Differential Co-expression Analysis Research Articles

Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs. MethodsData from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA–mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC. ResultsWe identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment. ConclusionWe successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.

Profiling of long non-coding RNAs in hippocampal–entorhinal system subfields: impact of RN7SL1 on neuroimmune response modulation in Alzheimer’s disease

Alzheimer’s disease (AD) is recognized as the predominant cause of dementia, and neuroimmune processes play a pivotal role in its pathological progression. The involvement of long non-coding RNAs (lncRNAs) in AD has attracted widespread attention. Herein, transcriptomic analysis of 262 unique samples extracted from five hippocampal–entorhinal system subfields of individuals with AD pathology and without AD pathology revealed distinctive lncRNA expression profiles. Through differential expression and coexpression analyses, we identified 16 pivotal lncRNAs. Notably, RN7SL1 knockdown significantly modulated microglial responses upon oligomeric amyloid-β stimulation, resulting in a considerable decrease in proinflammatory cytokine production and subsequent neuronal damage. These findings highlight RN7SL1 as an essential neuroimmune-related lncRNA that could serve as a prospective target for AD diagnosis and treatment.

ShinyExprPortal: a configurable 'shiny' portal for sharing analysis of molecular expression data.

The scale of omics research presents many obstacles to full sharing and access to analysis results. Current publication models impose limits on the number of pages and figures, requiring careful preparation and selection of content. At the same time, depositing data in open repositories significantly shifts the burden of access and reproduction to readers, who may include people who are not programmers or analysts. We introduce shinyExprPortal, an R package that implements omics web portals with minimal coding effort. The portals allow exploration of transcriptomic or proteomic expression data and phenotypes, showcasing results of various types of analysis including differential expression, co-expression and pathways analysis. The integration with bioinformatics workflows enables researchers to focus on their results and share findings using interactive and publication-quality plots. The shinyExprPortal package is available to download and install from CRAN and https://github.com/C4TB/shinyExprPortal.

Abstract 1140: A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer

Abstract Gastric intestinal metaplasia (GIM) is a precursor lesion for the intestinal subtype of gastric cancer (GC). A risk stratification tool is the Operative Link on GIM (OLGIM), a system that relies on histopathologic annotation of gastric biopsies. Advanced OLGIM (stages III and IV) have the highest risk of progression to GC. Molecular biomarkers for advanced OLGIM are lacking. We explored transcriptomics biomarkers for advanced OLGIM to aid risk assessment efforts among GC precursors.We used clinical and genomic data from four cohorts: 1) GAPS, a cohort of OLGIM-staged patients (N=303); 2) a subset of confirmed intestinal-type GC from The Cancer Genome Atlas (TCGA, N=198); 3) a compilation of scRNA-seq data (N=40), and 4) a spatial transcriptomics cohort (N=5) with annotated H&E-stained histopathology slides from GC or OLGIM-staged patients.We separated the GAPS bulk RNAseq gene expression dataset into independent discovery (N=88) and validation (N=215) sets. Through a combination of differential expression analysis and co-expression analysis, we identified a discrete set of 100 significantly upregulated genes characterizing advanced OLGIM that were confirmed in the validation set. Importantly, these genes were overexpressed both in the corpus and antrum of the stomach. We leveraged spatial transcriptomics assays to refine this signature to genes overexpressed specifically in GIM foci. We interrogated the TCGA dataset and confirmed these genes to be also expressed in gastric tumors. The final high-risk signature included 26 genes. Single-cell RNAseq analysis revealed these genes to be expressed by aberrant intestinal-like lineages. Further, reference-mapping identified cells resembling mature enterocytes and goblet cells that expressed 16 of these genes, whereas 10 genes from the high-risk signature pinpoint immature intestinal stem-like cells at varying stages of differentiation (duodenum stem cells, duodenum differentiating stem cells, and duodenum transit amplifying cells). Finally, we confirmed expression of 12 of these genes by single-molecule fluorescence in situ hybridization (smFISH), that distinguish differentiated from stem-like lineages in metaplasia and are absent in normal gastric glands in intact tissue sections from GC precursors.Our study identified as signature of 26 genes that are: i) characteristically over-expressed in advanced OLGIM stages, ii) localize specifically to metaplasia, iii) are expressed in gastric tumors, and iv) distinguish differentiated from stem-like cells in GIM. These results provide transcriptomics biomarkers that indicate the establishment and expansion of intestinal stem-like cells in advanced OLGIM, with profound translational implications, as these cells may harbor and expand mutations that predispose advanced OLGIM patients to GC. Citation Format: Ignacio A. Wichmann, Robert J. Huang, Andrew Su, Anuja Sathe, Miranda V. Shum, Susan M. Grimes, Rithika Meka, Alison Almeda, Xiangqi Bai, Jeanne Shen, Quan Nguyen, Manuel Amieva, Joo Ha Hwang, Hanlee P. Ji. A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1140.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals.

