissue and represented worse prognosis. miR-146b-5p mimic significantly enhanced proliferation and osimertinib resistance, while miR-146b-5p inhibitor inhibited above phenotype. Through bioinformatic analysis and experimental results, miR-146b-5p interacted directly with PTEN mRNA and activated subsequent signaling pathway activation. PI3K/AKT inhibitor could eliminate the tumorigenic effects of miR-146b-5p mimic on the progression of NSCLC, while PI3K/AKT agonist could rescue the inhibition effect of miR-146b-5p inhibitor group cells. Further, methyltransferase METTL16 is responsible for miR-146b m6A modification. Modified miR-146b-5p promotes osimertinib resistance through downstream PI3K/AKT activation.

CONCLUSIONS

In summary, we found that METTL16 mediated miR-146b-5p m6A modification promoted the proliferation and osimertinib resistance of NSLCL by activating PI3K/AKT signaling pathway. Our study is expected to provide a novel insight and potential therapeutic target for NSCLC osimertinib resistance.

Comprehensive bioinformatics analysis identified HMGB3 as a promising immunotherapy target for glioblastoma multiforme

OBJECTIVE

Glioblastoma multiforme (GBM) presents significant therapeutic challenges due to its heterogeneous tumorigenicity, drug resistance, and immunosuppression. Although several molecular markers have been developed, there still lack of sensitive molecular for accurately detection. Studying the mechanisms underlying the development of GBM and finding relevant prognostic biomarkers remains crucial.

METHODS

Single-cell RNA sequencing, bulk RNA-seq, and cancer immune cycle activities of GBM were used to assess the expression of different molecular related to GBM. Bioinformatics analyses were carried to evaluate the functional of the high mobility group protein B3 (HMGB3) in GBM.

RESULTS

HMGB3 was highly expressed in GBM tissues and influenced the interpatient and intratumoral transcriptomic heterogeneity as well as immunosuppression in GBM. HMGB3 also contributes to a no inflamed tumor microenvironment (TME) and has an inhibitory effect on tumor-associated immune cell infiltration. Besides, HMGB3 participated GBM chemotherapeutic sensitivity and negative correlation with 140 medicines.

CONCLUSION

HMGB3 as a heterogeneous and immunosuppressive molecule in the GBM TME, making it a potential target for precision therapy for GBM.

Computational insights into exploring the potential effects of environmental contaminants on human health

With rapid industrialization and urbanization, the increasing prevalence of air and water pollutants poses a significant threat to public health. Traditional research methods, such as epidemiological studies and in vitro/in vivo experiments, provide valuable biological insights but are often costly, time-consuming, and limited in scale. To address this gap, this study develops a machine learning-based approach to predict the carcinogenicity of pollutants. Using the dataset of carcinogenic and non-carcinogenic molecules that we collected, the pretrained KPGT model trained with molecular fingerprints and descriptors achieved an AUC of 0.83, surpassing traditional machine learning models. To validate this model, common pollutants from air and water sources were analyzed. Further clustering classified these pollutants into five distinct groups. Target prediction analysis identified key genes associated with representative pollutant molecules, such as MAPK1, MTOR, and PTPN11. GO and KEGG pathway analyses, along with survival analysis, revealed potential carcinogenic mechanisms and prognostic implications. Our findings contribute to improved pollution risk assessment and evidence-based environmental policy development, ultimately aiding in the mitigation of pollutant-related health risks.

Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer

BACKGROUND

Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensively evaluates LOX/LOXL expression in cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance.

METHODS

We assessed the association of LOX/LOXL expression with survival and drug sensitivity via analyzing public datasets (including bulk and single-cell RNA sequencing data of six datasets from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA) and Cancer Genome Atlas Program (TCGA)). We performed comprehensive machine learning-based bioinformatics analyses, including unsupervised consensus clustering, a total of 10 machine-learning algorithms for prognostic prediction and the Connectivity map tool for drug sensitivity prediction.

RESULTS

The clinical significance of the LOX/LOXL family was evaluated across 33 cancer types. Overexpression of LOX/LOXL showed a strong correlation with tumor progression and poor survival, particularly in glioma. Therefore, we developed a novel prognostic model for glioma by integrating LOX/LOXL expression and its co-expressed genes. This model was highly predictive for overall survival in glioma patients, indicating significant clinical utility in prognostic assessment. Furthermore, our analysis uncovered a distinct LOXL2-overexpressing malignant cell population in recurrent glioma, characterized by activation of collagen, laminin, and semaphorin-3 pathways, along with enhanced epithelial-mesenchymal transition. Apart from glioma, our data revealed the role of LOXL3 overexpression in macrophages and in predicting the response to immune checkpoint blockade in bladder and renal cancers. Given the pro-tumor role of LOX/LOXL genes in most analyzed cancers, we identified potential therapeutic compounds, such as the VEGFR inhibitor cediranib, to target pan-LOX/LOXL overexpression in cancer.

CONCLUSIONS

Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma.

Identification of necroptosis & mitophagy-related key genes and their prognostic value in colorectal cancer

BACKGROUND

Our study aimed to elucidate the potential necroptotic&mitophagy-related key genes in colorectal cancer (COAD) by bioinformatics analysis and identify their prognostic value in COAD.

METHODS

Firstly, we integrated the cancer genome atlas (TCGA) and gene expression omnibus (GEO) datasets to identify necroptosis & mitophagy-related differentially expressed genes (N&MRDEGs) in COAD using "TCGAbiolinks" and "GEOquery" packages. Secondly, the obtained data were used for differential expression analysis using the "limma" package, and further functional enrichment analysis using the "clusterProfiler" package. Then, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to explore pathway associations of the N&MRDEGs. Thirdly, the predictive model was developed utilizing LASSO (Least absolute shrinkage and selection regression) regression implemented through the "glmnet" package and validated via Kaplan-Meier analysis. Finally, we validated the function of the key genes by receiver operating characteristic (ROC) curve analysis, multivariate cox proportional hazards model and COAD cell lines.

RESULTS

There is a strong association between the 4 key genes (UCHL1, HSPA1A, MAPK8, and PLEC) of COAD and the necroptotic&mitophagy, which were found to be lowly mRNA level in COAD cell lines. Among them, PLEC exhibited a pronounced contribution to the utility of the model in the TCGA database and UCHL1 has excellent diagnostic potential with an area under the curve (AUC) greater than 0.9.

CONCLUSIONS

The perspective of bioinformatics analysis provides robust evidence suggested that UCHL1, HSPA1A, MAPK8, and PLEC genes are the prognostic biomarkers of COAD, the predictive model established herein provides a novel tool for risk stratification in clinical practice and serves as a foundation for further investigation into its underlying molecular mechanisms.

Integrative Bioinformatic Analysis of Cellular Senescence Genes in Ovarian Cancer: Molecular Subtyping, Prognostic Risk Stratification, and Chemoresistance Prediction

Background: Ovarian cancer (OC) is a heterogeneous malignancy associated with a poor prognosis, necessitating robust biomarkers for risk stratification and therapy optimization. Cellular senescence-related genes (CSGs) are emerging as pivotal regulators of tumorigenesis and immune modulation, yet their prognostic and therapeutic implications in OC remain underexplored. Methods: We integrated RNA-sequencing data from TCGA-OV (n = 376), GTEx (n = 88), and GSE26712 (n = 185) to identify differentially expressed CSGs (DE-CSGs). Consensus clustering, Cox regression, LASSO-penalized modeling, and immune infiltration analyses were employed to define molecular subtypes, construct a prognostic risk score, and characterize tumor microenvironment (TME) dynamics. Drug sensitivity was evaluated using the Genomics of Drug Sensitivity in Cancer (GDSC)-derived chemotherapeutic response profiles. Results: Among 265 DE-CSGs, 31 were prognostic in OC, with frequent copy number variations (CNVs) in genes such as STAT1, FOXO1, and CCND1. Consensus clustering revealed two subtypes (C1/C2): C2 exhibited immune-rich TME, elevated checkpoint expression (PD-L1, CTLA4), and poorer survival. A 19-gene risk model stratified patients into high-/low-risk groups, validated in GSE26712 (AUC: 0.586-0.713). High-risk patients showed lower tumor mutation burden (TMB), immune dysfunction, and resistance to Docetaxel/Olaparib. Six hub genes (HMGB3, MITF, CKAP2, ME1, CTSD, STAT1) were independently predictive of survival. Conclusions: This study establishes CSGs as critical determinants of OC prognosis and immune evasion. The molecular subtypes and risk model provide actionable insights for personalized therapy, while identified therapeutic vulnerabilities highlight opportunities to overcome chemoresistance through senescence-targeted strategies.

