Interpreting omics data with pathway enrichment analysis

Broad repression of DNA repair genes in senescent cells identified by integration of transcriptomic data

Cellular senescence plays a significant role in tissue aging. Senescent cells, which resist apoptosis while remaining metabolically active, generate endogenous DNA-damaging agents, primarily reactive oxygen species. Efficient DNA repair is therefore crucial in these cells, especially when they undergo senescence escape, resuming DNA replication and cellular proliferation. To investigate whether senescent cell transcriptomes reflect adequate DNA repair capacity, we conducted a comprehensive meta-analysis of 60 transcriptomic datasets comparing senescent to proliferating cells. Our analysis revealed a striking downregulation of genes encoding essential components across DNA repair pathways in senescent cells. This includes pathways active in different cell cycle phases such as nucleotide excision repair, base excision repair, nonhomologous end joining and homologous recombination repair of double-strand breaks, mismatch repair and interstrand crosslink repair. The downregulation observed suggests a significant accumulation of DNA lesions. Experimental monitoring of DNA repair readouts in cells that underwent radiation-induced senescence supported this conclusion. This phenomenon was consistent across various senescence triggers and was also observed in primary cell lines from aging individuals. These findings highlight the potential of senescent cells as 'ticking bombs' in aging-related diseases and tumors recurring following therapy-induced senescence.

Decade of TRAP Progress: Insights and Future Prospects for Advancing Functional Network Research in Epilepsy

Targeted Recombination in Active Populations (TRAP) represents an effective and extensively applied technique that has earned significant utilization in neuroscience over the past decade, primarily for identifying and modulating functionally activated neuronal ensembles associated with diverse behaviors. As epilepsy is a neurological disorder characterized by pathological hyper-excitatory networks, TRAP has already been widely applied in epilepsy research. However, the deployment of TRAP in this field remains underexplored, and there is significant potential for further application and development in epilepsy-related investigations. In this review, we embark on a concise examination of the mechanisms behind several TRAP tools, introduce the current applications of TRAP in epilepsy research, and collate the key advantages as well as limitations of TRAP. Furthermore, we sketch out perspectives on potential applications of TRAP in future epilepsy research, grounded in the present landscape and challenges of the field, as well as the ways TRAP has been embraced in other neuroscience domains.

Development and validation of a prognostic prediction model for endometrial cancer based on CD8+ T cell infiltration-related genes

Endometrial cancer (EC) is the most common gynecologic malignancy with increasing incidence and mortality. The tumor immune microenvironment significantly impacts cancer prognosis. Weighted Gene Co-Expression Network Analysis (WGCNA) is a systems biology approach that analyzes gene expression data to uncover gene co-expression networks and functional modules. This study aimed to use WGCNA to develop a prognostic prediction model for EC based on immune cell infiltration, and to identify new potential therapeutic targets. WGCNA was performed using the Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma dataset to identify hub modules associated with T-lymphocyte cell infiltration. Prognostic models were developed using LASSO regression based on genes in these hub modules. The Search Tool for the Retrieval of Interacting Genes/Proteins was used for protein-protein interaction network analysis of the hub module. Gene Set Variation Analysis identified differential gene enrichment analysis between high- and low-risk groups. The relationship between the model and microsatellite instability, tumor mutational burden, and immune cell infiltration was analyzed using The Cancer Genome Atlas data. The model's correlation with chemotherapy and immunotherapy resistance was examined using the Genomics of Drug Sensitivity in Cancer and Cancer Immunome Atlas databases. Immunohistochemical staining of EC tissue microarrays was performed to analyze the relationship between the expression of key genes and immune infiltration. The green-yellow module was identified as a hub module, with 4 genes (ARPC1B, BATF, CCL2, and COTL1) linked to CD8+ T cell infiltration. The prognostic model constructed from these genes showed satisfactory predictive efficacy. Differentially expressed genes in high- and low-risk groups were enriched in tumor immunity-related pathways. The model correlated with EC-related phenotypes, indicating its potential to predict immunotherapeutic response. Basic leucine zipper activating transcription factor-like transcription factor(BATF) expression in EC tissues positively correlated with CD8+ T cell infiltration, suggesting BATF's crucial role in EC development and antitumor immunity. The prognostic model comprising ARPC1B, BATF, CCL2, and COTL1 can effectively identify high-risk EC patients and predict their response to immunotherapy, demonstrating significant clinical potential. These genes are implicated in EC development and immune infiltration, with BATF emerging as a potential therapeutic target for EC.

An overview of key online resources for human genomics: a powerful and open toolbox for in silico research

Recent advances in high-throughput molecular methods have led to an extraordinary volume of genomics data. Simultaneously, the progress in the computational implementation of novel algorithms has facilitated the creation of hundreds of freely available online tools for their advanced analyses. However, a general overview of the most commonly used tools for the in silico analysis of genomics data is still missing. In the current article, we present an overview of commonly used online resources for genomics research, including over 50 tools. This selection will be helpful for scientists with basic or intermediate skills in the in silico analyses of genomics data, such as researchers and students from wet labs seeking to strengthen their computational competencies. In addition, we discuss current needs and future perspectives within this field.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Proteomic and ubiquitinome analysis reveal that microgravity affects glucose metabolism of mouse hearts by remodeling non-degradative ubiquitination

Long-term exposure to a microgravity environment leads to structural and functional changes in hearts of astronauts. Although several studies have reported mechanisms of cardiac damage under microgravity conditions, comprehensive research on changes at the protein level in these hearts is still lacking. In this study, proteomic analysis of microgravity-exposed hearts identified 156 differentially expressed proteins, and ubiquitinomic analysis of these hearts identified 169 proteins with differential ubiquitination modifications. Integrated ubiquitinomic and proteomic analysis revealed that differential proteomic changes caused by transcription affect the immune response in microgravity-exposed hearts. Additionally, changes in ubiquitination modifications under microgravity conditions excessively activated certain kinases, such as hexokinase and phosphofructokinase, leading to cardiac metabolic disorders. These findings provide new insights into the mechanisms of cardiac damage under microgravity conditions.

Metabolomic insights into the multiple stress responses of metabolites in major oilseed crops

The multidimensional significance of metabolomics has gained increasing attention in oilseeds research and development. Sesame, peanut, soybean, sunflower, rapeseed, and perilla are the most important oilseed crops consumed as vegetable oils worldwide. However, multiple biotic and abiotic stressors affect metabolites essential for plant growth, development, and ecological adaptation, resulting in reduced productivity and quality. Stressors can result in dynamic changes in oilseed crops' overall performance, leading to changes in primary (ex: saccharides, lipids, organic acids, amino acids, vitamins, phytohormones, and nucleotides) and secondary (ex: flavonoids, alkaloids, phenolic acids, terpenoids, coumarins, and lignans) major metabolite classes. Those metabolites indicate plant physiological conditions and adaptation strategies to diverse biotic and abiotic stressors. Advancements in targeted and untargeted detection and quantification approaches and technologies aided metabolomics and crop improvement. This review seeks to clarify the metabolomics advancements, significant contributions of metabolites, and specific metabolites that accumulate in reaction to various stressors in oilseed crops. Considering the response of metabolites to multiple stress effects, we compiled comprehensive and combined metabolic biosynthesis pathways for six major classes. Understanding these essential metabolites and pathways can inform molecular breeding strategies to develop resilient oilseed cultivars. Hence, this review highlights metabolomics advancements and metabolites' potential roles in major oilseed crops' biotic and abiotic stress responses.

Innovative omics strategies in fermented fruits and vegetables: Unveiling nutritional profiles, microbial diversity, and future prospects

Fermented fruits and vegetables (FFVs) are not only rich in essential nutrients but also contain distinctive flavors, prebiotics, and metabolites. Although omics techniques have gained widespread recognition as an analytical strategy for FFVs, its application still encounters several challenges due to the intricacies of biological systems. This review systematically summarizes the advances, obstacles and prospects of genomics, transcriptomics, proteomics, metabolomics, and multi-omics strategies in FFVs. It is evident that beyond traditional applications, such as the exploration of microbial diversity, protein expression, and metabolic pathways, omics techniques exhibit innovative potential in deciphering stress response mechanisms and uncovering spoilage microorganisms. The adoption of multi-omics strategies is paramount to acquire a multidimensional network fusion, thereby mitigating the limitations of single omics strategies. Although substantial progress has been made, this review underscores the necessity for a comprehensive repository of omics data and the establishment of universal databases to ensure precision in predictions. Furthermore, multidisciplinary integration with other physical or biochemical approaches is imperative, as it enriches our comprehension of this intricate process.

Using clusterProfiler to characterize multiomics data

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

Two subtle problems with overrepresentation analysis

Motivation

Overrepresentation analysis (ORA) is used widely to assess the enrichment of functional categories in a gene list compared to a background list. ORA is therefore a critical method in the interpretation of 'omics data, relating gene lists to biological functions and themes. Although ORA is hugely popular, we and others have noticed two potentially undesired behaviours of some ORA tools. The first one we call the 'background problem', because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. The second one we call the 'false discovery rate problem', because some tools underestimate the true number of parallel tests conducted.

Results

Here, we demonstrate the impact of these issues on several real RNA-seq datasets and use simulated RNA-seq data to quantify the impact of these problems. We show that the severity of these problems depends on the gene set library, the number of genes in the list, and the degree of noise in the dataset. These problems can be mitigated by changing packages/websites for ORA or by changing to another approach such as functional class scoring.

Availability and implementation

An R/Shiny tool has been provided at https://oratool.ziemann-lab.net/ and the supporting materials are available from Zenodo (https://zenodo.org/records/13823301).

Comprehensive data for studying serum exosome microRNA transcriptome in Parkinson's disease patients

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, was classically attributed to alpha-synuclein aggregation and consequent loss of dopaminergic neurons in the substantia nigra pars compacta. Recently, emerging evidence suggested a broader spectrum of contributing factors, including exosome-mediated intercellular communication, which can potentially serve as biomarkers and therapeutic targets. However, there is a remarkable lack of comprehensive studies that connect the serum exosome microRNA (miRNA) transcriptome with demographic, clinical, and neuroimaging data in PD patients. Here, we present serum exosome miRNA transcriptome data generated from four cohort studies. Two of these studies include 96 PD patients and 80 age- and gender-matched controls, with anonymised demographic, clinical, and neuroimaging data provided for PD patients. The other two studies involve 96 PD patients who were evaluated both before and after one year of treatment with rasagiline, a widely prescribed anti-parkinsonism drug. Together, the datasets provide a valuable source for understanding pathogenesis and discovering biomarkers and therapeutic targets in PD.

Comprehensive time-course gene expression evaluation of high-risk beef cattle to establish immunological characteristics associated with undifferentiated bovine respiratory disease

Bovine respiratory disease (BRD) remains the leading infectious disease in beef cattle production systems. Host gene expression upon facility arrival may indicate risk of BRD development and severity. However, a time-course approach would better define how BRD development influences immunological and inflammatory responses after disease occurrences. Here, we evaluated whole blood transcriptomes of high-risk beef cattle at three time points to elucidate BRD-associated host response. Sequenced jugular whole blood mRNA from 36 cattle (2015: n = 9; 2017: n = 27) across three time points (n = 100 samples; days [D]0, D28, and D63) were processed through ARS-UCD1.2 reference-guided assembly (HISAT2/Stringtie2). Samples were categorized into BRD-severity cohorts (Healthy, n = 14; Treated 1, n = 11; Treated 2+, n = 11) via frequency of antimicrobial clinical treatment. Assessment of gene expression patterns over time within each BRD cohort was modeled through an autoregressive hidden Markov model (EBSeq-HMM; posterior probability ≥ 0.5, FDR < 0.01). Mixed-effects negative binomial models (glmmSeq; FDR < 0.05) and edgeR (FDR < 0.10) identified differentially expressed genes between and across cohorts overtime. A total of 2,580, 2,216, and 2,381 genes were dynamically expressed across time in Healthy, Treated 1, and Treated 2+ cattle, respectively. Genes involved in the production of specialized resolving mediators (SPMs) decreased at D28 and then increased by D63 across all three cohorts. Accordingly, SPM production and alternative complement were differentially expressed between Healthy and Treated 2+ at D0, but not statistically different between the three groups by D63. Magnitude, but not directionality, of gene expression related to SPM production, alternative complement, and innate immune response signified Healthy and Treated 2+ cattle. Differences in gene expression at D63 across the three groups were related to oxygen binding and carrier activity, natural killer cell-mediated cytotoxicity, cathelicidin production, and neutrophil degranulation, possibly indicating prolonged airway pathology and inflammation weeks after clinical treatment for BRD. These findings indicate genomic mechanisms indicative of BRD development and severity over time.

Comparative analysis of single-cell pathway scoring methods and a novel approach

Single-cell gene set analysis (scGSA) provides a useful approach for quantifying molecular functions and pathways in high-throughput transcriptomic data, facilitating the biological interpretation of complex human datasets. However, various factors such as gene set size, quality of the gene sets and the dropouts impact the performance of scGSA. To address these limitations, we present a single-cell Pathway Score (scPS) method to measure gene set activity at single-cell resolution. Furthermore, we benchmark our method with six other methods: AUCell, AddModuleScore, JASMINE, UCell, SCSE and ssGSEA. The comparison across all the methods using two different simulation approaches highlights the effect of cell count, gene set size, noise, condition-specific genes and zero imputation on their performance. The results of our study indicate that the scPS is comparable with other single-cell scoring methods and detects fewer false positives. Importantly, this work reveals critical variables in the scGSA.

