Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes.

Background: Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. Methods: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. Results: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. Conclusion: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.

STK3 higher expression association with clinical characteristics in intrinsic subtypes of breast cancer invasive ductal carcinoma patients.

PURPOSE: STK3 has a central role in maintaining cell homeostasis, proliferation, growth, and apoptosis. Previously, we investigated the functional link between STK3/MST2, and estrogen receptor in MCF-7 breast cancer cells. To expand the investigation, this study evaluated STK3's higher expression and associated genes in breast cancer intrinsic subtypes using publicly available data. METHODS: The relationship between clinical pathologic features and STK3 high expression was analyzed using descriptive and multivariate analysis. RESULTS: Increased STK3 expression in breast cancer was significantly associated with higher pathological cancer stages, and a different expression level was observed in the intrinsic subtypes of breast cancer. Kaplan-Meier analysis showed that breast cancer with high STK3 had a lower survival rate in IDC patients than that with low STK3 expression (p < 0.05). The multivariate analysis unveiled a strong correlation between STK3 expression and the survival rate among IDC patients, demonstrating hazard ratios for lower expression. In the TCGA dataset, the hazard ratio was 0.56 (95% CI 0.34-0.94, p = 0.029) for patients deceased with tumor, and 0.62 (95% CI 0.42-0.92, p = 0.017) for all deceased patients. Additionally, in the METABRIC dataset, the hazard ratio was 0.76 (95% CI 0.64-0.91, p = 0.003) for those deceased with tumor. From GSEA outcomes 7 gene sets were selected based on statistical significance (FDR < 0.25 and p < 0.05). Weighted Sum model (WSM) derived top 5% genes also have higher expression in basal and lower in luminal A in association with STK3. CONCLUSION: By introducing a novel bioinformatics approach that combines GSEA and WSM, the study successfully identified the top 5% of genes associated with higher expression of STK3.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets.

Background: Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets. Methods: According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared. Results: Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients. Conclusions: Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

Immune gene signatures as prognostic criteria for cancer patients.

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

Identification of a novel immune-related transcriptional regulatory network in sarcopenia.

BACKGROUND: Sarcopenia is highly prevalent in elderly individuals and has a significant adverse effect on their physical health and quality of life, but the mechanisms remain unclear. Studies have indicated that transcription factors (TFs) and the immune microenvironment play a vital role in skeletal muscle atrophy. METHODS: RNA-seq data of 40 muscle samples were downloaded from the GEO database. Then, differentially expressed genes (DEGs), TFs(DETFs), pathways(DEPs), and the expression of immune gene sets were identified with limma, edgeR, GO, KEGG, ORA, GSVA, and ssGSEA. Furthermore, the results above were integrated into coexpression analysis by Pearson correlation analysis (PCA). Significant coexpression patterns were used to construct the immune-related transcriptional regulatory network by Cytoscape and potential medicine targeting the network was screened by Connectivity Map. Finally, the regulatory mechanisms and RNA expression of DEGs and DETFs were identified by multiple online databases and RT‒qPCR. RESULTS: We screened 808 DEGs (log2 fold change (FC) > 1 or < - 1, p < 0.05), 4 DETFs (log2FC > 0.7 or < - 0.7, p < 0.05), 304 DEPs (enrichment scores (ES) > 1 or < - 1, p < 0.05), and 1208 differentially expressed immune genes sets (DEIGSs) (p < 0.01). Based on the results of PCA (correlation coefficient (CC) > 0.4 or < - 0.4, p < 0.01), we then structured an immune-related network with 4 DETFs, 9 final DEGs, 11 final DEPs, and 6 final DEIGSs. Combining the results of online databases and in vitro experiments, we found that PAX5-SERPINA5-PI3K/Akt (CC ≤ 0.444, p ≤ 0.004) was a potential transcriptional regulation axis, and B cells (R = 0.437, p = 0.005) may play a vital role in this signal transduction. Finally, the compound of trichostatin A (enrichment = -0.365, specificity = 0.4257, p < 0.0001) might be a potential medicine for sarcopenia based on the PubChem database and the result of the literature review. CONCLUSIONS: We first identified immune-related transcriptional regulatory network with high-throughput RNA-seq data in sarcopenia. We hypothesized that PAX5-SERPIAN5-PI3K/Akt axis is a potential mechanism in sarcopenia and that B cells may play a vital role in this signal transduction. In addition, trichostatin A might be a potential medicine for sarcopenia.