An antibody-dependent cellular phagocytosis-related gene signature predicts survival and response to immunotherapy in stomach adenocarcinoma

Antibody-dependent cellular phagocytosis (ADCP) is an immune biological process and plays a biological role in the clearance of tumor cells and the response to immune checkpoint inhibitors. However, the effects of ADCP on stomach adenocarcinoma (STAD) remain unclear. Clinical and genomic data were extracted from multiple datasets. The ADCP-related signature was established using Cox least absolute shrinkage and selection operator regression. Expression of the C5a receptor also known as complement component 5a receptor 1 in the tumor and adjacent-normal tissues was calculated using immunohistochemistry staining. Validation of the signature was conducted in the training and validation cohorts by Cox regression and log-rank tests. Furthermore, the immune infiltrates, the tumor immune dysfunction and exclusion score, and tumor mutation burden score were calculated using the corresponding algorithms, and Mann-Whitney U tests were used to evaluate the differences between groups. Seventy-three hub genes with predictive performance were identified to establish an ADCP-related signature. Accordingly, a 27-gene signature was established, C5a receptor also known as complement component 5a receptor 1, one of the signature genes, had higher expression in tumors than adjacent-normal samples, and its predictive performance was validated in the GSE84437 and The Cancer Genome Atlas cohorts. We found that the ADCP-related signature is an excellent prognostic predictor of STAD. Moreover, the molecular characteristics and some indices of response to immunotherapy differed between the high- and low-risk groups. We constructed a 27-gene signature that is associated with the prognosis and response to STAD-based immunotherapy and provide insights into the biological mechanisms underlying this predictive function.

Integrating bulk and single-cell RNA sequencing reveals SH3D21 promotes hepatocellular carcinoma progression by activating the PI3K/AKT/mTOR pathway

As a novel genetic biomarker, the potential role of SH3D21 in hepatocellular carcinoma remains unclear. Here, we decipher the expression and function of SH3D21 in human hepatocellular carcinoma. The expression level and clinical significance of SH3D21 in hepatocellular carcinoma patients, the relationship between SH3D21 and the features of tumor microenvironment (TME) and role of SH3D21 in promoting hepatocellular carcinoma progression were analyzed based on the bulk samples obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Single-cell sequencing samples from Gene Expression Omnibus (GEO) database were employed to verify the prediction mechanism. Additionally, different biological effects of SH3D21 on hepatocellular carcinoma cells were investigated by qRT-PCR, CCK-8 assay, colony forming assay and Western blot analysis. Bioinformatics analysis and in vitro experiments revealed that the expression level of SH3D21 was up-regulated in hepatocellular carcinoma and correlated with the poor prognosis in hepatocellular carcinoma patients. SH3D21 effectively promoted the proliferation, invasion, and migration as well as the formation of immunosuppressive microenvironment of hepatocellular carcinoma. In addition, SH3D21 can activate the PI3K/AKT/mTOR signaling pathway. SH3D21 stimulates the progression of hepatocellular carcinoma by activating the PI3K/AKT/mTOR signaling pathway, and SH3D21 can serve as a prognostic biomarker and therapeutic target for hepatocellular carcinoma.

Transcript-targeted antigen mapping reveals the potential of POSTN splicing junction epitopes in glioblastoma immunotherapy

Tumor antigens are crucial for T-cell mediated immunotherapy, but identified antigens for gliomas remain limited. Aberrant splicing variants are commonly expressed in tumors, resulting in unique tumor isoforms with potential antigenic properties. Herein, we analyzed multi-omics data from 587 glioma patients and assembled a library of putative tumor-enriched isoform antigens (TIA) and corresponding peptides presented on each HLA-I allele. We constructed an individual-specific TIA peptide candidate repertoire for each patient based on their TIA expression and HLA-I haplotypes. TIAs were highly expressed, enriched with glioma malignancy, and demonstrated strong HLA-binding affinity. We focused on periostin isoform-203 (POSTN-203), which was associated with poor survival of patients and contained multiple predicted HLA-restricted peptide epitopes. A selected HLA-A11-restricted peptide from POSTN-203 (POSTN-203A11) induced antigen-specific T-cell responses against both peptide-pulsed and POSTN-203-expressing glioma cells in an HLA-specific manner. Our findings highlight TIAs as a promising source of immunogenic antigens and POSTN-203 as a potential promising target for glioma immunotherapy.

Immune Clustering Reveals Molecularly Distinct Subtypes of Lung Adenocarcinoma

Background/objectives: Lung adenocarcinoma, the most prevalent type of non-small cell lung cancer, consists of two driver mutations in KRAS or EGFR. These mutations are generally mutually exclusive and biologically and clinically different. In this study, we aimed to test if lung adenocarcinoma tumors could be separated by their immune profiles using an unsupervised machine learning method. The underlying assumption was that differences in the immune response to tumors are characteristic of tumor subtypes. Methods: RNA-seq data were projected into inferred immune profiles. Unsupervised learning was used to divide the lung adenocarcinoma population based on their projected immune profiles. Results: The patient population was divided into three subgroups, one of which appeared to contain mostly EGFR patients. The tumors in the different clusters significantly differed in their expression of some of their known immune checkpoints (TIGIT, PD-1/PD-L1, and CTLA4). Discussion: We argue that EGFR mutations in each subgroup are immunologically different, which implies a distinct tumor microenvironment and might relate to the relatively high resistance of EGFR-positive tumors to immune checkpoint inhibitors. However, we cannot make the same claim about KRAS mutations.

ABCB1 confers resistance to carboplatin by accumulating stem-like cells in the G2/M phase of the cell cycle in p53null ovarian cancer

High-grade ovarian serous carcinoma, mostly bearing the various mutations in the TP53 gene, typically relapses within six months after first-line therapy due to chemoresistance, with a median overall survival of less than a year. However, the molecular mechanisms of action behind acquired drug resistance, particularly in relation to different TP53 mutation types, have not been fully elucidated. In this study, we demonstrated that acquired resistance to carboplatin in SKOV3 harboring a p53null mutation, but not in OVCAR3 with a p53R248Q, induces a significant portion of cells accumulated in the G2/M phase of the cell cycle, where cells highly expressed stemness marker with elevated proliferative capacity, which we believe was reversed by ABCB1 inhibition to the levels observed in non-resistant parental cells. ABCB1 suppression re-sensitized carboplatin-resistant cells to additional genotoxic stress and reduced their proliferative ability by recovering DNA repair activity and lowering stemness-like features, especially in the G2/M-distributed fraction. This suggests that high levels of stemness and attenuated DNA repair function exhibited in the G2/M-accumulated portion may be a key contributor of chemoresistance in patients with ovarian cancer bearing a p53null mutation, but not other types of mutations expressing p53. Furthermore, the inhibition of ΔNp73 resulted in the suppression of ABCB1, which consequently restricted cell growth in carboplatin-resistant SKOV3, suggesting that the ΔNp73 may act as an upstream regulator of the ABCB1. Notably, combinatorial treatment of carboplatin with the p53 reactivator, APR-246, proved effective in overcoming chemoresistance in OVCAR3 with the p53R248Q. Our findings suggest that the ΔNp73-ABCB1 axis is a promising molecular target for carboplatin-resistant ovarian cancers harboring p53null mutations, which we uncovered could be utilized to increase the efficacy of conventional anti-cancer therapies, to develop more efficient combinatorial therapeutic interventions directed toward overcoming the chemoresistance and improving the survival rates in patients with ovarian cancer.

Identification of a TNIK-CDK9 Axis as a Targetable Strategy for Platinum-Resistant Ovarian Cancer

Up to 90% of patients with high-grade serous ovarian cancer (HGSC) will develop resistance to platinum-based chemotherapy, posing substantial therapeutic challenges due to a lack of universally druggable targets. Leveraging BenevolentAI's artificial intelligence (AI)-driven approach to target discovery, we screened potential AI-predicted therapeutic targets mapped to unapproved tool compounds in patient-derived 3D models. This identified TNIK, which is modulated by NCB-0846, as a novel target for platinum-resistant HGSC. Targeting by this compound demonstrated efficacy across both in vitro and ex vivo organoid platinum-resistant models. Additionally, NCB-0846 treatment effectively decreased Wnt activity, a known driver of platinum resistance; however, we found that these effects were not solely mediated by TNIK inhibition. Comprehensive AI, in silico, and in vitro analyses revealed CDK9 as another key target driving NCB-0846's efficacy. Interestingly, TNIK and CDK9 co-expression positively correlated, and chromosomal gains in both served as prognostic markers for poor patient outcomes. Combined knockdown of TNIK and CDK9 markedly diminished downstream Wnt targets and reduced chemotherapy-resistant cell viability. Furthermore, we identified CDK9 as a novel mediator of canonical Wnt activity, providing mechanistic insights into the combinatorial effects of TNIK and CDK9 inhibition and offering a new understanding of NCB-0846 and CDK9 inhibitor function. Our findings identified the TNIK-CDK9 axis as druggable targets mediating platinum resistance and cell viability in HGSC. With AI at the forefront of drug discovery, this work highlights how to ensure that AI findings are biologically relevant by combining compound screens with physiologically relevant models, thus supporting the identification and validation of potential drug targets.

