Severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) is the causative virus of the pandemic coronavirus disease 2019 (COVID-19). Evaluating the immunological factors and other implicated processes underlying the progression of COVID-19 is essential for the recognition and then the design of efficacious therapies. Therefore, we analyzed RNAseq data obtained from PBMCs of the COVID-19 patients to explore coding and non-coding RNA diagnostic immunological panels. For this purpose, we integrated multiple RNAseq data and analyzed them overall as well as by considering the state of disease including severe and non-severe conditions. Afterward, we utilized a co-expressed-based machine learning procedure comprising weighted-gene co-expression analysis and differential expression gene as filter phase and recursive feature elimination-support vector machine as wrapper phase. This procedure led to the identification of two modules containing 5 and 84 genes which are mostly involved in cell dysregulation and innate immune suppression, respectively. Moreover, the role of vitamin D in regulating some classifiers was highlighted. Further analysis disclosed the role of discriminant miRNAs including miR-197-3p, miR-150-5p, miR-340-5p, miR-122-5p, miR-1307-3p, miR-34a-5p, miR-98-5p and their target genes comprising GAN, VWC2, TNFRSF6B, and CHST3 in the metabolic pathways. These classifiers differentiate the final fate of infection toward severe or non-severe COVID-19. The identified classifier genes and miRNAs may help in the proper design of therapeutic procedures considering their involvement in the immune and metabolic pathways.

Background Adult T-cell Leukemia/Lymphoma (ATLL) is a cancer disease that is developed due to the infection by human T-cell leukemia virus type 1. It can be classified into four main subtypes including, acute, chronic, smoldering, and lymphoma. Despite the clinical manifestations, there are no reliable diagnostic biomarkers for the classification of these subtypes. Methods Herein, we employed a machine learning approach, namely, Support Vector Machine-Recursive Feature Elimination with Cross-Validation (SVM-RFECV) to classify the different ATLL subtypes from Asymptomatic Carriers (ACs). The expression values of multiple mRNAs and miRNAs were used as the features. Afterward, the reliable miRNA-mRNA interactions for each subtype were identified through exploring the experimentally validated-target genes of miRNAs. Results The results revealed that miR-21 and its interactions with DAAM1 and E2F2 in acute, SMAD7 in chronic, MYEF2 and PARP1 in smoldering subtypes could significantly classify the diverse subtypes. Conclusions Considering the high accuracy of the constructed model, the identified mRNAs and miRNA are proposed as the potential therapeutic targets and the prognostic biomarkers for various ATLL subtypes.

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Background Adult T-cell Leukemia/Lymphoma (ATLL) is a rapidly progressing type of T-cell non-Hodgkin lymphoma that is developed after the infection by human T-cell leukemia virus type 1 (HTLV-1). It could be categorized into four major subtypes, acute, lymphoma, chronic, and smoldering. These different subtypes have some shared clinical manifestations, and there are no trustworthy biomarkers for diagnosis of them. Methods We applied weighted-gene co-expression network analysis to find the potential gene and miRNA biomarkers for various ATLL subtypes. Afterward, we found reliable miRNA-gene interactions by identifying the experimentally validated-target genes of miRNAs. Results The outcomes disclosed the interactions of miR-29b-2-5p and miR-342-3p with LSAMP in ATLL_acute, miR-575 with UBN2, miR-342-3p with ZNF280B, and miR-342-5p with FOXRED2 in ATLL_chronic, miR-940 and miR-423-3p with C6orf141, miR-940 and miR-1225-3p with CDCP1, and miR-324-3p with COL14A1 in ATLL_smoldering. These miRNA-gene interactions determine the molecular factors involved in the pathogenesis of each ATLL subtype and the unique ones could be considered biomarkers. Conclusion The above-mentioned miRNAs-genes interactions are suggested as diagnostic biomarkers for different ATLL subtypes.

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