Subtyping based on immune cell fractions reveal heterogeneity of cardiac fibrosis in end-stage heart failure.

Background: A central issue hindering the development of effective anti-fibrosis drugs for heart failure is the unclear interrelationship between fibrosis and the immune cells. This study aims at providing precise subtyping of heart failure based on immune cell fractions, elaborating their differences in fibrotic mechanisms, and proposing a biomarker panel for evaluating intrinsic features of patients' physiological statuses through subtype classification, thereby promoting the precision medicine for cardiac fibrosis. Methods: We inferred immune cell type abundance of the ventricular samples by a computational method (CIBERSORTx) based on ventricular tissue samples from 103 patients with heart failure, and applied K-means clustering to divide patients into two subtypes based on their immune cell type abundance. We also designed a novel analytic strategy: Large-Scale Functional Score and Association Analysis (LAFSAA), to study fibrotic mechanisms in the two subtypes. Results: Two subtypes of immune cell fractions: pro-inflammatory and pro-remodeling subtypes, were identified. LAFSAA identified 11 subtype-specific pro-fibrotic functional gene sets as the basis for personalised targeted treatments. Based on feature selection, a 30-gene biomarker panel (ImmunCard30) established for diagnosing patient subtypes achieved high classification performance, with the area under the receiver operator characteristic curve corresponding to 0.954 and 0.803 for the discovery and validation sets, respectively. Conclusion: Patients with the two subtypes of cardiac immune cell fractions were likely having different fibrotic mechanisms. Patients' subtypes can be predicted based on the ImmunCard30 biomarker panel. We envision that our unique stratification strategy revealed in this study will unravel advance diagnostic techniques for personalised anti-fibrotic therapy.

A Ribosome-Related Prognostic Signature of Breast Cancer Subtypes Based on Changes in Breast Cancer Patients' Immunological Activity.

Background and Objectives. The prognostic role of adjacent nontumor tissue in patients with breast cancer (BC) is still unclear. The activity changes in immunologic and hallmark gene sets in normal tissues adjacent to BC may play a crucial role in predicting the prognosis of BC patients. The aim of this study was to identify BC subtypes and ribosome-associated prognostic genes based on activity changes of immunologic and hallmark gene sets in tumor and adjacent nontumor tissues to improve patient prognosis. Materials and Methods. Gene set variation analysis (GSVA) was applied to assess immunoreactivity changes in the overall sample and three immune-related BC subtypes were identified by non-negative matrix factorization (NMF). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses were after determining the prognostic gene set using the least absolute shrinkage and selection operator (LASSO) method. Ribosome-related genes were identified by PPI (protein-protein interaction) analysis, and finally a prognostic risk model was constructed based on the expression of five ribosomal genes (RPS18, RPL11, PRLP1, RPL27A, and RPL38). Results. A comprehensive analysis of immune and marker genomic activity changes in normal breast tissue and BC tissue identified three immune-related BC subtypes. BC subtype 1 has the best prognosis, and subtype 3 has the worst overall survival rate. We identified a prognostic gene set in nontumor tissue by the least absolute shrinkage and selection operator (LASSO) method. We found that the results of both KEGG and GO analyses were indistinguishable from those of ribosome-associated genes. Finally, we determined that genes associated with ribosomes exhibit potential as a reliable predictor of overall survival in breast cancer patients. Conclusions. Our research provides an important guidance for the treatment of BC. After a mastectomy, the changes in gene set activity of both BC tissues and the nontumor tissues adjacent to it should be thoroughly evaluated, with special attention to changes in ribosome-related genes in the nontumor tissues.