DCTPP1: A promising target in cancer therapy and prognosis through nucleotide metabolism

Deoxycytidine triphosphate pyrophosphatase 1 (DCTPP1) is an important deoxycytidine triphosphate (dCTP) hydrolase responsible for eliminating noncanonical dCTP and maintaining deoxyribonucleoside triphosphate (dNTP) pool homeostasis. This regulation is vital for proper DNA replication and genome stability. Emerging evidence highlights the considerable role of DCTPP1 in tumor progression, chemotherapy resistance, and prognostic prediction. Consequently, DCTPP1 has emerged as a promising nucleotide metabolism-related target for cancer therapy. In this review, we provide a comprehensive summary of the structural and cellular biological features of DCTPP1, its functions, and its role in cancer. In addition, we discuss recent advancments in small molecules targeting DCTPP1, and propose potential directions for future research.

The NLRP1 Emerges as a Promising Therapeutic Target and Prognostic Biomarker Across Multiple Cancer Types: A Comprehensive Pan-Cancer Analysis

INTRODUCTION

Nod-like receptor family pyrin domain containing 1 (NLRP1) serves as the central component of the inflammasome complex and has emerged as a potential contributor to cancer development. Despite accumulating evidence, a comprehensive assessment of NLRP1 across various cancer types has yet to be undertaken.

METHODS

Several databases have evaluated NLRP1 expression across various cancer types in The Cancer Genome Atlas (TCGA). Additionally, studies have investigated the correlation between NLRP1 and various survival metrics, infiltration of cancer-associated fibroblasts, genetic alterations, drug sensitivity, and promoter methylation. Furthermore, research has explored the potential roles of NLRP1 and its interactions with other proteins.

RESULTS

Our analysis revealed decreased expression of NLRP1 in BLCA, BRCA, KICH, LUAD, LUSC, PRAD, and UCEC tumor tissues compared to normal tissues. We identified a significant correlation between NLRP1 expression and various cancer survival parameters, genetic mutations, and immune infiltration of cancer-associated fibroblasts. Furthermore, we observed that NLRP1 expression is regulated by promoter DNA methylation in ESCA. Abnormal expression of NLRP1 was associated with decreased sensitivity to multiple anti-tumor drugs and small compounds. NLRP1 was found to be involved in pathways associated with T cell receptors and chemokines.

CONCLUSIONS

Reduced NLRP1 expression contributes to cancer progression and holds potential as a crucial biomolecular marker for diagnostic, prognostic, and personalized therapeutic interventions across different malignancies.

Unravelling Cancer Immunity: Coagulation.Sig and BIRC2 as Predictive Immunotherapeutic Architects

Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals a significant positive correlation between coagulation scores and immune-related gene expression across 30 diverse cancer types. Notably, tumours exhibiting high coagulation scores demonstrated enhanced infiltration of cytotoxic immune cells, including CD8+ T cells, natural killer (NK) cells, and macrophages. Leveraging the TCGA pan-cancer database, we developed the Coagulation.Sig model, a sophisticated predictive framework utilising a coagulation-related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis of ten ICI-treated cohorts, we identified and validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 and IFNG, which form the foundation of our predictive model. Functional analyses revealed that low-risk tumours characterised by higher immune cell populations, particularly CD8+ T cells, demonstrated superior ICI responses. These tumours also exhibited increased mutation rates, elevated neoantigen loads, and greater TCR/BCR diversity. Conversely, high-risk tumours displayed pronounced intratumor heterogeneity (ITH) and elevated NRF2 pathway activity, mechanisms strongly associated with immune evasion. Experimental validation highlighted BIRC2 as a promising therapeutic target. Targeted BIRC2 knockdown, when combined with anti-PD-1 therapy, significantly suppressed tumour growth, enhanced CD8+ T cell infiltration, and amplified IFN-γ and TNF-α secretion in tumour models. Our findings position the Coagulation.Sig model as a novel, comprehensive approach to personalised cancer treatment, with BIRC2 emerging as both a predictive biomarker and a potential therapeutic intervention point.

TCGEx: a powerful visual interface for exploring and analyzing cancer gene expression data

Analyzing gene expression data from the Cancer Genome Atlas (TCGA) and similar repositories often requires advanced coding skills, creating a barrier for many researchers. To address this challenge, we developed The Cancer Genome Explorer (TCGEx), a user-friendly, web-based platform for conducting sophisticated analyses such as survival modeling, gene set enrichment analysis, unsupervised clustering, and linear regression-based machine learning. TCGEx provides access to preprocessed TCGA data and immune checkpoint inhibition studies while allowing integration of user-uploaded data sets. Using TCGEx, we explore molecular subsets of human melanoma and identify microRNAs associated with intratumoral immunity. These findings are validated with independent clinical trial data on immune checkpoint inhibitors for melanoma and other cancers. In addition, we identify cytokine genes that can be used to predict treatment responses to various immune checkpoint inhibitors prior to treatment. Built on the R/Shiny framework, TCGEx offers customizable features to adapt analyses for diverse research contexts and generate publication-ready visualizations. TCGEx is freely available at https://tcgex.iyte.edu.tr , providing an accessible tool to extract insights from cancer transcriptomics data.

EFTUD2 is a promising diagnostic and prognostic indicator involved in the tumor immune microenvironment and glycolysis of lung adenocarcinoma

Background

Elongation Factor Tu GTP Binding Domain Containing 2 (EFTUD2), a conserved spliceosomal GTPase, is involved in craniofacial development and various cancers, but its role in lung adenocarcinoma (LUAD) remains unclear.

Methods

EFTUD2 expression in LUAD tissues was analyzed using data from TCGA and GEO, and validated by immunohistochemistry, RT-qPCR, and Western blotting. The relationship between EFTUD2 expression and clinical features was examined using Fisher's exact test. Diagnostic and prognostic analyses were performed in R. Hub genes related to EFTUD2 were identified through topological algorithms, and immune infiltration was assessed using CIBERSORT. The cGAS-STING pathway and m6A modification were also analyzed in the TCGA LUAD cohort. Functional assays were conducted to assess EFTUD2's impact on LUAD cell proliferation, cell cycle, invasion, and metastasis, while glycolytic enzyme levels were measured by Western blotting.

Results

EFTUD2 was upregulated in LUAD tissues and cells, correlating with N classification, visceral pleural invasion, intravascular tumor embolism, and cytokeratin-19 fragment antigen 21-1. Sixteen EFTUD2-related hub genes were identified. Higher EFTUD2 expression was linked to altered immune cell infiltration, with increased TumorPurity scores and decreased StromalScore, ImmuneScore, and ESTIMATEScore values. Gene enrichment analyses highlighted EFTUD2's involvement in cell adhesion, immune response. EFTUD2 was strongly associated with the cGAS-STING pathway and m6A modification. EFTUD2 knockdown inhibited LUAD cell proliferation, migration, and tumorigenicity, causing G0/G1 phase cell cycle arrest, and altered glycolytic enzyme expression. These findings may suggest that EFTUD2 positively regulates the progression of LUAD and modulates the glycolytic activity of tumor cells, making it valuable for LUAD treatment and prognosis.

Conclusions

EFTUD2 is a potential diagnostic and prognostic marker for LUAD, associated with immune infiltration, the tumor microenvironment, the cGAS-STING pathway, m6A modification, and glycolysis.

A Cuproptosis-Related gene Signature as a Prognostic Biomarker in Thyroid Cancer Based on Transcriptomics

Thyroid cancer (THCA) is the most prevalent endocrine tumor, and its incidence continues to increase every year. However, the processes underlying the aggressive progression of thyroid cancer are unknown. We concentrated on the prognostic and biological importance of thyroid cancer cuproptosis-related genes in this investigation. Genomic and clinical data were obtained from the UCSC XENA website, and cuproptosis-related genes were obtained from the FerrDb website. We performed differential expression analysis and Cox regression analysis to identify possible predictive targets associated with thyroid cancer prognosis. To assess the role of CDKN2A in thyroid cancer and the ability to predict prognosis on the basis of the CDKN2A expression level, we performed immunohistochemical staining, survival analysis, immunological analysis, functional analysis, and clinical analysis with respect to CDKN2A gene expression. CDKN2A expression levels were found to be inversely correlated with thyroid cancer prognosis. Higher levels of CDKN2A expression were associated with higher T, N, and clinicopathological stage and more residual tumor cells. Through univariate and multivariate Cox regression analyses, the CDKN2A expression level was shown to be linked with thyroid cancer patients' overall survival (OS). Moreover, we discovered that CDKN2A expression was linked to a dysfunctional tumor immune microenvironment. The study shows that CDKN2A, a cuproptosis-related gene, can be used as a prognostic marker for thyroid cancer.

ALKBH4 functions as a hypoxia-responsive tumor suppressor and inhibits metastasis and tumorigenesis

PURPOSE

The human AlkB homolog (ALKBH) dioxygenase superfamily plays a crucial role in gene regulation and is implicated in cancer progression. Under hypoxic conditions, hypoxia-inducible factors (HIFs) dynamically regulate methylation by controlling various dioxygenases, thereby modulating gene expression. However, the role of hypoxia-responsive AlkB dioxygenase remains unclear.