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.

Herpesvirus latent infection promotes stroke via activating the OTUD1/NF-κB signaling pathway.

Our study aimed to reveal the molecular mechanisms underlying the regulation of cerebral infarction by herpes virus latency infection via the OTUD1/NF-κB signaling pathway using evidence-based medicine Meta-analysis and bioinformatics analysis. We conducted a Meta-analysis by searching Pubmed, Embase, and Web of Science databases to evaluate the correlation between herpes virus infection and increased risk of cerebral infarction. We obtained wild-type or mutant herpes virus latent infection-related brain tissue datasets from the GEO database and performed differential analysis to identify differentially expressed genes (DEGs) in the brain tissue after herpes virus latent infection. We further conducted WGCNA co-expression analysis on the cerebral infarction-related datasets from the GEO database to obtain key module genes and intersect them with the DEGs. We used ROC curve analysis to identify the key gene OTUD1 for predicting the occurrence of cerebral infarction and combined correlation and pathway enrichment analyses to identify the downstream pathways regulated by OTUD1. Our meta-analysis revealed that herpes virus infection is associated with an increased risk of cerebral infarction. By integrating the differential analysis and WGCNA co-expression analysis of GEO chip data, we identified three key genes mediating cerebral infarction after herpes virus latent infection. ROC curve analysis identified the key gene OTUD1, and the correlation and pathway enrichment analyses showed that OTUD1 regulates the NF-κB signaling pathway to mediate cerebral infarction. Herpes virus latent infection promotes cerebral infarction by activating the OTUD1/NF-κB signaling pathway.

Identification of stromal cell proportion-related genes in the breast cancer tumor microenvironment using CorDelSFS feature selection: implications for tumor progression and prognosis.

Background: The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear. Objective: The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis. Methods: A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated. Results: TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses. Conclusions: This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.

Comparative transcriptomic analysis of male and females in the dioecious weeds Amaranthus palmeri and Amaranthus tuberculatus

BackgroundWaterhemp (Amaranthus tuberculatus (Moq.) Sauer) and Palmer amaranth (Amaranthus palmeri S. Wats.) are two dioecious and important weed species in the world that can rapidly evolve herbicide-resistance traits. Understanding these two species' dioecious and sex-determination mechanisms could open opportunities for new tools to control them. This study aims to identify the differential expression patterns between males and females in A. tuberculatus and A. palmeri. Multiple analyses, including differential expression, co-expression, and promoter analyses, used RNA-seq data from multiple tissue types to identify putative essential genes for sex determination in both dioecious species.ResultsGenes were identified as potential key players for sex determination in A. palmeri. Genes PPR247, WEX, and ACD6 were differentially expressed between the sexes and located at scaffold 20 within or near the male-specific Y (MSY) region. Multiple genes involved with flower development were co-expressed with these three genes. For A. tuberculatus, no differentially expressed gene was identified within the MSY region; however, multiple autosomal class B and C genes were identified as differentially expressed and possible candidate genes.ConclusionsThis is the first study comparing the global expression profile between males and females in dioecious weedy Amaranthus species. Results narrow down putative essential genes for sex-determination in A. palmeri and A. tuberculatus and also strengthen the hypothesis of two different evolutionary events for dioecy within the genus.