Investigation of androgen receptor-dependent alternative splicing has identified a unique subtype of lethal prostate cancer.

A complete proteomics study characterizing active androgen receptor (AR) complexes in prostate cancer (PCa) cells identified a diversity of protein interactors with tumorigenic annotations, including known RNA splicing factors. Thus, we chose to further investigate the functional role of AR-mediated alternative RNA splicing in PCa disease progression. We selected two AR-interacting RNA splicing factors, Src associated in mitosis of 68 kDa (SAM68) and DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) to examine their associative roles in AR-dependent alternative RNA splicing. To assess the true physiological role of AR in alternative RNA splicing, we assessed splicing profiles of LNCaP PCa cells using exon microarrays and correlated the results to PCa clinical datasets. As a result, we were able to highlight alternative splicing events of clinical significance. Initial use of exon-mini gene cassettes illustrated hormone-dependent AR-mediated exon-inclusion splicing events with SAM68 or exon-exclusion splicing events with DDX5 overexpression. The physiological significance in PCa was investigated through the application of clinical exon array analysis, where we identified exon-gene sets that were able to delineate aggressive disease progression profiles and predict patient disease-free outcomes independently of pathological clinical criteria. Using a clinical dataset with patients categorized as prostate cancer-specific death (PCSD), these exon gene sets further identified a select group of patients with extremely poor disease-free outcomes. Overall, these results strongly suggest a nonclassical role of AR in mediating robust alternative RNA splicing in PCa. Moreover, AR-mediated alternative spicing contributes to aggressive PCa progression, where we identified a new subtype of lethal PCa defined by AR-dependent alternative splicing.

Investigation of androgen receptor-dependent alternative splicing has identified a unique subtype of lethal prostate cancer / 亚洲男科学杂志(英文版)

A complete proteomics study characterizing active androgen receptor (AR) complexes in prostate cancer (PCa) cells identified a diversity of protein interactors with tumorigenic annotations, including known RNA splicing factors. Thus, we chose to further investigate the functional role of AR-mediated alternative RNA splicing in PCa disease progression. We selected two AR-interacting RNA splicing factors, Src associated in mitosis of 68 kDa (SAM68) and DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) to examine their associative roles in AR-dependent alternative RNA splicing. To assess the true physiological role of AR in alternative RNA splicing, we assessed splicing profiles of LNCaP PCa cells using exon microarrays and correlated the results to PCa clinical datasets. As a result, we were able to highlight alternative splicing events of clinical significance. Initial use of exon-mini gene cassettes illustrated hormone-dependent AR-mediated exon-inclusion splicing events with SAM68 or exon-exclusion splicing events with DDX5 overexpression. The physiological significance in PCa was investigated through the application of clinical exon array analysis, where we identified exon-gene sets that were able to delineate aggressive disease progression profiles and predict patient disease-free outcomes independently of pathological clinical criteria. Using a clinical dataset with patients categorized as prostate cancer-specific death (PCSD), these exon gene sets further identified a select group of patients with extremely poor disease-free outcomes. Overall, these results strongly suggest a nonclassical role of AR in mediating robust alternative RNA splicing in PCa. Moreover, AR-mediated alternative spicing contributes to aggressive PCa progression, where we identified a new subtype of lethal PCa defined by AR-dependent alternative splicing.

Multiset multicover methods for discriminative marker selection.

Markers are increasingly being used for several high-throughput data analysis and experimental design tasks. Examples include the use of markers for assigning cell types in scRNA-seq studies, for deconvolving bulk gene expression data, and for selecting marker proteins in single-cell spatial proteomics studies. Most marker selection methods focus on differential expression (DE) analysis. Although such methods work well for data with a few non-overlapping marker sets, they are not appropriate for large atlas-size datasets where several cell types and tissues are considered. To address this, we define the phenotype cover (PC) problem for marker selection and present algorithms that can improve the discriminative power of marker sets. Analysis of these sets on several marker-selection tasks suggests that these methods can lead to solutions that accurately distinguish different phenotypes in the data.

A transcriptome-wide association study provides new insights into the etiology of osteoarthritis.

Background: Osteoarthritis (OA) is a common clinical disease caused by a variety of factors, including genetic variants. Although genome-wide association studies (GWAS) have been performed to elucidate the genetic basis of OA, some loci of risk located in noncoding regions of the genome have been neglected. Therefore, we integrated multiple data types to detect the genetic component of gene expression in OA patients through transcriptome-wide association studies (TWAS) and summary-data-based Mendelian randomization (SMR) analysis. Methods: TWAS was performed by integrating the larger GWAS summary-data for OA (n=30,727 cases, n=297,191 controls) and 2 expression weight sets (muscle-skeletal tissue and whole blood). Colocalization analysis, conditional analysis, and fine-mapping analysis were also conducted. A broad description of the identified associations was obtained. In addition, a causal relationship between certain risk genes and OA was identified with SMR. Results: New significant genome-wide associations were found, including on chromosome 1q36.12 (rs1555024, P=4.24E-07) near the ASAP3 and TCEA3 genes, on chromosome 17q24.2 (rs2521348, P=1.01E-06) near the ABCA9 gene, on chromosome 20q11.22 (rs224331, P=8.17E-09) near the UQCC1 and MYH7B genes, and on chromosome 21q21.3 (rs2832155, P=5.39E-08) near the RWDD2B gene. In addition, SMR results exhibited that upregulated UQCC1 and downregulated ASAP3 were associated with OA development and both had a significant causal relationship with OA. Conclusions: We revealed some novel OA-associated genes and risk loci by integrating multiple data types and analysis methods, thus providing new clues for the study of genetic mechanisms of OA.

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets.

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete ($\gt 95\%$ pure, $\gt 90\%$ complete) and high-quality ($\gt 90\%$ pure, $\gt 70\%$ complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates.

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

Getting Started with LINCS Datasets and Tools.

The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.

Association Mining Identifies MAL2 as a Novel Tumor Suppressor in Colorectal Cancer.

Introduction: Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. However, the driver genes that promote CRC metastasis remain poorly understood. Association mining mines and extracts the repeated correlations and relevance in a dataset to predict the appearance of other data items according to the appearance of one item. Methods: Here, the Apriori algorithm was used to find the frequent mutational gene sets (FMGSs) and hidden association rules (ARs) within these FMGSs from 383 CRCs with whole exome sequencing datasets. The weighted correlation network analysis (WGCNA) was used to identify the hub genes in CRC. CCK8, colony formation, cell migration and invasion assays were adopted to detect the roles of hub genes in CRC. Results: Intriguingly, we found that MAL2 (myelin and lymphocyte protein 2) was associated with TP53 and APC in stage IV of CRC, and further subnetwork exploration based on WGCNA identified MAL2 as a potent hub gene. To validate the metastasis-related role of MAL2 in CRC, a lentivirus-based overexpression system was utilized to construct MAL2-overexpressing human CRC LOVO cells. Overexpression of MAL2 remarkably inhibited CRC cell proliferation and invasion. Conclusion: Our results highlighted that MAL2 acts as a tumor suppressor in CRC and could serve as a potential therapeutic target.

Breast Cancer Subtypes Based on Hypoxia-Related Gene Sets Identify Potential Therapeutic Agents.