METHODS

The molecular events were examined using real-time PCR and Western blot analysis. Tumor cell aggressiveness was evaluated through migration, invasion, MTT, trypan blue exclusion, and colony formation assays. In vivo metastatic models and xenograft experiments were conducted to evaluate tumor progression.

RESULTS

Here, we examined the expression of the ALKBH superfamily under hypoxic conditions and found that ALKBH4 expression was negatively regulated by hypoxia. Knockdown of ALKBH4 enhanced the epithelial-mesenchymal transition (EMT), cell migration, invasion, and growth in vitro. The silencing of ALKBH4 enhanced metastatic ability and tumor growth in vivo. Conversely, overexpression of ALLKBH4 reversed these observations. Furthermore, overexpression of ALKBH4 significantly reversed hypoxia/HIF-1α-induced EMT, cell migration, invasion, tumor metastasis, and tumorigenicity. Notably, high expression of ALKBH4 was associated with better outcomes in head and neck cancer and breast cancer patients. Enrichment analysis also revealed that ALKBH4 was negatively enriched in hypoxia-related pathways. Clinically, a negative correlation between ALKBH4 and HIF-1α protein expression has been observed in tissues from both head and neck cancers and breast cancers.

CONCLUSION

These findings collectively suggest that ALKBH4 acts as a tumor suppressor and holds therapeutic potential for hypoxic tumors.

Ramipril, perindopril and trandolapril as potential chemosensitizers in ovarian cancer: Considerations for drug repurposing

Ovarian cancer (OC) has poor survival statistics and increasing prevalence. One of the new options for its therapy could be overcoming platinum resistance. In this review, we have considered the idea of repositioning angiotensin-converting enzyme inhibitors (ACE-Is) as chemosensitizers. These drugs have been shown to suppress angiogenesis and OC cell migration in preclinical studies. Moreover, clinical data have shown that using ACE-Is with standard chemotherapy prolongs patient survival. Based on this rationale, we discuss the available in vitro models of OC for future studies with ACE-Is and demonstrate an in silico approach that has enabled us to select the most promising molecules: perindopril, ramipril, trandolapril and their diketopiperazine derivatives.

Integrated radiomics and immune infiltration analysis to decipher immunotherapy efficacy in lung adenocarcinoma

Background

Research in recent years has witnessed unprecedented improvements in immunotherapy, especially immune checkpoint blockade (ICB) for the treatment of lung adenocarcinoma (LUAD) patients. Nevertheless, due to the heterogeneity of immunotherapy response, reliable biomarkers are urgently needed to guide precision cancer therapy. In this study, we aimed to identify immune subtypes in LUAD and develop a radiogenomic model to improve immunotherapy predictive accuracy.

Methods

In this study, clinical data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) databases, and immune subtypes were identified using the ConsensusClusterPlus package in R. Biological, genomic, and epigenomic distinctions were compared. The TCGA cohort and clinical cohort from the Third Xiangya Hospital were utilized to demonstrate no significant differences of survival probability between sexes. Feature extraction and definition were conducted from 103 computed tomography (CT) images from The Cancer Imaging Archive (TCIA) dataset via the "PyRadiomics" embedded in Python. A series of machine learning techniques were applied to build a radiogenomic model.

Results

Two LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in biological, genomic, and epigenomic distinctions among the two subtypes were observed (P<0.05). The immune subtype A participated in pathways related to immune activation and displayed a higher tumor microenvironment (TME) score (P<0.001) with a better prognosis of LUAD [overall survival (OS), P=0.037; disease-specific survival (DSS), P=0.034]. Besides, the model appears to show better fit for females (P=0.015) than for males (P=0.641). Our constructed radiogenomic model incorporating 12 radiomics features displayed satisfactory potential to facilitate the predictive accuracy of immunotherapy in LUAD [test area under the curve (AUC) =0.89; train AUC =0.95].

Conclusions

Our study presented a promising avenue to harness the rich radiomics data to identify the specific immune subtype and integrate it into the existing clinical decision-making system to facilitate the predictive accuracy of immunotherapy in LUAD.

ERVcancer: a web resource designed for querying activation of human endogenous retroviruses across major cancer types

Human endogenous retroviruses (HERVs) comprise approximately 8% of the human genome, integrated into the dynamic regulatory network of cellular potency during early embryonic development. In recent studies, resurgent the transcriptional activity of HERVs has been frequently observed in many types of human cancers, suggesting their potential functions in the occurrence and progression of malignancy. However, a dedicated web resource for querying the relationship between the activation of HERVs and cancer development is lacking. Here, we construct a database to explore the sequence information, expression profiles, survival prognosis, and genetic interactions of HERVs in diverse cancer types. Our database currently contains RNA sequencing data of 580 HERVs across 16,246 samples, including that of 6478 tumoral and 634 normal tissues, 932 cancer cell lines, as well as 151 early embryonic and 8051 human adult tissues. The primary goal is to provide an easily accessible and user-friendly database for professionals in the fields of bioinformatics, pathology, pharmacology, and related areas, enabling them to efficiently screen the activity of HERVs of interest in normal and cancerous tissues and evaluate the clinical relevance. The ERVcancer database is available at http://kyuanlab.com/ervcancer/.

Efficient discovery of robust prognostic biomarkers and signatures in solid tumors

Recent advancements in multi-omics and big-data technologies have facilitated the discovery of numerous cancer prognostic biomarkers and gene signatures. However, their clinical application remains limited due to poor reproducibility and insufficient independent validation. Despite the availability of high-quality datasets, achieving reliable biomarker identification across multiple cohorts continues to be a significant challenge. To address these issues, we developed a comprehensive platform, SurvivalML, designed to support the discovery and validation of prognostic biomarkers and gene signatures using large-scale and harmonized data from 21 cancer types. Through SurvivalML, we identified DCLRE1B as a novel prognostic biomarker for hepatocellular carcinoma, with experimental confirmation of its role in promoting tumor progression. Additionally, we developed the Chinese glioblastoma prognostic signature (CGPS) and its simplified version, SCGPS, a three-gene model. Both demonstrated superior predictive performance compared to other glioblastoma signatures in our in-house cohort and five independent Chinese datasets. The SCGPS model was further validated in 109 clinical samples using multiplex immunofluorescence, showing strong consistency with the original CGPS model. Overall, SurvivalML provides a robust platform for the identification and validation of prognostic biomarkers and gene signatures, offering a valuable resource for advancing cancer research and clinical application.

Engineered-affibody conjugates contribute to the specific targeting and cellular retention of polyplexes in Erbb3 overexpressed lung cancer cells

Ligand-modified nanoparticles have shown the ability to specifically bind to tumor cells, improving retention in tumors after initial accumulation driven by the enhanced permeability and retention effect. These particles are typically engineered to bind to receptors overexpressed in cancer cells compared to healthy cells, such as the HER3 (Erbb3) receptor in lung cancer. In this study, we confirmed the overexpression of Erbb3 in various KRAS mutant lung cancer cell lines. An engineered affibody, well-established in previous research, was selected to target Erbb3 as a proof of concept. The affibody was integrated into the particle system via two distinct strategies. In the pre-functionalization approach, the affibody was conjugated to PEI or C14-PEI using SPDP as a linker. A spectral shift technique was then used to assess the affinity of the affibody and affibody conjugates toward Erbb3, allowing us to estimate the half-maximal effective concentration (EC50). Following synthesis and characterization, various polyplex formulations were prepared, including mRNA complexes with PEI-affibody, C14-PEI/PEI-affibody, and C14-PEI/C14-PEI-affibody. In the post-functionalization approach, polyplex formulations composed of different blends of C14-PEI and functionalized Azido-PEI were initially prepared and subsequently modified with DBCO-functionalized affibody via click chemistry. These formulations were prepared at various nitrogen to phosphate (N/P) ratios and characterized in terms of particle size, polydispersity index (PDI), and zeta potential. We also evaluated cellular uptake and eGFP mRNA expression to understand how the different formulations and conjugates influenced ligand-modified polyplex properties and delivery behavior. Our results demonstrated that affibody conjugates can specifically target Erbb3 and promote polyplex accumulation in KRAS-mutated lung cancer cells. We further analyzed the impact of conjugation methods and affibody density on polyplex design and performance. In conclusion, this study highlights the advantages of using specific targeting ligands. By optimizing formulation components, conjugation methods, and ligand density, various targeting ligands can be attached to polyplexes, enhancing cell-specific targeting, internalization, and retention. These findings provide valuable insights and a foundation for future targeted therapies and polyplex design.

Exploring the impact of RPN1 on tumorigenesis and immune response in cancer

The ribophorin family, including RPN1, has been associated with tumor progression, but its specific role in pan-cancer dynamics remains unclear. Using data from TCGA, GTEx, and Ualcan databases, we investigated the relationship of RPN1 with prognosis, genomic alterations, and epigenetic modifications across various cancers. Differential analysis revealed elevated RPN1 expression in multiple cancer types, indicating a potential prognostic value. Amplification was the predominant mutation type of RPN1 in pan-cancer, with notable correlations with DNA methylation and copy number variation. Gene set variation analysis identified RPN1's involvement in cancer development, immunity, and metabolism. Additionally, RPN1 expression correlated with the tumor microenvironment, immune response factors, and response to anti-tumor therapies. Functional validation in triple-negative breast cancer, glioblastoma, and bladder cancer cell lines demonstrated the role of RPN1 in tumor cell proliferation and migration. Our findings highlight RPN1 as a potential biomarker for cancer diagnosis and treatment response in pan-cancer therapy.

Super-enhancer profiling reveals ThPOK/ZBTB7B, a CD4+ cell lineage commitment factor, as a master regulator that restricts breast cancer cells to a luminal non-migratory phenotype

Despite efforts to understand breast cancer biology, metastatic disease remains a clinical challenge. Identifying suppressors of breast cancer progression and mechanisms of transition to more invasive phenotypes could provide game changing therapeutic opportunities. Transcriptional deregulation is central to all malignancies, highlighted by the extensive reprogramming of regulatory elements that underlie oncogenic programs. Among these, super-enhancers (SEs) stand out due to their enrichment in genes controlling cancer hallmarks. To reveal novel breast cancer dependencies, we integrated the analysis of the SE landscape with master regulator activity inference for a series of breast cancer cell lines. As a result, we identified T-helper-inducing Poxviruses and Zinc-finger (POZ)/Krüppel-like factor (ThPOK, ZBTB7B), a CD4+ cell lineage commitment factor, as a breast cancer master regulator that is recurrently associated with a SE. ThPOK expression is highest in luminal breast cancer but is significantly reduced in the basal subtype. Manipulation of ThPOK levels in cell lines shows that its repressive function restricts breast cancer cells to an epithelial phenotype by suppressing the expression of genes involved in the epithelial-mesenchymal transition (EMT), WNT/b-catenin target genes, and the pro-metastatic TGFb pathway. Our study reveals ThPOK as a master transcription factor that restricts the acquisition of metastatic features in breast cancer cells.

Bioinformatic analysis of glycolysis and lactate metabolism genes in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with significant global incidence. This study investigates glycolysis- and lactate metabolism-related genes (GALMRGs) in HNSCC, focusing on their impact on prognosis, the tumor immune microenvironment, and their potential as therapeutic biomarkers. Analysis of data from the Cancer Genome Atlas and Gene Expression Omnibus identified 16 GALMRGs that were differentially expressed in HNSCC compared to normal tissues. Functional analysis revealed the involvement of lactate and pyruvate metabolism and HIF-1 signaling pathways. Weighted gene co-expression network analysis identified two module genes, CDKN3 and SLC2A1. Five key genes (CAV1, CDKN3, LDHA, MB, and PER2) were identified through univariate, multivariate, and LASSO regression analyses and used to construct a prognostic model. This model demonstrated strong predictive accuracy for overall survival, stratifying patients into high- and low-risk groups. Immune cell infiltration analysis showed a negative correlation between resting and activated mast cells, and low-risk patients had higher tumor mutational burden, suggesting a better response to immunotherapy. Consensus clustering classified HNSCC into two distinct molecular subtypes with differing expression of the key genes. This GALMRG-based prognostic model is a promising biomarker for predicting HNSCC outcomes and immunotherapy responses, providing valuable insights for personalized treatment strategies.

RAB39B: A novel biomarker for acute myeloid leukemia identified via multi-omics and functional validation

Background

The objective of this research was to investigate the involvement of RAB39B in acute myeloid leukemia (AML) using bioinformatics analysis and in vitro experiments for validation.

Methods

In this article, RNA sequencing data from The Cancer Genome Atlas and genotype-tissue expression were utilized to analyze the expression of RAB39BA and identify differentially expressed genes.

Results

AML exhibited elevated expression of RAB39B in diverse tumor types. In laboratory experiments, it has been demonstrated that RAB39B exhibits a significant expression level in AML cell lines when compared to normal peripheral blood monocytes. Moreover, RAB39B is closely linked to the growth and programmed cell death of AML cells.

Conclusion

In conclusion, RAB39B shows potential as a biomarker for the identification and prediction of AML, contributing to the growth and cell death processes in AML.

Connecting concentrations of copper, selenium, and zinc with transcriptomic and proteomic data of well-characterized human colorectal cancer cell lines

BACKGROUND

Colorectal cancer (CRC) incidence is associated with lower circulating selenium and zinc and elevated copper concentrations. Moreover, copper and selenium accumulate within tumor tissue, indicating a disturbed homeostasis of these essential trace elements in CRC.

OBJECTIVE

This study aimed to identify associations between CRC characteristics (based on genomic, transcriptomic and proteomic data) and trace element concentrations.

METHODS

The concentrations of copper, selenium, and zinc were measured in 83 human CRC cell lines and correlated with transcript and protein expression levels to identify trace element-related gene signatures. By using publicly available gene expression data from The Cancer Genome Atlas we investigated the association between those signatures with the survival probability of CRC patients.

RESULTS

The CRC cell lines differed in their copper (fold change 7.3), selenium (fold change 6), and zinc (fold change 2.6) concentrations. The concentrations were not associated with genetic or cellular characteristics, except for lower copper concentrations in KRAS mutant cells. Expression levels of known copper- and zinc-related proteins correlated significantly with the respective trace element concentrations, serving as a proxy for trace element concentrations in tumors, and with patient survival. This was not the case for selenium and selenoproteins. In addition, an unbiased approach identified novel high and low copper- and zinc-related gene expression signatures significantly associated with patient's outcome.

CONCLUSION

Herein we identify gene signatures associated with intracellular copper and zinc concentrations in CRC cell lines. Extrapolating these signatures to primary colorectal tumors revealed that they can inform outcome of CRC patients.

Combined targeting of glioblastoma stem cells of different cellular states disrupts malignant progression

Glioblastoma (GBM) is the most lethal primary brain tumor with intra-tumoral hierarchy of glioblastoma stem cells (GSCs). The heterogeneity of GSCs within GBM inevitably leads to treatment resistance and tumor recurrence. Molecular mechanisms of different cellular state GSCs remain unclear. Here, we find that classical (CL) and mesenchymal (MES) GSCs are enriched in reactive immune region and high CL-MES signature informs poor prognosis in GBM. Through integrated analyses of GSCs RNA sequencing and single-cell RNA sequencing datasets, we identify specific GSCs targets, including MEOX2 for the CL GSCs and SRGN for the MES GSCs. MEOX2-NOTCH and SRGN-NFκB axes play important roles in promoting proliferation and maintaining stemness and subtype signatures of CL and MES GSCs, respectively. In the tumor microenvironment, MEOX2 and SRGN mediate the resistance of CL and MES GSCs to macrophage phagocytosis. Using genetic and pharmacologic approaches, we identify FDA-approved drugs targeting MEOX2 and SRGN. Combined CL and MES GSCs targeting demonstrates enhanced efficacy, both in vitro and in vivo. Our results highlighted a therapeutic strategy for the elimination of heterogeneous GSCs populations through combinatorial targeting of MEOX2 and SRGN in GSCs.

Identification of mitochondrial permeability transition-related lncRNAs as quantitative biomarkers for the prognosis and therapy of breast cancer

Breast cancer (BC) continues to pose a global health threat and presents challenges for treatment due to its high heterogeneity. Recent advancements in the understanding of mitochondrial permeability transition (MPT) and the regulatory roles of long non-coding RNAs (lncRNAs) offer potential insights for the stratification and personalized treatment of BC. Although the association between MPT and lncRNAs has not been widely studied, a few research studies have indicated a regulatory impact of lncRNAs on MPT, further deepening the understanding of the tumor. To identify reliable biomarkers associated with MPT for managing BC, bulk RNA-seq data of MPT-related lncRNAs acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project were utilized to assess BC patients. A scoring system, termed the MPT-related score (MPTRscore), was developed using LASSO-Cox regression on data from 1,029 BC patients from TCGA-BRCA. Meanwhile, the superior prognostic accuracy of the MPTRscore was demonstrated by comparing it with biomarkers, including PAM50 subtyping for standardization. Subsequently, a clinical prediction model was created by incorporating the MPTRscore and clinical variables. This analysis revealed two distinct MPTRscore groups characterized by different biomolecular processes, tumor microenvironment (TME) patterns, and clinical outcomes. The MPTRscore was further investigated through unsupervised consensus clustering of TCGA-BRCA based on MPTRscore-related prognostic genes. Additionally, the MPTRscore was identified as an independent prognostic factor for BC and showed guiding utility in immunotherapy and chemotherapy response. Specifically, patients with a low MPTRscore exhibited better prognosis and treatment responses compared to those with a high MPTRscore. Significantly, the relevance of clustering results and MPTRscore was found to be mediated by lncRNA transcript RP11-573D15.8-018. In conclusion, MPTRscore-related clusters were identified in BC, and an integrative score was developed as a biomarker for predicting BC prognosis and therapeutic response. Additionally, molecular interactions underlying the relationship between MPTRscore-related clusters and MPTRscore were uncovered, proving insights for BC stratification. These findings may aid in prognosis determination and therapeutic decision-making for BC patients.

