A molecular mechanism for environmental sex determination.

Daphnia produce genetically identical males and females; their sex is determined by environmental conditions. Recently, Kato et al. identified isoform switching events in Daphnia as a gene regulatory mechanism for sex-specific development. This finding uncovers the impact of alternative usage of gene isoforms on this extreme phenotypic plasticity trait.

Uncovering dark matter in cancer by identifying epigenetic drivers.

The complex relationship between chromatin accessibility, transcriptional regulation, and cancer transitions presents a daunting puzzle. Terekhanova et al. created a pan-cancer epigenetic and transcriptomic atlas at single-cell resolution, yielding important insights into the underlying chromatin architecture of cancer transitions and novel discoveries with the potential to advance precision medicine.

An integrative miRNA-mRNA expression analysis identifies miRNA signatures associated with SOD1 and TARDBP patient-derived motor neurons.

MicroRNAs (miRNAs) are a subset of small non-coding single-stranded RNA molecules involved in the regulation of post-transcriptional gene expression of a variety of transcript targets. Therefore altered miRNA expression may result in the dysregulation of key genes and biological pathways that has been reported with the onset and progression of neurodegenerative diseases, such as Amyotrophic lateral sclerosis (ALS). ALS is marked by a progressive degeneration of motor neurons (MNs) present in the spinal cord, brain stem and motor cortex. Although the pathomechanism underlying molecular interactions of ALS remains poorly understood, alterations in RNA metabolism, including dysregulation of miRNA expression in familial as well as sporadic forms are still scarcely studied. In this study, we performed combined transcriptomic data and miRNA profiling in MN samples of the same samples of iPSC-derived MNs from SOD1- and TARDBP (TDP-43 protein)-mutant-ALS patients and healthy controls. We report a global upregulation of mature miRNAs, and suggest that differentially expressed (DE) miRNAs have a significant impact on mRNA-level in SOD1-, but not in TARDBP-linked ALS. Furthermore, in SOD1-ALS we identified dysregulated miRNAs such as miR-124-3p, miR-19b-3p and miR-218 and their potential targets previously implicated in important functional process and pathogenic pathways underlying ALS. These miRNAs may play key roles in the neuronal development and cell survival related functions in SOD1-ALS. Altogether, we provide evidence of miRNA regulated genes expression mainly in SOD1 rather than TDP43-ALS.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs).

The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing.

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

Single-cell RNA sequencing reveals the gene expression profile and cellular communication in human fetal heart development.

The heart is the central organ of the circulatory system, and its proper development is vital to maintain human life. As fetal heart development is complex and poorly understood, we use single-cell RNA sequencing to profile the gene expression landscapes of human fetal hearts from the four-time points: 8, 10, 11, 17 gestational weeks (GW8, GW10, GW11, GW17), and identified 11 major types of cells: erythroid cells, fibroblasts, heart endothelial cells, ventricular cardiomyocytes, atrial cardiomyocytes, macrophage, DCs, smooth muscle, pericytes, neural cells, schwann cells. In addition, we identified a series of differentially expressed genes and signaling pathways in each cell type between different gestational weeks. Notably, we found that ANNEXIN, MIF, PTN, GRN signalling pathways were simple and fewer intercellular connections in GW8, however, they were significantly more complex and had more intercellular communication in GW10, GW11, and GW17. Notably, the interaction strength of OSM signalling pathways was gradually decreased during this period of time (from GW8 to GW17). Together, in this study, we presented a comprehensive and clear description of the differentiation processes of all the main cell types in the human fetal hearts, which may provide information and reference data for heart regeneration and heart disease treatment.

Revisiting the gene mutations and protein profile of WT 9-12: An autosomal dominant polycystic kidney disease cell line.

WT 9-12 is one of the cell lines commonly used for autosomal dominant polycystic kidney disease (ADPKD) studies. Previous studies had described the PKD gene mutations and polycystin expression in WT 9-12. Nonetheless, the mutations occurring in other ADPKD-associated genes have not been investigated. This study aims to revisit these mutations and protein profile of WT 9-12. Whole genome sequencing verified the presence of truncation mutation at amino acid 2556 (Q2556X) in PKD1 gene of WT 9-12. Besides, those variations with high impacts included single nucleotide polymorphisms (rs8054182, rs117006360, and rs12925771) and insertions and deletions (InDels) (rs145602984 and rs55980345) in PKD1L2; InDel (rs1296698195) in PKD1L3; and copy number variations in GANAB. Protein profiles generated from the total proteins of WT 9-12 and HK-2 cells were compared using isobaric tags for relative and absolute quantitation (iTRAQ) analysis. Polycystin-1 was absent in WT 9-12. The gene ontology enrichment and reactome pathway analyses revealed that the upregulated and downregulated proteins of WT 9-12 relative to HK-2 cell line leaded to signaling pathways related to immune response and amino acid metabolism, respectively. The ADPKD-related mutations and signaling pathways associated with differentially expressed proteins in WT 9-12 may help researchers in cell line selection for their studies.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

A comprehensive assessment of cell type-specific differential expression methods in bulk data.