Identification and validation of long noncoding RNA AC083900.1 and RP11-283C24.1 for prediction of progression of osteosarcoma

BackgroundThe role of cuproptosis, an emerging cell death pathway that makes a remarkable contribution to tumor progression, remains elusive in osteosarcoma (OS), in addition to its regulator, including long-no-coding RNAs (lncRNAs) that are also a critical factor for fueling OS. MethodsTranscriptome and clinical data from 70 normal human bone tissue samples and 84 frozen clinical osteosarcoma samples were included in this study. Cuproptosis-associated lncRNAs (CRlncs) were identified through differential expression and co-expression analyses. Univariate Cox regression was performed to screen for prognostic lncRNAs, then we used least absolute shrinkage and selection operator regression to distinguish prognosis-related CRlncs (AC083900.1 and RP11-283C24.1) for modeling the CRlncs prognostic signature (CLPS) by multivariate Cox regression using the stepwise method. CLPS performance was tested by independent prognostic analyses, survival curve and receiver operating characteristic (ROC) curve. In addition, the molecular and immune mechanisms that underlie the unfavorable prognosis of CLPS-identified high-risk group were elucidated. ResultAC083900.1 and RP11–283C24.1 have been identified as the most important CRlncs for OS progression (hazard ratio: 3.498 and 2.724, respectively), and the derived CLPS demonstrated outstanding performance for the prediction of OS prognosis (AUC of 0.799 and 0.778 in the training and test sets, both adj-p < 0.05 in survival curve). As was anticipated, CLPS also outperformed a recent clinical prognostic approach that only achieved an AUC of 0.682 [metastasis]. It is notable that AC083900.1 progressed OS metastasis, evidenced by its high expression in metastatic OS, its high correlation to metastasis-related genes, and its high AUC of 0.683 for the prediction of metastasis. Mechanistically, AC083900.1 and RP11–283C24.1 dysregulated many critical biological processes regarding humoral immune response, immunoglobulin complex, etc.; while reducing the infiltration of many cytotoxic immune cells (B-cells, TIL, neutrophils, etc.). It is encouraging that BMS-509744 and KIN001–135 demonstrated high therapeutic implications for CLPS-identified high-risk OS, and the low-risk counterpart was sensitive to SB-216763. Quantitative RT-PCR analysis showed that both AC083900.1 and RP11-283C24.1 were significantly upregulated in different osteosarcoma cell lines. ConclusionThis study elucidated the roles and mechanisms of AC083900.1 and RP11-283C24.1 in the development of OS, fostering a reliable prognostic approach and treatment for OS patients.

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level.We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition.We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Differential expression and differential coexpression analyses revealed altered gene expressions and pathways in atypical Alzheimer’s Disease sub‐cohort brains