Purpose: The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of breast cancer (BC). We aimed to establish a hypoxia-related gene signature to identify a new BC subtype through the clustering analysis and explore potential compounds targeting the BC subtypes. Methods: Gene expression data and clinical features of BC and adjacent non-tumor tissues were downloaded from the Cancer Genome Atlas-Breast cancer (TCGA-BRCA) database. We comprehensively revealed the activity changes of Gene Ontology (GO) biological processes (BP) gene sets in BC by gene set variation analysis (GSVA) and identified three hypoxia-related BC subtypes. We then matched the differentially expressed gene profile of each subtype with the gene profile in CMap database to identify the potential agents targeting the BC subtypes. Results: 562 of Gene Ontology biological processes gene sets significantly correlated with hypoxia score in breast cancer. 969 BC patients were clustered into three subtypes based on the enrichment score of hypoxia-associated gene sets. Subtype 1 patients displayed better survival than subtype 2 and 3. KEGG pathway enrichment analysis of each subtype was performed based on the unique differential expression genes profile. In subtype 1, the upregulated genes were associated with lipid and amino acid metabolism regulation; in subtype 2, the upregulated genes were associated with metabolic energy regulation, while in subtype 3, the upregulated genes were associated with apoptosis and protein process. Using the CMap database, 55, 111 and 63 compounds were identified, targeting subtype 1, 2, and 3, respectively. Conclusion: In this study, novel hypoxia-related subtypes were developed for patients with BC. In addition, biological processes associated with differential expression genes profile and potential therapeutic target compounds were identified in each subtype. The new classification might provide a better understanding of the role of hypoxia in breast cancer and more individualized treatment for patients.

Distinguishable Prognostic Signatures and Tumor Immunogenicity Between Pancreatic Head Cancer and Pancreatic Body/Tail Cancer.

Background: Pancreatic head cancer and pancreatic body/tail cancer are considered to have different clinical presentations and to have altered outcomes. Methods: Ninety cases of pancreatic adenocarcinoma (PDAC) from our institution were used as a discovery set and 166 cases of PDAC from the TCGA cohort were used as a validation set. According to the anatomical location, the cases of PDAC were divided into the pancreatic head cancer group and the pancreatic body/tail cancer group. Firstly, the different biological functions of the two groups were assessed by ssGSEA. Meanwhile, ESTIMATE and CIBERSORT were conducted to estimate immune infiltration. Then, a novel anatomical site-related risk score (SRS) model was constructed by LASSO and Cox regression. Survival and time-dependent ROC analysis was used to prove the predictive ability of our model in two cohorts. Subsequently, an integrated survival decision tree and a scoring nomogram were constructed to improve prognostic stratification and predictive accuracy for individual patients. In addition, gseaGO and gseaKEGG pathway analyses were performed on genes in the key module by the R package. Results: Overall survival and the objective response rate (ORR) of patients with pancreatic body/tail cancer were markedly superior to those with pancreatic head cancer. In addition, distinct immune characteristics and gene patterns were observed between the two groups. Then, we screened 5 biomarkers related to the prognosis of pancreatic cancer and constructed a more powerful novel SRS model to predict prognosis. Conclusions: Our research shed some light on the revelation of gene patterns, immune and mutational landscape characterizations, and their relationships in different PDAC locations.

massiveGST: A Mann-Whitney-Wilcoxon Gene-Set Test Tool That Gives Meaning to Gene-Set Enrichment Analysis.

Gene-set enrichment analysis is the key methodology for obtaining biological information from transcriptomic space's statistical result. Since its introduction, Gene-set Enrichment analysis methods have obtained more reliable results and a wider range of application. Great attention has been devoted to global tests, in contrast to competitive methods that have been largely ignored, although they appear more flexible because they are independent from the source of gene-profiles. We analyzed the properties of the Mann-Whitney-Wilcoxon test, a competitive method, and adapted its interpretation in the context of enrichment analysis by introducing a Normalized Enrichment Score that summarize two interpretations: a probability estimate and a location index. Two implementations are presented and compared with relevant literature methods: an R package and an online web tool. Both allow for obtaining tabular and graphical results with attention to reproducible research.