Transcriptomic characterisation of acute myeloid leukemia cell lines bearing the same t(9;11) driver mutation reveals different molecular signatures

BACKGROUND

Acute myeloid leukemia (AML) is the most common type of acute leukemia, accounting for 20% of cases in children and adolescents. Genome-wide studies have identified genes that are commonly mutated in AML, including many epigenetic regulators involved in either DNA methylation (DNMT3A, TET2, IDH1/2) or histone post-translational modifications (ASXL1, EZH2, MLL1). Several cell lines derived from AML patients are widely used in cancer research. Whether important differences in these cell lines exist remains poorly characterised.

RESULTS

Here, we used RNA sequencing (RNA-Seq) to contrast the transcriptome of four commonly used AML-derived cell lines: THP-1, NOMO-1, MOLM-13 bearing the common initiating t(9;11) translocation, and MV4.11 bearing the t(4;11) translocation. Gene set enrichment analyses and comparison of key transcription and epigenetic regulator genes revealed important differences in the transcriptome, distinguishing these AML models. Among these, we found striking differences in the expression of clusters of genes located on chromosome 19 encoding Zinc Finger (ZNF) transcriptional repressors. Low expression of many ZNF genes within these clusters is associated with poor survival in AML patients.

CONCLUSION

The present study offers a valuable resource by providing a detailed comparative characterisation of the transcriptome of cell lines within the same AML subtype used as models for leukemia research.

iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment

Metabolic enzymes play a central role in cancer metabolic reprogramming, and their dysregulation creates vulnerabilities that can be exploited for therapy. However, accurately measuring metabolic enzyme activity in a high-throughput manner remains challenging due to the complex, multi-layered regulatory mechanisms involved. Here, we present iMetAct, a framework that integrates metabolic-transcription networks with an information propagation strategy to infer enzyme activity from gene expression data. iMetAct outperforms expression-based methods in predicting metabolite conversion rates by accounting for the effects of post-translational modifications. With iMetAct, we identify clinically significant subtypes of hepatocellular carcinoma with distinct metabolic preferences driven by dysregulated enzymes and metabolic regulators acting at both the transcriptional and non-transcriptional levels. Moreover, applying iMetAct to single-cell RNA sequencing data allows for the exploration of cancer cell metabolism and its interplay with immune regulation in the tumor microenvironment. An accompanying online platform further facilitates tumor metabolic analysis, patient stratification, and immune microenvironment characterization.

Pan-cancer analysis of co-inhibitory molecules revealing their potential prognostic and clinical values in immunotherapy

Background

The widespread use of immune checkpoint inhibitors (anti-CTLA4 or PD-1) has opened a new chapter in tumor immunotherapy by providing long-term remission for patients. Unfortunately, however, these agents are not universally available and only a minority of patients respond to them. Therefore, there is an urgent need to develop novel therapeutic strategies targeting other co-inhibitory molecules. However, comprehensive information on the expression and prognostic value of co-inhibitory molecules, including co-inhibitory receptors and their ligands, in different cancers is not yet available.

Methods

We investigated the expression, correlation, and prognostic value of co-inhibitory molecules in different cancer types based on TCGA, UCSC Xena, TIMER, CellMiner datasets. We also examined the associations between the expression of these molecules and the extent of immune cell infiltration. Besides, we conducted a more in-depth study of VISTA.

Result

The results of differential expression analysis, correlation analysis, and drug sensitivity analysis suggest that CTLA4, PD-1, TIGIT, LAG3, TIM3, NRP1, VISTA, CD80, CD86, PD-L1, PD-L2, PVR, PVRL2, FGL1, LGALS9, HMGB1, SEMA4A, and VEGFA are associated with tumor prognosis and immune cell infiltration. Therefore, we believe that they are hopefully to serve as prognostic biomarkers for certain cancers. In addition, our analysis indicates that VISTA plays a complex role and its expression is related to TMB, MSI, cancer cell stemness, DNA/RNA methylation, and drug sensitivity.

Conclusions

These co-inhibitory molecules have the potential to serve as prognostic biomarkers and therapeutic targets for a broad spectrum of cancers, given their strong associations with key clinical metrics. Furthermore, the analysis results indicate that VISTA may represent a promising target for cancer therapy.

Migrasome Marker Epidermal Growth Factor Domain-Specific O-GlcNAc Transferase: Pan-Cancer Angiogenesis Biomarker and the Potential Role of circ_0058189/miR-130a-3p/EOGT Axis in Hepatocellular Carcinoma Progression and Sorafenib Resistance

Background: The EGF domain-specific O-GlcNAc transferase (EOGT), a migrasome marker, plays emerging roles in cancer biology through O-GlcNAcylation modifications, yet its pan-cancer functions and therapeutic implications remain underexplored. This study aimed to systematically characterize EOGT's oncogenic mechanisms across malignancies, with particular focus on hepatocellular carcinoma (HCC) progression and sorafenib resistance. Methods: Multi-omics analysis integrated TCGA/GTEx data from 33 cancer types with spatial/single-cell transcriptomics and 10 HCC cohorts. Functional validation employed Huh7 cell models with EOGT modulation, RNA sequencing, and ceRNA network construction. Drug sensitivity analysis leveraged GDSC/CTRP/PRISM databases, while immune microenvironment assessment utilized ESTIMATE/TIMER algorithms. Results: EOGT showed cancer-specific dysregulation, marked by significant upregulation in HCC correlating with advanced stages and poor survival. Pan-cancer analysis revealed EOGT's association with genomic instability, tumor stemness, and angiogenesis. Experimental validation demonstrated EOGT's promotion of HCC proliferation and migration. A novel exosomal circ_0058189/miR-130a-3p/EOGT axis was identified, showing that circ_0058189 was upregulated in HCC tissues, plasma samples and exosomes of sorafenib-resistant cells. Conclusion: This study establishes EOGT as a pan-cancer angiogenesis biomarker, while elucidating its role in therapeutic resistance via exosomal circRNA-mediated regulation, providing mechanistic insights for targeted intervention strategies.

Pharmacy faculty and students perceptions of artificial intelligence: A National Survey

INTRODUCTION

This study explores the perceptions, familiarity, and utilization of artificial intelligence (AI) among pharmacy faculty and students across the United States. By identifying key gaps in AI education and training, it highlights the need for structured curricular integration to prepare future pharmacists for an evolving digital healthcare landscape.

METHODS

A 19-item Qualtrics™ survey was created to assess perceptions of AI use among pharmacy faculty and students and distributed utilizing publicly available contacts at schools of pharmacy and intern lists. The electronic survey was open from September 5th to November 22nd 2023. Responses were analyzed for trends and compared between faculty and student responses across four sub-domains.

RESULTS

A total of 235 pharmacy faculty and 405 pharmacy students completed the survey. Responses indicated high familiarity with AI in both groups but found differences in training. Both groups identified ethical considerations and training as major barriers to AI integration. Faculty were less likely to trust AI responses than students despite reporting similar rates of incorrect information. Students were more concerned than faculty about AI reducing pharmacy jobs, particularly in community and health-system settings.

DISCUSSION

This study highlights the need for intentional AI training tailored to pharmacy students, aiming to bridge the knowledge gap and equip them with the skills to navigate an AI-driven future. The inconsistency in how AI is addressed within the curriculum and the lack of established ethical guidelines display the need for clear and consistent institutional policies and professional guidance.

Genome-wide CRISPR screen identifies IRF1 and TFAP4 as transcriptional regulators of Galectin-9 in T cell acute lymphoblastic leukemia

Galectin-9 is overexpressed in a variety of cancers and associated with worse clinical outcome in some cancers. However, the regulators driving Galectin-9 expression are unknown. Here, we defined the transcriptional regulators and epigenetic circuitry of Galectin-9 in pediatric T cell acute lymphoblastic leukemia (T-ALL), as an example of a disease with strong Galectin-9 expression, in which higher expression was associated with lower overall survival. By performing a genome-wide CRISPR screen, we identified the transcription factors IRF1 and TFAP4 as key regulators for Galectin-9 expression by binding its regulatory elements. Whereas IRF1 was observed exclusively on the promoter, TFAP4 binding was detected at an enhancer solely in T-ALL cells associated with higher Galectin-9 levels. Together, our results show that IRF1 is responsible and indispensable for Galectin-9 expression and TFAP4 further fine-tunes its expression. Our approach, a flow-based genome-wide CRISPR screen complemented by transcription factor binding and enhancer mapping, creates innovative opportunities for understanding and manipulating epigenetic transcriptional regulation in cancer.

SurvDB: Systematic Identification of Potential Prognostic Biomarkers in 33 Cancer Types

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research.

NCBP2 predicts the prognosis and the immunotherapy response of cancers: a pan-cancer analysis

Background

The cap-binding complex (CBC) plays a crucial role in facilitating gene expression by safeguarding mRNA from nonsense-mediated decay, promoting mRNA splicing, 3'-end processing, and facilitating nuclear export. Nevertheless, the precise biological functions and clinical implications of CBC in cancer remain ambiguous, necessitating further investigation for clarification.

