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PURPOSE: The main function of cartilage oligomeric matrix protein (COMP) is to maintain the synthesis and stability of the extracellular matrix by interacting with collagen. At present, there are relatively few studies on the role of this protein in tumors. This study aimed to explore the relationship between COMP and pan-cancer, and analyzed its diagnostic and prognostic value. METHODS: The Cancer Genome Atlas database, the Genotype-Tissue Expression database and the Cancer Cell Line Encyclopedia database was used for gene expression analysis. The receiver operating characteristic curve was used to assess the diagnostic value of COMP in pan-cancer. Kaplan-Meier plots were used to assess the relationship between COMP expression and prognosis of cancers. R software v4.1.1 was used for statistical analysis, and the ggplot2 package was used for visualization. RESULTS: COMP was significantly overexpressed in 15 human cancers and showed significantly difference in 12 molecular subtypes and 16 immune subtypes. In addition, the expression of COMP is associated with tumor immune evasion. The ROC curve showed that the expression of COMP could predict the occurrence of 16 kinds of tumors with relative accuracy, including adrenocortical carcinoma (ACC) (AUC = 0.737), breast invasive carcinoma (BRCA) (AUC = 0.896), colon adenocarcinoma (COAD) (AUC = 0.760), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD) (AUC = 0.775), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) (AUC = 0.875), kidney renal papillary cell carcinoma (KIRP) (AUC = 0.773), kidney chromophobe (KICH) (AUC = 0.809), ovarian serous cystadenocarcinoma (OV) (AUC = 0.906), prostate adenocarcinoma (PRAD) (AUC = 0.721), pancreatic adenocarcinoma (PAAD) (AUC = 0.944), rectum adenocarcinoma (READ) (AUC = 0.792), skin cutaneous melanoma (SKCM) (AUC = 0.746), stomach adenocarcinoma (STAD) (AUC = 0.711), testicular germ cell tumors (TGCT) (AUC = 0.823), thymoma (THYM) (AUC = 0.777) and uterine carcinosarcoma (UCS) (AUC = 0.769). Furthermore, COMP expression was correlated with overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) in ACC (OS, HR = 4.95, DSS, HR = 5.55, PFI, HR = 2.79), BLCA (OS, HR = 1.59, DSS, HR = 1.72, PFI, HR = 1.36), KIRC (OS, HR = 1.36, DSS, HR = 1.94, PFI, HR = 1.57) and COADREAD (OS, HR = 1.46, DSS, HR = 1.98, PFI, HR = 1.43). We selected previously unreported bladder urothelial carcinoma (BLCA) for further study and found that COMP could be an independent risk factor for OS, DSS and PFI. Moreover, we found differentially expressed genes of COMP in BLCA and obtained top 10 hub genes, including LGR4, LGR5, RSPO2, RSPO1, RSPO3, RNF43, ZNRF3, FYN, LYN and SYK. Finally, we verified the function of COMP at the cellular level by using J82 and T24 cells and found that knockdown of COMP could significantly inhibit migration and invasion. This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis. CONCLUSION: This finding supports that COMP could be a potential biomarker for pan-cancer diagnosis and prognosis encompassing tumor microenvironment, disease stage and prognosis.

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Severe COVID-19 patients present a clinical and laboratory overlap with other hyperinflammatory conditions such as hemophagocytic lymphohistiocytosis (HLH). However, the underlying mechanisms of these conditions remain to be explored. Here, we investigated the transcriptome of 1596 individuals, including patients with COVID-19 in comparison to healthy controls, other acute inflammatory states (HLH, multisystem inflammatory syndrome in children [MIS-C], Kawasaki disease [KD]), and different respiratory infections (seasonal coronavirus, influenza, bacterial pneumonia). We observed that COVID-19 and HLH share immunological pathways (cytokine/chemokine signaling and neutrophil-mediated immune responses), including gene signatures that stratify COVID-19 patients admitted to the intensive care unit (ICU) and COVID-19_nonICU patients. Of note, among the common differentially expressed genes (DEG), there is a cluster of neutrophil-associated genes that reflects a generalized hyperinflammatory state since it is also dysregulated in patients with KD and bacterial pneumonia. These genes are dysregulated at the protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins that point to neutrophil hyperactivation in COVID-19 patients admitted to the intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.