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

Evaluation of epitranscriptome-wide N6-methyladenosine differential analysis methods.

RNA methylation has emerged recently as an active research domain to study post-transcriptional alteration in gene expression regulation. Various types of RNA methylation, including N6-methyladenosine (m6A), are involved in human disease development. As a newly developed sequencing biotechnology to quantify the m6A level on a transcriptome-wide scale, MeRIP-seq expands RNA epigenetics study in both basic and clinical applications, with an upward trend. One of the fundamental questions in RNA methylation data analysis is to identify the Differentially Methylated Regions (DMRs), by contrasting cases and controls. Multiple statistical approaches have been recently developed for DMR detection, but there is a lack of a comprehensive evaluation for these analytical methods. Here, we thoroughly assess all eight existing methods for DMR calling, using both synthetic and real data. Our simulation adopts a Gamma-Poisson model and logit linear framework, and accommodates various sample sizes and DMR proportions for benchmarking. For all methods, low sensitivities are observed among regions with low input levels, but they can be drastically boosted by an increase in sample size. TRESS and exomePeak2 perform the best using metrics of detection precision, FDR, type I error control and runtime, though hampered by low sensitivity. DRME and exomePeak obtain high sensitivities, at the expense of inflated FDR and type I error. Analyses on three real datasets suggest differential preference on identified DMR length and uniquely discovered regions, between these methods.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation.

BACKGROUND: Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging. RESULTS: Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters. CONCLUSION: Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer.

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

Gene Expression Profiles of AHNAK2, DCSTAMP, FN1, and TERT Correlate With Mutational Status and Recurrence in Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC), the most common malignancy of follicular cell derivation, is generally associated with good prognosis. Nevertheless, it is important to identify patients with aggressive PTCs and unfavorable outcome. Molecular markers such as BRAFV600E mutation and TERT promoter mutations have been proposed for risk stratification. While TERT promoter mutations have been frequently associated with aggressive PTCs, the association of BRAFV600E mutation with increased recurrence and mortality is less clear and has been controversially discussed. The aim of the present study was to analyze whether differentially expressed genes can predict BRAFV600E mutations as well as TERT promoter mutations in PTCs. RNA sequencing identified a large number of differentially expressed genes between BRAFV600E and BRAFwildtype PTCs. Of those, AHNAK2, DCSTAMP, and FN1 could be confirmed in a larger cohort (n = 91) to be significantly upregulated in BRAFV600E mutant PTCs using quantitative RT-PCR. Moreover, individual PTC expression values of DCSTAMP and FN1 were able to predict the BRAFV600E mutation status with high sensitivity and specificity. The expression of TERT was detected in all PTCs harboring TERT promoter mutations and in 19% of PTCs without TERT promoter mutations. Tumors with both TERT expression and TERT promoter mutations were particularly associated with aggressive clinicopathological features and a shorter recurrence-free survival. Altogether, it will be interesting to explore the biological function of AHNAK2, DCSTAMP, and FN1 in PTC in more detail. The analysis of their expression patterns could allow the characterization of PTC subtypes and thus enabling a more individualized surgical and medical treatment.

Transcriptomic evidence of immune modulation in subjects with chronic Trypanosoma cruzi infection.

Chagas disease is a neglected tropical infection that affects millions of people. This study explores transcriptomic changes in T. cruzi-infected subjects before and after treatment. Using total RNA sequencing, gene transcription was analyzed in peripheral blood mononuclear cells from asymptomatic (n=19) and symptomatic (n=8) T. cruzi-infected individuals, and non-infected controls (n=15). Differential expression was compared across groups, and before/after treatment in infected subgroups. Untreated infection showed 12 upregulated and 206 downregulated genes in all T. cruzi-infected subjects, and 47 upregulated and 215 downregulated genes in the symptomatic group. Few differentially expressed genes were found after treatment and between the different infected groups. Gene set enrichment analysis highlighted immune-related pathways activated during infection, with therapy normalizing immune function. Changes in the kynurenine/tryptophan ratio, increased pre-treatment, suggested chronic immune fatigue, which was restored post-treatment. These differentially expressed genes offer insights for potential biomarkers and pathways associated with disease progression and treatment response.

Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions.

BACKGROUND: DNA methylation is a biochemical process in which a methyl group is added to the cytosine-phosphate-guanine (CpG) site on DNA molecules without altering the DNA sequence. Multiple CpG sites in a certain genome region can be differentially methylated across phenotypes. Identifying these differentially methylated CpG regions (DMRs) associated with the phenotypes contributes to disease prediction and precision medicine development. RESULTS: We propose a novel DMR detection algorithm, gbdmr. In contrast to existing methods under a linear regression framework, gbdmr assumes that DNA methylation levels follow a generalized beta distribution. We compare gbdmr to alternative approaches via simulations and real data analyses, including dmrff, a new DMR detection approach that shows promising performance among competitors, and the traditional EWAS that focuses on single CpG sites. Our simulations demonstrate that gbdmr is superior to the other two when the correlation between neighboring CpG sites is strong, while dmrff shows a higher power when the correlation is weak. We provide an explanation of these phenomena from a theoretical perspective. We further applied the three methods to multiple real DNA methylation datasets. One is from a birth cohort study undertaken on the Isle of Wight, United Kingdom, and the other two are from the Gene Expression Omnibus database repository. Overall, gbdmr identifies more DMR CpGs linked to phenotypes than dmrff, and the simulated results support the findings. CONCLUSIONS: Gbdmr is an innovative method for detecting DMRs based on generalized beta regression. It demonstrated notable advantages over dmrff and traditional EWAS, particularly when adjacent CpGs exhibited moderate to strong correlations. Our real data analyses and simulated findings highlight the reliability of gbdmr as a robust DMR detection tool. The gbdmr approach is accessible and implemented by R on GitHub: https://github.com/chengzhouwu/gbdmr .

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.

BACKGROUND: Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. RESULTS: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . CONCLUSIONS: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Epigenomic Dysregulation in Youth Vapers: Implications for Disease Risk Assessment.

Despite the ongoing epidemic of youth vaping, the long-term health consequences of electronic cigarette use are largely unknown. We report the effects of vaping versus smoking on the oral cell methylome of healthy young vapers and smokers relative to non-users. Whereas vapers and smokers differ in number of differentially methylated regions (DMRs) (831 vs 2,863), they share striking similarities in the distribution and patterns of DNA methylation, chromatin states, transcription factor binding motifs, and pathways. There is substantial overlap in DMR-associated genes between vapers and smokers, with the shared subset of genes enriched for transcriptional regulation, signaling, tobacco use disorders, and cancer-related pathways. Of significance is the identification of a common hypermethylated DMR at the promoter of "Hypermethylated In Cancer 1" (HIC1), a tumor suppressor gene frequently silenced in smoking-related cancers. Our data support a potential link between epigenomic dysregulation in youth vapers and disease risk. These novel findings have significant implications for public health and tobacco product regulation.

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis.

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (n = 24), osteopenia group (n = 20), and normal bone mass group (n = 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Identification and validation of basement membrane-associated gene AGRN as prognostic and immune-associated biomarkers in colorectal cancer patients.

Colorectal cancer (COCA) has a poor prognosis, with growing evidence implicating basement membranes (BMs) in cancer progression. Our goal was to investigate the role and predictive significance of BMs in COCA patients. We obtained BMs-related genes from cutting-edge research and used TCGA and GTEx databases for mRNA expression and patient information. Cox regression and LASSO regression were used for prognostic gene selection and risk model construction. We compared prognosis using Kaplan-Meier analysis and examined drug sensitivity differences. The CMAP dataset identified potential small molecule drugs. In vitro tests involved suppressing a crucial gene to observe its impact on tumour metastasis. We developed a 12 BMs-based approach, finding it to be an independent prognostic factor. Functional analysis showed BMs concentrated in cancer-associated pathways, correlating with immune cell infiltration and immune checkpoint activation. High-risk individuals exhibited increased drug sensitivity. AGRN levels were linked to decreased progression-free survival (p < 0.001). AGRN knockdown suppressed tumour growth and metastasis. Our study offers new perspectives on BMs in COCA, concluding that AGRN is a dependable biomarker for patient survival and prognosis.