Methods

The present study utilized the cBioPortal database to investigate the genetic alterations of nuclear cap binding protein subunit 2 (NCBP2) in pan-cancer. The Cancer Genome Atlas (TCGA) and online web tools were employed to analyze the correlation between NCBP2 and prognosis, genome instability, immune infiltration, immune response, cancer stemness, and chemotherapeutic efficacy in pan-cancer. Furthermore, the expression of NCBP2 was confirmed by immunohistochemistry (IHC) and functional analysis at the single-cell level was conducted using the CancerSEA database.

Results

NCBP2 exhibited distinct genetic alterations in pan-cancer with an increased expression in 24/32, while decreased expression in 3/32, types of cancers. IHC confirmed the aberrant expression of NCBP2 in lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), kidney renal papillary cell carcinoma (KIRP) and kidney renal clear cell carcinoma (KIRC). NCBP2 was correlated with overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) in various cancers. Importantly, it was identified as a risk factor for OS, DSS and PFS in PAAD and uterine corpus endometrial carcinoma (UCEC). Gene Set Enrichment Analysis (GSEA) demonstrated that elevated NCBP2 was linked to immune and proliferation related pathways across multiple cancer types. Furthermore, a negative association between NCBP2 and stromal score, immune score, and ESTIMATE score was detected, and a positive correlation was observed between NCBP2 and diverse immune cells as well as stemness-indexes in the majority of cancer types. Drug sensitivity analysis revealed that drugs associated with NCBP2 primarily targeted DNA replication, chromatin histone methylation, ABL signaling, cell cycle, and PI3K signaling. Additionally, an examination at the single-cell level indicated that NCBP2 was positively correlated with cell cycle progression, DNA damage, DNA repair, invasion, and stemness in most cancer types, while negatively correlated with apoptosis, inflammation, and hypoxia in certain cancers.

Conclusion

In this study, we revealed the correlation of NCBP2 with prognosis, microenvironment and stemness, indicating that NCBP2 might be a potential therapeutic target for more effective and personalized therapy strategies in pan-cancer.

ADME gene-driven prognostic model for bladder cancer: a breakthrough in predicting survival and personalized treatment

BACKGROUND

Genes that participate in the absorption, distribution, metabolism, excretion (ADME) processes occupy a central role in pharmacokinetics. Meanwhile, variability in clinical outcomes and responses to treatment is notable in bladder cancer (BLCA).

METHODS

Our study utilized expansive datasets from TCGA and the GEO to explore prognostic factors in bladder cancer. Utilizing both univariate Cox regression and the lasso regression techniques, we identified ADME genes critical for patient outcomes. Utilizing genes identified in our study, a model for assessing risk was constructed. The evaluation of this model's predictive precision was conducted using Kaplan-Meier survival curves and assessments based on ROC curves. Furthermore, we devised a predictive nomogram, offering a straightforward visualization of crucial prognostic indicators. To explore the potential factors mediating the differences in outcomes between high and low risk groups, we performed comprehensive analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based enrichment analyses, immune infiltration variations, somatic mutation landscapes, and pharmacological sensitivity response assessment etc. Immediately following this, we selected core genes based on the PPI network and explored the prognostic potential of the core genes as well as immune modulation, and pathway activation. And the differential expression was verified by immunohistochemistry and qRT-PCR. Finally we explored the potential of the core genes as pan-cancer biomarkers.

RESULTS

Our efforts culminated in the establishment of a validated 17-gene ADME-centered risk prediction model, displaying remarkable predictive accuracy for BLCA prognosis. Through separate cox regression analyses, the importance of the model's risk score in forecasting BLCA outcomes was substantiated. Furthermore, a novel nomogram incorporating clinical variables alongside the risk score was introduced. Comprehensive studies established a strong correlation between the risk score and several key indicators: patterns of immune cell infiltration, reactions to immunotherapy, landscape of somatic mutation and profiles of drug sensitivity. We screened the core prognostic gene CYP2C8, explored its role in tumor bioregulation and validated its upregulated expression in bladder cancer. Furthermore, we found that it can serve as a reliable biomarker for pan-cancer.

CONCLUSION

The risk assessment model formulated in our research stands as a formidable instrument for forecasting BLCA prognosis, while also providing insights into the disease's progression mechanisms and guiding clinical decision-making strategies.

Bioinformatic analysis of serpina1 expression in papillary thyroid carcinoma and its potential association with Hashimoto's thyroiditis

PURPOSE

Previous studies have suggested that SERPINA1 may promote a better prognosis in papillary thyroid carcinoma (PTC) along with Hashimoto's thyroiditis (HT). This study aims to further explore the role of the SERPINA1 gene in PTC and its relationship with HT using multiple databases.

METHODS

Transcriptomic data from The Cancer Genome Atlas (TCGA) were utilized to analyze differences in SERPINA1 expression between PTC patients with and without HT. The expression levels of SERPINA1 in tumor tissues and its association with tumor characteristics were assessed using the Wilcoxon test across both patient groups. The impact of SERPINA1 expression on immune cell infiltration in PTC was evaluated using the CIBERSORT tool. Single-cell transcriptomic data from the Gene Expression Omnibus (GEO) were further analyzed to identify SERPINA1-expressing subpopulations based on Thyroid Differentiation Score (TDS) and pseudotime analysis. Gene Set Variation Analysis (GSVA) was employed to characterize pathways associated with SERPINA1, inferring its potential functions. Finally, CellChat was used to investigate key ligand-receptor interactions between SERPINA1-positive subpopulations and other cell types.

RESULTS

TCGA data analysis reveals that, compared to normal thyroid tissue, the transcriptional level of SERPINA1 is significantly elevated in PTC tissues. Moreover, the expression of SERPINA1 is closely linked to certain clinical pathological features of PTC and the infiltration of immune cells in the tumor microenvironment. Single-cell transcriptome analysis reveals that SERPINA1 is primarily expressed in thyrocytes and myeloid cells. In thyrocytes, SERPINA1 is associated with complement-related proteins (e.g., C3, CD55). In poorly differentiated thyrocytes, it is linked to protease inhibitors and epithelial-mesenchymal transition (EMT) pathways, while in moderately differentiated thyrocytes, it associates with apolipoproteins APOE and APOC1. In macrophages, SERPINA1 is highly expressed in HT-associated macrophages and unpolarized macrophages, correlating with inflammation and extracellular matrix regulation pathways. Cell-cell interaction analysis indicates that SERPINA1-positive cells interact with other cells in the tumor microenvironment through macrophage migration inhibitory factor (MIF) and fibronectin 1 (FN1).

CONCLUSION

Compared to normal thyroid tissue or cells, the expression level of SERPINA1 is elevated in PTC. In cancer cells, SERPINA1 may be associated with the complement system and complement regulator functions. In poorly differentiated thyrocytes, SERPINA1 may primarily function as a protease inhibitor and is closely related to FN1. In moderately differentiated thyrocytes, SERPINA1 is associated with apolipoproteins. In unpolarized macrophages, the function of SERPINA1 may be to act as a serine protease inhibitor, participating in the remodeling of the extracellular matrix. In macrophages within an HT environment, the elevated expression of SERPINA1 may serve as a protective mechanism to limit inflammation. In the tumor microenvironment coexisting with HT, SERPINA1 outside the tumor cells may enter the tumor cells through lipid metabolism pathways. The potential role of SERPINA1 in PTC progression is complex, and the findings of this study require further validation.

Gene Signature-Based Prognostic Model for Acute Myeloid Leukemia: The Role of BATF, EGR1, PD-1, PD-L1, and TIM-3

Background: Acute myeloid leukemia (AML) is a malignancy of hematopoietic stem and progenitor cells, with T cell exhaustion linked to poor outcomes. Our previous research has shown that basic leucine zipper ATF-like transcription factor (BATF) and early growth response 1 (EGR1) play a role in chimeric antigen receptor T (CAR-T) cell exhaustion during AML tumor elimination. However, the roles of BATF and EGR1 and their association with immune checkpoint genes in AML prognosis remain underexplored. Methods: Bone marrow (BM) samples from 92 newly diagnosed AML patients at our clinical center (JUN-dataset) were analyzed to detect the expression levels of BATF, EGR1, programmed cell death 1 (PD-1), programmed death-ligand 1 (PD-L1), T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) together with conducting a prognostic assessment. Our findings were validated using RNA sequencing data from 155 AML patients from the TCGA database and 199 AML patients from the Beat-AML database. Results: High BATF expression correlated with poor overall survival (OS) (P = 0.030), whereas high EGR1 expression indicated a favorable prognosis (P = 0.040). Patients with high BATF and low EGR1 expression had worst outcomes (P < 0.001). Among those receiving allogenic hematopoietic stem cell transplantation (allo-HSCT), high BATF expression was linked to shorter OS (P = 0.004). Moreover, a prognostic model incorporating BATF, EGR1, PD-1, PD-L1, and TIM-3 calculated a risk score, with high-risk patients demonstrating significantly shorter OS than low-risk patients in both total AML patients and allo-HSCT recipients (P < 0.001). Similar results were found in both the TCGA and Beat-AML datasets. Conclusions: We establish a prognostic model based on BATF, EGR1, PD-1, PD-L1, and TIM-3 expression that effectively predicts survival outcomes for AML patients and allo-HSCT recipients. This model may provide valuable insights for prognosis assessment and treatment strategies.

Developing and validating a prognostic disulfidptosis-related signature for glioblastoma: predicting radioresistance and synergestic effect with immunotherapy

BACKGROUND

Programmed cell death (PCD) modulated radioresistance is one of the predominant causes of treatment failure in glioblastoma (GBM). Disulfidptosis, a newly discovered form of PCD, plays a crucial role in GBM progression. However, the association among disulfidptosis, radiosensitivity and radiotherapy (RT) in GBM remain unclear.

METHODS

We systematically analyzed disulfidptosis-related genes in 1075 GBM patients and constructed a disulfidptosis-related gene signature (DRS). Correlations among the DRS, patient prognosis and immune microenvironment were fully explored. The effects of DRS and EFEMP2 on radiotherapy efficacy were investigated via single cell sequencing analysis and validated via in vitro and in vivo experiments.

RESULTS

The DRS was identified as a robust and independent prognostic biomarker for GBM by multivariate Cox regression analysis, receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in multiple cohorts. High DRS is characterized by radioresistance, and EFEMP2 was proven to be the key gene involved in this process by single cell sequencing analysis, CCK-8 assay and a clonogenic survival assay. In high-DRS patients, the cancer-immunity cycle is attenuated because the antitumor cytotoxicity of CD8+ T cells is inhibited by immune checkpoints. Preclinically, the overexpression of EFEMP2 induced radioresistance and enhancing the efficacy of programmed cell death ligand-1 (PD-L1) blockade in GL261-bearing mice. The combination of irradiation and anti-PD-L1 therapy had a synergistic effect on GBM murine models in which EFEMP2 was overexpressed.

CONCLUSION

Our study bioinformatically and experimentally reveals the molecular landscape of disulfidptosis in GBM, develops a predictive signature for predicting prognosis as well as radioresistance, and provides a synergistic treatment that combines radiotherapy with immunotherapy for radioresistant GBM patients with high DRS or EFEMP2 expression.

Association of tumor microbiome with survival in resected early-stage PDAC

The pancreas tumor microbiota may influence tumor microenvironment and influence survival in early-stage pancreatic ductal adenocarcinoma (PDAC); however, current studies are limited and small. We investigated the relationship of tumor microbiota to survival in 201 surgically resected patients with localized PDAC (Stages I-II), from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. We characterized the tumor microbiome using RNA-sequencing data. We examined the association of the tumor microbiome with overall survival (OS), via meta-analysis with the Cox PH model. A microbial risk score (MRS) was calculated from the OS-associated microbiota. We further explored whether the OS-associated microbiota is related to host tumor immune infiltration. PDAC tumor microbiome α- and β-diversities were not associated with OS; however, 11 bacterial species, including species of Gammaproteobacteria, confirmed by extensive resampling, were significantly associated with OS (all Q < 0.05). The MRS summarizing these bacteria was related to a threefold change in OS (hazard ratio = 2.96 per standard deviation change in the MRS, 95% confidence interval = 2.26-3.86). This result was consistent across the two cohorts and in stratified analyses by adjuvant therapy (chemotherapy/radiation). Identified microbiota and the MRS also exhibited association with memory B cells and naïve CD4+ T cells, which may be related to the immune landscape through BCR and TCR signaling pathways. Our study shows that a unique tumor microbiome structure, potentially affecting the tumor immune microenvironment, is associated with poorer survival in resected early-stage PDAC. These findings suggest that microbial mechanisms may be involved in PDAC survival, potentially informing PDAC prognosis and guiding personalized treatment strategies.IMPORTANCEMuch of the available data on the PDAC tumor microbiome and survival are derived from relatively small and heterogeneous studies, including those involving patients with advanced stages of pancreatic cancer. There is a critical knowledge gap in terms of the tumor microbiome and survival in early-stage patients treated by surgical resection; we expect that advancements in survival may initially be best achieved in these patients who are treated with curative intent.

Exploring the oncogenic role and prognostic value of CKS1B in human lung adenocarcinoma and squamous cell carcinoma

Introduction

Lung cancer (LC) is a highly aggressive malignancy and remains a leading cause of cancer-related mortality worldwide. Non-small cell lung cancer (NSCLC), which includes adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), accounts for the majority of these deaths. Due to the lack of early clinical symptoms and late-stage diagnosis, there is an urgent need for precise and targeted therapeutic strategies. Cyclin-dependent kinase regulatory subunit 1B (CKS1B), a key regulator of the cell cycle, has been implicated in various human cancers. Emerging evidence suggests that its upregulation is associated with poor prognosis in NSCLC, highlighting its potential as a biomarker for early detection and targeted therapy.

Methods

In this study, we conducted a comprehensive bioinformatics analysis to evaluate the role of CKS1B in LUAD and LUSC. Differential gene expression analysis, survival analysis, immune infiltration correlation, and pathway enrichment analysis were performed using publicly available transcriptomic datasets. Additionally, gene interaction networks were analyzed to assess the functional significance of CKS1B in lung cancer progression.

Results

Our findings indicate a significant overexpression of CKS1B in LUAD and LUSC compared to normal lung tissues. Survival analysis demonstrated that higher CKS1B expression correlates with poor prognosis in NSCLC patients. Immune infiltration analysis revealed a potential role of CKS1B in modulating the tumor microenvironment, further supporting its relevance in lung cancer progression. Functional enrichment analysis highlighted its involvement in critical oncogenic pathways, including cell cycle regulation and immune modulation.

Discussion

The results suggest that CKS1B serves as a potential biomarker for early detection and prognosis in NSCLC. Its association with immune response pathways underscores its possible role in immunotherapy. However, despite these promising findings, further in vivo and in vitro studies are necessary to validate CKS1B's clinical applicability as a diagnostic and therapeutic target for lung cancer.

Metabolic state uncovers prognosis insights of esophageal squamous cell carcinoma patients

BACKGROUND

Metabolite-protein interactions (MPIs) are crucial regulators of cancer metabolism; however, their roles and coordination within the esophageal squamous cell carcinoma (ESCC) microenvironment remain largely unexplored. This study is the first to comprehensively map the metabolic landscape of the ESCC microenvironment by integrating an MPI network with multi-scale transcriptomics data.

METHODS

First, we characterized the metabolic states of cells in ESCC using single-cell transcriptome profiles of key metabolite-interacting proteins. Next, we determined the metabolic patterns of each ESCC patient based on the composition of different metabolic states within bulk samples. Finally, the ESCC samples were clustered into unique subtypes.

RESULTS

Sixteen ESCC metabolic states across 7 cell types were identified based on the re-analysis of single-cell RNA-sequencing data of 208,659 cells in 64 ESCC samples. Each of the 7 cell types within the tumor microenvironment exhibited distinct metabolic states, highlighting the high metabolic heterogeneity of ESCC. Based on differences in the compositions of the metabolic states, 4 ESCC subtypes were identified in two independent cohorts (n = 79 and 119), which were associated with significant variations in prognosis, clinical features, gene expression, and pathways. Notably, the inactivation of cellular detoxification processes may contribute to the poor prognosis of ESCC patients.

CONCLUSIONS

Overall, we redefined robust ESCC prognostic subtypes and identified key MPI pathways that link metabolism to tumor heterogeneity. This study provides the first comprehensive mapping of the ESCC metabolic microenvironment, offering novel insights into ESCC metabolic diversity and its clinical applications.

Lung Cancer Biomarker Database (LCBD): a comprehensive and curated repository of lung cancer biomarkers

BACKGROUND

Lung cancer remains a leading cause of cancer-related mortality, primarily because of the lack of effective diagnostic and therapeutic biomarkers. To address the issue of fragmented biomarker data across numerous publications, we have developed the Lung Cancer Biomarker Database (LCBD, http://lcbd.biomarkerdb.com ).

METHODS

We comprehensively reviewed biomarker-related studies up to June 30, 2023, and extracted relevant biomarker information. The identified biomarkers were systematically annotated, including genes, proteins, GO terms, KEGG pathways, biomarker types, molecular types, developmental stages, discovery methods, sources, populations, and sample sizes. The LCBD online platform was developed to integrate lung cancer biomarker data, and provide search, browsing, and data download functions for researchers. To validate the data in the LCBD, we conducted three case studies comparing models with and without LCBD data.

RESULTS

After deduplication and summarization, we collected 1,447 unique biomarkers that were systematically annotated. We then developed the LCBD specifically for use in lung cancer diagnosis. The validity of the biomarkers in the LCBD was confirmed using prognostic models, diagnostic models, and immune infiltration models.

CONCLUSION

The LCBD provides a centralized platform for lung cancer biomarkers, facilitating early screening and personalized treatment. This database is poised to become a valuable resource for lung cancer research and therapeutic strategies.