AbstractBackgroundIn the Alzheimer’s disease (AD) brain, the hyperphosphorylated tau protein forms neurofibrillary tangles (NFTs), which extent usually matches the dementia severity. However, for some patients, this is not the case. which we name them as asymptomatic AD (AsymAD, heavy NFTs but relatively normal cognition) and Low‐NFT AD (light NFT but severe dementia). We aim to characterize these two subtypes’ transcriptome and proteome, to understand the role of tauopathy in the AD etiology and identify resilient/vulnerable factors contributing to the cognitive decline as well as the potential drug targets for the precision treatment of AD.MethodWe performed differential analyses on both RNA and protein expression data from the atypical groups of two separate datasets ROSMAP and MSBB, using typical AD sub‐cohort as reference. In addition, highly correlated gene/protein co‐expression network modules were mined for each of the four datasets. Enrichment analysis identified biological processes and functions associated with the differentially expressed genes (DEGs) and co‐expressed modules.ResultFive DEG targets were tested in human AsymAD samples and compared with age‐matched controls and typical AD. Using western blot and qRT‐PCR, we confirmed the altered gene expressions in PSCK1 which is related to vesicle trafficking and neuron secretory pathway. Moreover, oppositely regulated pathways were identified for the two subtypes, such as oxidative phosphorylation pathway, neuron CREB signaling, necroptosis signaling pathway. Coexpression analysis also revealed that several coexpressed modules have significant altered expressions in the atypical AD subtypes as compared to typical AD group and are correlated with the disease severeness. For example, the protein modules from both ROSMAP and MSBB, which are enriched with mitochondrial functions and negatively correlated with disease severeness, are significantly elevated in Asym AD group than that of the typical AD or Low‐tau AD group.ConclusionWe have identified several altered pathways and genes that potentially render neuron protection or vulnerability. Identifying key factors in such uncoupling between tauopathy and cognitive impairment will not only improve precision medicine in AD, but also provide insights to prevent or slow down cognitive decline during AD progression. Further validations in Drosophila will elucidate their roles in the pathology or disease progression.

ScViewer: An Interactive Single-Cell Gene Expression Visualization Tool.

Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.

Proteomics Profiling Reveals the Molecular Signatures and Potential Therapeutic Targets of Human Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC), a malignant tumor distinctly characterized by ethnic and geographic distribution, is highly prevalent in Southern China and Southeast Asia. However, the molecular mechanisms of NPC have not been fully revealed at the proteomic level. In this study, 30 primary NPC samples and 22 normal nasopharyngeal epithelial tissues were collected for proteomics analysis, and a relatively complete proteomics landscape of NPC was depicted for the first time. By combining differential expression analysis, differential co-expression analysis, and network analysis, potential biomarkers and therapeutic targets were identified. Some identified targets were verified by biological experiments. We found that 17-AAG, a specific inhibitor of the identified target heat shock protein 90 (HSP90), could be a potential therapeutic drug for NPC. Finally, consensus clustering identified two NPC subtypes with specific molecular features. The subtypes and the related molecules were verified by an independent data set and may have different progression-free survival. The results of this study provide a comprehensive understanding of the proteomics molecular signatures of NPC and provide new perspectives and inspiration for prognostic determination and treatment of NPC.

Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals will die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, GZMB and fibronectin), immune suppression (eg, FOXP3), exhaustion and cytotoxicity (eg, CD8), PD-L1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.

Sex-specific gene expression patterns in head and neck squamous cell carcinomas

ObjectiveThe head and neck squamous cell carcinomas (HNSCCs) have higher incidence rates in men, but the reasons are still obscure. This study aimed to investigate the sex-specific gene expression patterns and predict the regulatory mechanisms. DesignData including clinical, survival, RNA-seq, miRNA, and methylation information were derived from The Cancer Genome Atlas (TCGA). A total of 131 paired male and female cases were included based on propensity score matching. We concentrated on the prognostic values of the sex-specific pathways enriched by differentially expressed genes (DEGs) and predicted the potential regulatory mechanisms from immune cell infiltration, ceRNA regulatory network, methylation, and differential coexpression analysis. ResultsCompared with females, males exhibited a lower activity of immune-related functions and higher activities of mitochondrial and ubiquitination functions. The pathway activities were associated with the prognosis of males but less relevant to females. We extracted eight pathways with sex-biased survival patterns, of which five were about down-regulated immune functions, and three were up-regulated pathways (GTP biosynthetic, DNA polymerase, and spliceosomal complex assembly). The five immune pathways were moderately or strongly correlated with the proportion of macrophages. We identified six over-expressed lncRNAs that might be involved in the regulation of the three up-regulated pathways. These lncRNAs exhibited a lower methylation density in males, which might account for their over-expression. ConclusionsFor HNSCCs, males were characterized by immunosuppression. It was a sign of unfavorable prognosis and might be associated the proportion of macrophages. LncRNAs and methylation might be involved in the regulation of these pathways.