Deciphering molecular basis of pesticide-induced recurrent pregnancy loss: insights from transcriptomics analysis

Recent studies have revealed a notable connection between pesticide exposure and Recurrent Pregnancy Loss (RPL), yet the precise molecular underpinning of this toxicity remains elusive. Through the alignment of Differentially Expressed Genes (DEGs) of healthy and RPL patients with the target genes of 9 pesticide components, we identified a set of 12 genes responsible for RPL etiology. Interestingly, biological process showed that besides RPL, those 12 genes also associated with preeclampsia and cardiovascular disease. Enrichment analysis showed the engagement of these genes associated with essential roles in the molecular transport of small molecules, as well as the aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, mineral absorption, ion homeostasis, and ion transport by P-type ATPases. Notably, the crosstalk targets between pesticide components played crucial roles in influencing RPL results, suggesting a role in attenuating pesticide agents that contribute to RPL. It is important to note that non-significant concentration of the pesticide components observed in both control and RPL samples should not prematurely undermine the potential for pesticides to induce RPL in humans. This study emphasizes the complexity of pesticide induced RPL and highlights avenues for further research and precautionary measures.

Generalized reporter score-based enrichment analysis for omics data

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Lipid emulsion structure, digestion behavior, physiology, and health: a scoping review and future directions

Research investigating the effects of the food matrix on health is needed to untangle many unresolved questions in nutritional science. Emulsion structure plays a fundamental role in this inquiry; however, the effects of oil-in-water emulsion structure on broad metabolic, physiological, and health-related outcomes have not been comprehensively reviewed. This systematic scoping review targets this gap and examines methodological considerations for the field of relating food structure and health. MEDLINE, Web of Science, and CAB Direct were searched from inception to December 2022, returning 3106 articles, 52 of which were eligible for inclusion. Many investigated emulsion lipid droplet size and/or gastric colloidal stability and their relation to postprandial weight-loss-related outcomes. The present review also identifies numerous novel relationships between emulsion structures and health-related outcomes. "Omics" endpoints present an exciting avenue for more comprehensive analysis in this area, yet interpretation remains difficult. Identifying valid surrogate biomarkers for long-term outcomes and disease risk will be a turning point for food structure research, leading to breakthroughs in the pace and utility of research that generates advancements in health. The review's findings and recommendations aim to support new hypotheses, future trial design, and evidence-based emulsion design for improved health and well-being.

An R package for Survival-based Gene Set Enrichment Analysis

Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease's survival. We developed an R package and corresponding Shiny App called SGSEA for this analysis and presented a study of kidney renal clear cell carcinoma (KIRC) to demonstrate the approach. In Gene Set Enrichment Analysis (GSEA), the log-fold change in expression between treatments is used to rank genes, to determine if a biological function has a non-random distribution of altered gene expression. SGSEA is a variation of GSEA using the hazard ratio instead of a log fold change. Our study shows that pathways enriched with genes whose increased transcription is associated with mortality (NES > 0, adjusted p-value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This approach allows researchers to quickly identify disease variant pathways for further research and provides supplementary information to standard GSEA, all within a single R package or through using the convenient app.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

Combined bulk RNA and single-cell RNA analyses reveal TXNL4A as a new biomarker for hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) has a high mortality rate worldwide. The dysregulation of RNA splicing is a major event leading to the occurrence, progression, and drug resistance of cancer. Therefore, it is important to identify new biomarkers of HCC from the RNA splicing pathway.

Methods

We performed the differential expression and prognostic analyses of RNA splicing-related genes (RRGs) using The Cancer Genome Atlas-liver hepatocellular carcinoma (LIHC). The International Cancer Genome Consortium (ICGC)-LIHC dataset was used to construct and validate prognostic models, and the PubMed database was used to explore genes in the models to identify new markers. The screened genes were subjected to genomic analyses, including differential, prognostic, enrichment, and immunocorrelation analyses. Single-cell RNA (scRNA) data were used to further validate the immunogenetic relationship.

Results

Of 215 RRGs, we identified 75 differentially expressed prognosis-related genes, and a prognostic model incorporating thioredoxin like 4A (TXNL4A) was identified using least absolute shrinkage and selection operator regression analysis. ICGC-LIHC was used as a validation dataset to confirm the validity of the model. PubMed failed to retrieve HCC-related studies on TXNL4A. TXNL4A was highly expressed in most tumors and was associated with HCC survival. Chi-squared analyses indicated that TXNL4A expression positively correlated positively with the clinical features of HCC. Multivariate analyses revealed that high TXNL4A expression was an independent risk factor for HCC. Immunocorrelation and scRNA data analyses indicated that TXNL4A was correlated with CD8 T cell infiltration in HCC.

Conclusion

Therefore, we identified a prognostic and immune-related marker for HCC from the RNA splicing pathway.