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BACKGROUND: Inhaled corticosteroid (ICS) response among patients with asthma is influenced by genetics, but biologically actionable insights based on associations have not been found. Various glucocorticoid response omics data sets are available to interrogate their biological effects. OBJECTIVE: We sought to identify functionally relevant ICS-response genetic associations by integrating complementary multiomics data sets. METHODS: Variants with P values less than 10-4 from a previous ICS-response genome-wide association study were reranked on the basis of integrative scores determined from (1) glucocorticoid receptor- and (2) RNA polymerase II-binding regions inferred from ChIP-Seq data for 3 airway cell types, (3) glucocorticoid response element motifs, (4) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic data sets, and (5) expression quantitative trait loci from GTEx. Candidate variants were tested for association with ICS response and asthma in 6 independent studies. RESULTS: Four variants had significant (q value < 0.05) multiomics integrative scores. These variants were in a locus consisting of 52 variants in high linkage disequilibrium (r2 ≥ 0.8) near glucocorticoid receptor-binding sites by the gene BIRC3. Variants were also BIRC3 expression quantitative trait loci in lung, and 2 were within/near putative glucocorticoid response element motifs. BIRC3 had increased RNA polymerase II occupancy and gene expression, with glucocorticoid exposure in 2 ChIP-Seq and 13 transcriptomic data sets. Some BIRC3 variants in the 52-variant locus were associated (P < .05) with ICS response in 3 independent studies and others with asthma in 1 study. CONCLUSIONS: BIRC3 should be prioritized for further functional studies of ICS response.

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Penile cancer (PeC) carcinogenesis is not fully understood, and no biomarkers are reported in clinical practice. We aimed to investigate molecular signatures based on miRNA and mRNA and perform an integrative analysis to identify molecular drivers and pathways for PeC development. Affymetrix miRNA microarray was used to identify differentially expressed miRNAs (DEmiRs) comparing 11 tumoral tissues (TT) paired with non-neoplastic tissues (NNT) with further validation in an independent cohort (n = 13). We also investigated the mRNA expression of 83 genes in the total sample. Experimentally validated targets of DEmiRs, miRNA-mRNA networks, and enriched pathways were evaluated in silico. Eight out of 69 DEmiRs identified by microarray analysis were validated by qRT-PCR (miR-145-5p, miR-432-5p, miR-487b-3p, miR-30a-5p, miR-200a-5p, miR-224-5p, miR-31-3p and miR-31-5p). Furthermore, 37 differentially expressed genes (DEGs) were identified when comparing TT and NNT. We identified four downregulated DEmiRs (miR-30a-5p, miR-432-5p, miR-487b-3p, and miR-145-5p) and six upregulated DEGs (IL1A, MCM2, MMP1, MMP12, SFN and VEGFA) as potential biomarkers in PeC by their capacity of discriminating TT and NNT with accuracy. The integration analysis showed eight dysregulated miRNA-mRNA pairs in penile carcinogenesis. Taken together, our findings contribute to a better understanding of the regulatory roles of miRNAs and altered transcripts levels in penile carcinogenesis.

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Oral squamous cell carcinoma (OSCC) has high mortality rates that are largely associated with lymph node metastasis. However, the molecular mechanisms that drive OSCC metastasis are unknown. Extracellular vesicles (EVs) are membrane-bound particles that play a role in intercellular communication and impact cancer development and progression. Thus, profiling EVs would be of great significance to decipher their role in OSCC metastasis. For that purpose, we used a reductionist approach to map the proteomic, miRNA, metabolomic, and lipidomic profiles of EVs derived from human primary tumor (SCC-9) cells and matched lymph node metastatic (LN1) cells. Distinct omics profiles were associated with the metastatic phenotype, including 670 proteins, 217 miRNAs, 26 metabolites, and 63 lipids differentially abundant between LN1 cell- and SCC-9 cell-derived EVs. A multi-omics integration identified 11 'hub proteins' significantly decreased at the metastatic site compared with primary tumor-derived EVs. We confirmed the validity of these findings with analysis of data from multiple public databases and found that low abundance of seven 'hub proteins' in EVs from metastatic lymph nodes (ALDH7A1, CAD, CANT1, GOT1, MTHFD1, PYGB, and SARS) is correlated with reduced survival and tumor aggressiveness in patients with cancer. In summary, this multi-omics approach identified proteins transported by EVs that are associated with metastasis and which may potentially serve as prognostic markers in OSCC.

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Breast cancer is the neoplasm with the highest number of deaths in women. Although the molecular mechanisms associated with the development of this tumor have been widely described, metastatic disease has a high mortality rate. In recent years, several studies show that microRNAs or miRNAs regulate complex processes in different biological systems including cancer. In the present work, we describe a group of 61 miRNAs consistently over-expressed in breast cancer (BC) samples that regulate the breast cancer transcriptome. By means of data mining from TCGA, miRNA and mRNA sequencing data corresponding to 1091 BC patients and 110 normal adjacent tissues were downloaded and a miRNA-mRNA network was inferred. Calculations of their oncogenic activity demonstrated that they were involved in the regulation of classical cancer pathways such as cell cycle, PI3K-AKT, DNA repair, and k-Ras signaling. Using univariate and multivariate analysis, we found that five of these miRNAs could be used as biomarkers for the prognosis of overall survival. Furthermore, we confirmed the over-expression of two of them in 56 locally advanced BC samples obtained from the histopathological archive of the National Cancer Institute of Mexico, showing concordance with our previous bioinformatic analysis.

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Members of the GATA family of transcription factors play key roles in the differentiation of specific cell lineages by regulating the expression of target genes. Three GATA factors play distinct roles in hematopoietic differentiation. In order to better understand how these GATA factors function to regulate genes throughout the genome, we are studying the epigenomic and transcriptional landscapes of hematopoietic cells in a model-driven, integrative fashion. We have formed the collaborative multi-lab VISION project to conduct ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis. The epigenomic data included nuclease accessibility in chromatin, CTCF occupancy, and histone H3 modifications for 20 cell types covering hematopoietic stem cells, multilineage progenitor cells, and mature cells across the blood cell lineages of mouse. The analysis used the Integrative and Discriminative Epigenome Annotation System (IDEAS), which learns all common combinations of features (epigenetic states) simultaneously in two dimensions-along chromosomes and across cell types. The result is a segmentation that effectively paints the regulatory landscape in readily interpretable views, revealing constitutively active or silent loci as well as the loci specifically induced or repressed in each stage and lineage. Nuclease accessible DNA segments in active chromatin states were designated candidate cis-regulatory elements in each cell type, providing one of the most comprehensive registries of candidate hematopoietic regulatory elements to date. Applications of VISION resources are illustrated for the regulation of genes encoding GATA1, GATA2, GATA3, and Ikaros. VISION resources are freely available from our website http://usevision.org.

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In 2014, a DNA-based phylogenetic study confirming the paraphyly of the grass subtribe Sporobolinae proposed the creation of a large monophyletic genus Sporobolus, including (among others) species previously included in the genera Spartina, Calamovilfa, and Sporobolus. Spartina species have contributed substantially (and continue contributing) to our knowledge in multiple disciplines, including ecology, evolutionary biology, molecular biology, biogeography, experimental ecology, biological invasions, environmental management, restoration ecology, history, economics, and sociology. There is no rationale so compelling to subsume the name Spartina as a subgenus that could rival the striking, global iconic history and use of the name Spartina for over 200 yr. We do not agree with the subjective arguments underlying the proposal to change Spartina to Sporobolus. We understand the importance of both the objective phylogenetic insights and of the subjective formalized nomenclature and hope that by opening this debate we will encourage positive feedback that will strengthen taxonomic decisions with an interdisciplinary perspective. We consider that the strongly distinct, monophyletic clade Spartina should simply and efficiently be treated as the genus Spartina.

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OBJECTIVES: The current treatment of laryngeal squamous cell carcinoma (LSCC) is based on radical surgery and radiotherapy resulting in high morbidity. Chemoradiotherapy has been used as alternative to organ sparing; however, several advanced cases presented resistance to treatment, which contributes to a high risk of recurrence and mortality. Coding RNAs and miRNAs have potential to be used as biomarkers or targets for cancer therapy. MATERIALS AND METHODS: In this study, 36 LSCC and 5 non-neoplastic control samples were investigated using miRNA and mRNA large-scale expression analysis and a cross-validation was performed using the TCGA database (116 LSCC and 12 surrounding normal tissues). RESULTS: The large-scale profiling revealed the involvement of 28 miRNAs and 817 genes differentially expressed in LSCC. An integrative analysis comprising predicted and experimentally validated miRNA/mRNA interactions (negatively correlated), resulted in 28 miRNAs and 543 mRNAs. Decreased expression of miR-199b was significantly associated with shorter disease-free survival in LSCC (internal and TCGA datasets). The expression levels of selected miRNAs (miR-199b-5p, miR-29c-3p, miR-204-5p, miR-125b-5p and miR-92a-3p) and genes (COL3A1, COL10A1, ERBB4, HMGA2, HLF, TOP2A, MMP3, MMP13, MMP10 and PPP1R3) were confirmed as altered in LSCC by RT-qPCR. Additionally, a drug target prediction analysis revealed drug combinations based on miRNA and mRNA expression, pointing out novel alternatives to optimize the LSCC treatment. CONCLUSION: Collectively, these findings provide new insights in the LSCC transcriptional deregulation and potential drug targets.

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INTRODUCTION: Liver regeneration is a normal response to liver injury. The aim of this study was to determine the molecular basis of liver regeneration, through an integrative analysis of high-throughput gene expression datasets. METHODS: We identified and curated datasets pertaining to liver regeneration from the Gene Expression Omnibus, where regenerating liver tissue was compared to healthy liver samples. The key dysregulated genes and pathways were identified using Ingenuity Pathway Analysis software. There were three eligible datasets in total. RESULTS: In the early phase after hepatectomy, inflammatory pathways such as Nrf2 oxidative stress-mediated response and cytokine signaling were significantly upregulated. At peak regeneration, we discovered that cell cycle genes were predominantly expressed to promote cell proliferation. Using the Betweenness centrality algorithm, we discovered that Jun is the key central gene in liver regeneration. Calcineurin inhibitors may inhibit liver regeneration, based on predictive modeling. CONCLUSION: There is a paucity of human literature in defining the molecular mechanisms of liver regeneration along a time continuum. Nonetheless, using an integrative computational analysis approach to the available high-throughput data, we determine that the oxidative stress response and cytokine signaling are key early after hepatectomy, whereas cell cycle control is important at peak regeneration. The transcription factor Jun is central to liver regeneration and a potential therapeutic target. Future studies of regeneration in humans along a time continuum are needed to better define the underlying mechanisms, and ultimately enhance care of patients with acute and chronic liver failure while awaiting transplant.

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Protein complexes play a significant role in the core functionality of cells. These complexes are typically identified by detecting densely connected subgraphs in protein-protein interaction (PPI) networks. Recently, multiple large-scale mass spectrometry-based experiments have significantly increased the availability of PPI data in order to further expand the set of known complexes. However, high-throughput experimental data generally are incomplete, show limited agreement between experiments, and show frequent false positive interactions. There is a need for computational approaches that can address these limitations in order to improve the coverage and accuracy of human protein complexes. Here, we present a new method that integrates data from multiple heterogeneous experiments and sources in order to increase the reliability and coverage of predicted protein complexes. We first fused the heterogeneous data into a feature matrix and trained classifiers to score pairwise protein interactions. We next used graph based methods to combine pairwise interactions into predicted protein complexes. Our approach improves the accuracy and coverage of protein pairwise interactions, accurately identifies known complexes, and suggests both novel additions to known complexes and entirely new complexes. Our results suggest that integration of heterogeneous experimental data helps improve the reliability and coverage of diverse high-throughput mass-spectrometry experiments, leading to an improved global map of human protein complexes.

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BACKGROUND: Papillary thyroid carcinoma (PTC) is a common endocrine neoplasm with a recent increase in incidence in many countries. Although PTC has been explored by gene expression and DNA methylation studies, the regulatory mechanisms of the methylation on the gene expression was poorly clarified. In this study, DNA methylation profile (Illumina HumanMethylation 450K) of 41 PTC paired with non-neoplastic adjacent tissues (NT) was carried out to identify and contribute to the elucidation of the role of novel genic and intergenic regions beyond those described in the promoter and CpG islands (CGI). An integrative and cross-validation analysis were performed aiming to identify molecular drivers and pathways that are PTC-related. RESULTS: The comparisons between PTC and NT revealed 4995 methylated probes (88% hypomethylated in PTC) and 1446 differentially expressed transcripts cross-validated by the The Cancer Genome Atlas data. The majority of these probes was found in non-promoters regions, distant from CGI and enriched by enhancers. The integrative analysis between gene expression and DNA methylation revealed 185 and 38 genes (mainly in the promoter and body regions, respectively) with negative and positive correlation, respectively. Genes showing negative correlation underlined FGF and retinoic acid signaling as critical canonical pathways disrupted by DNA methylation in PTC. BRAF mutation was detected in 68% (28 of 41) of the tumors, which presented a higher level of demethylation (95% hypomethylated probes) compared with BRAF wild-type tumors. A similar integrative analysis uncovered 40 of 254 differentially expressed genes, which are potentially regulated by DNA methylation in BRAFV600E-positive tumors. The methylation and expression pattern of six selected genes (ERBB3, FGF1, FGFR2, GABRB2, HMGA2, and RDH5) were confirmed as altered by pyrosequencing and RT-qPCR. CONCLUSIONS: DNA methylation loss in non-promoter, poor CGI and enhancer-enriched regions was a significant event in PTC, especially in tumors harboring BRAFV600E. In addition to the promoter region, gene body and 3'UTR methylation have also the potential to influence the gene expression levels (both, repressing and inducing). The integrative analysis revealed genes potentially regulated by DNA methylation pointing out potential drivers and biomarkers related to PTC development.

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Penile carcinoma (PeCa) is an important public health issue in poor and developing countries, and has only recently been explored in terms of genetic and epigenetic studies. Integrative data analysis is a powerful method for the identification of molecular drivers involved in cancer development and progression. miRNA and mRNA expression profiles followed by integrative analysis were investigated in 23 PeCa and 12 non-neoplastic penile tissues (NPT). Expression levels of eight miRNAs and 10 mRNAs were evaluated in the same set of samples used for microarray and in a validation set of cases (PeCa = 36; NPT = 27). Eighty-one miRNAs and 2,697 mRNAs were identified as differentially expressed in PeCa. Integrative data analysis revealed 255 mRNAs potentially regulated by 68 miRNAs. Using RT-qPCR, eight miRNAs and nine transcripts were confirmed as altered in PeCa. We identified that MMP1, MMP12 and PPARG and hsa-miR-31-5p, hsa-miR-224-5p, and hsa-miR-223-3p were able to distinguish tumors from NPT with high sensitivity and specificity. Higher MMP1 expression was detected as a better predictor of lymph node metastasis than the clinical-pathological data. In addition, PPARG and EGFR were highlighted as potential pathways for targeted therapy in PeCa. The analysis based on HPV positivity (7 of 23 cases) revealed five miRNA and 13 mRNA differentially expressed. Although in a limited number of cases, HPV positive PeCa presented less aggressive phenotype in comparison with negative cases. Overall, an integrative analysis using mRNA and miRNA profiles revealed markers related with tumor development and progression. Furthermore, MMP1 expression level was a predictive marker for lymph node metastasis in patients with PeCa.

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Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

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