Pan-cancer analysis reveals signal transducer and activator of transcription (STAT) gene family as biomarkers for prognostic prediction and therapeutic guidance.

Background: The signal transducer and activator of transcription (STAT) gene family have been widely found to regulate cell proliferation, differentiation, apoptosis, and angiogenesis through complex signaling pathways, and thus impacting tumor formation and development in different types of tumor. However, the roles of STATs on prognostic prediction and therapeutic guidance in pan-cancer remain unexplored. Materials and Methods: The dataset of 33 types of TCGA tumor, para-carcinoma and normal tissues, was obtained from the UCSC Xena database, including the gene expression profiles in the formats of FPKM value, demographic characteristics, clinical information, and survival data of STATs. Differential expression and co-expression analyses, WGCNA, clinical relevance analysis, immune subtype analysis, tumor stemness analysis, tumor purity analysis, immune infiltration analysis, immunotherapy related analysis, tumor mutation related analysis, and drug sensitivity analysis were performed by R software. Results: Differential expression of STAT1 was found between normal and BRCA tissues (p < 0.001, log2FC = 0.895). Additionally, the strongest correlation among STATs lied between STAT1 and STAT2 (correlation coefficient = 0.6). Moreover, high expression levels of STAT1 (p = 0.031) were revealed to be notably correlated with poor prognosis in KIRP. In addition, STAT1 expressed the highest value in immune subtypes C1, C2, C3, and C6 in LUAD. What's more, strong negative correlations were demonstrated between expression of STAT6 and mDNAss and mRNAss of TGCT. Additionally, STAT4 expression was characterized to be significantly negatively correlated with tumor purity of the majority of cancer types. Moreover, STAT1 and STAT3 were shown to be generally high-expressed in pan-cancer myeloid cells, and STATs all had positive correlation with the infiltration of the majority of immune cells. In addition, STATs were revealed to be closely linked with immunotherapy response. What's more, STAT4 expression was identified to have a strong negative correlation with TMB value in DLBC. Last but not least, positive correlations were accessed between STAT5 and sensitivity of Nelarabine (cor = 0.600, p < 0.001). Conclusion: In the present study, we identified STATs as biomarkers for prognostic prediction and therapeutic guidance in pan-cancer. Hopefully our findings could provide a valuable reference for future STATs research and clinical applications.

RNA-seq analysis reveals the critical role of the novel lncRNA BIANCR in intramuscular adipogenesis through the ERK1/2 signaling pathway

BackgroundLong non-coding RNAs (lncRNAs) regulate numerous biological processes, including adipogenesis. Research on adipogenesis will assist in the treatment of human metabolic diseases and improve meat quality in livestock, such as the content of intramuscular fat (IMF). However, the significance of lncRNAs in intramuscular adipogenesis remains unclear. This research aimed to reveal the lncRNAs transcriptomic profiles in the process of bovine intramuscular adipogenesis and to identify the lncRNAs involved in the adipogenesis of bovine intramuscular adipocytes.ResultsIn this research, a landscape of lncRNAs was identified with RNA-seq in bovine intramuscular adipocytes at four adipogenesis stages (0 d, 3 d, 6 d, and 9 d after differentiation). A total of 7035 lncRNAs were detected, including 3396 novel lncRNAs. Based on the results of differential analysis, co-expression analysis, and functional prediction, we focused on the bovine intramuscular adipogenesis-associated long non-coding RNA (BIANCR), a novel lncRNA that may have an important regulatory function. The knockdown of BIANCR inhibited proliferation and promoted apoptosis of intramuscular preadipocytes. Moreover, BIANCR knockdown inhibited intramuscular adipogenesis by regulating the ERK1/2 signaling pathway.ConclusionThis study obtained the landscape of lncRNAs during adipogenesis in bovine intramuscular adipocytes. BIANCR plays a crucial role in adipogenesis through the ERK1/2 signaling pathway. The results are noteworthy for improving beef meat quality, molecular breeding, and metabolic disease research.Graphical

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect.