Comprehensive analysis of differentially expressed miRNAs in hepatocellular carcinoma: Prognostic, predictive significance and pathway insights.

Robust prognostic and predictive factors for hepatocellular carcinoma, a leading cause of cancer-related deaths worldwide, have not yet been identified. Previous studies have identified potential HCC determinants such as genetic mutations, epigenetic alterations, and pathway dysregulation. However, the clinical significance of these molecular alterations remains elusive. MicroRNAs are major regulators of protein expression. MiRNA functions are frequently altered in cancer. In this study, we aimed to explore the prognostic value of differentially expressed miRNAs in HCC, to elucidate their associated pathways and their impact on treatment response. To this aim, bioinformatics techniques and clinical dataset analyses were employed to identify differentially expressed miRNAs in HCC compared to normal hepatic tissue. We validated known associations and identified a novel miRNA signature with potential prognostic significance. Our comprehensive analysis identified new miRNA-targeted pathways and showed that some of these protein coding genes predict HCC patients' response to the tyrosine kinase inhibitor sorafenib.

In Silico Transcriptomic Expression of MSR1 in Solid Tumors Is Associated with Responses to Anti-PD1 and Anti-CTLA4 Therapies.

Immuno-oncology has gained momentum with the approval of antibodies with clinical activities in different indications. Unfortunately, for anti-PD (L)1 agents in monotherapy, only half of the treated population achieves a clinical response. For other agents, such as anti-CTLA4 antibodies, no biomarkers exist, and tolerability can limit administration. In this study, using publicly available genomic datasets, we evaluated the expression of the macrophage scavenger receptor-A (SR-A) (MSR1) and its association with a response to check-point inhibitors (CPI). MSR1 was associated with the presence of macrophages, dendritic cells (DCs) and neutrophils in most of the studied indications. The presence of MSR1 was associated with macrophages with a pro-tumoral phenotype and correlated with TIM3 expression. MSR1 predicted favorable overall survival in patients treated with anti-PD1 (HR: 0.56, FDR: 1%, p = 2.6 × 10-5), anti PD-L1 (HR: 0.66, FDR: 20%, p = 0.00098) and anti-CTLA4 (HR: 0.37, FDR: 1%, p = 4.8 × 10-5). When specifically studying skin cutaneous melanoma (SKCM), we observed similar effects for anti-PD1 (HR: 0.65, FDR: 50%, p = 0.0072) and anti-CTLA4 (HR: 0.35, FDR: 1%, p = 4.1 × 10-5). In a different dataset of SKCM patients, the expression of MSR1 predicted a clinical response to anti-CTLA4 (AUC: 0.61, p = 2.9 × 10-2). Here, we describe the expression of MSR1 in some solid tumors and its association with innate cells and M2 phenotype macrophages. Of note, the presence of MSR1 predicted a response to CPI and, particularly, anti-CTLA4 therapies in different cohorts of patients. Future studies should prospectively explore the association of MSR1 expression and the response to anti-CTLA4 strategies in solid tumors.

CovidOutcome2: a tool for SARS-CoV2 mutation identification and for disease severity prediction

Our goal was to develop a platform, CovidOutcome2, capable of predicting disease severity from viral mutation profiles using automated machine learning (autoML) and deep neural networks applied to the available large corpus of sequenced SARS-CoV2 genomes. CovidOutcome2 accepts either user-submitted genomes or user defined mutation combinations as the input. The output is a predicted severity score plus a list of identified, annotated mutations and their functional effects in VCF format. The best model performance is a ROC-AUC 0.899 for the model including patient age and ROC-AUC 0.83 for the model without patient age. AvailabilityCovidOutcome is freely available online under the URL https://www.covidoutcome.bio-ml.com as well as in a standalone version https://github.com/bio-apps/covid-outcome.

COVIDOUTCOME - Estimating COVID Severity Based on Mutation Signatures in the SARS-CoV-2 Genome

IntroductionNumerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. MethodsWe used an automated machine learning approach where 1,594 viral genomes with available clinical follow-up data were used as the training set (797 "severe" and 797 "mild"). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV), then adjusted for multiple testing with Bootstrap Bias Corrected CV. ResultsWe identified 26 protein and UTR mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patients age as the input and shows high classification efficiency with an AUC of 0.94 (CI: [0.912, 0.962]) and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) which is capable to use a viral sequence and the patients age as the input and provides a percentage estimation of disease severity. DiscussionWe demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes. KEY MESSAGESO_LIA statistical link between SARS-Cov-2 mutation status and severe COVID outcome was established using automated machine learning techniques based on random forest and logistic regression combined with feature selection algorithms. C_LIO_LIA mutation signature based on 3,779 protein coding and 36 UTR mutations capable to identify severe outcome cases was established. C_LIO_LIThe trained model showed high classification performance (AUC=0.94 (CI: [0.912, 0.962]), accuracy=0.87 (CI: [0.830, 0.903])). C_LIO_LIA registration-free web-server for automated classification of new samples was set up and is accessible at http://www.covidoutcome.com. C_LIO_LIThe established pipeline provides a quick assessment of future patients warranting a prospective clinical validation. C_LI

Different mutations in SARS-CoV-2 associate with severe and mild outcome

IntroductionGenomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance. Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome. MethodsMutations in viral sequences from the GISAID virus repository were evaluated by using "hCoV-19/Wuhan/WIV04/2019" as the reference. Patient outcomes were classified as mild disease, hospitalization and severe disease (death or documented treatment in an intensive-care unit). Chi-square test was applied to examine the association between each mutation and patient outcome. False discovery rate was computed to correct for multiple hypothesis testing and results passing a FDR cutoff of 5% were accepted as significant. ResultMutations were mapped to amino acid changes for 2,120 non-silent mutations. Mutations correlated to mild outcome were located in the ORF8, NSP6, ORF3a, NSP4, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface (S) glycoprotein, in the RNA dependent RNA polymerase, in the 3-to5 exonuclease, in ORF3a, NSP2 and N. Mutations leading to severe outcome with low prevalence were found in the surface (S) glycoprotein and in NSP7. Five out of 17 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome. ConclusionsWe demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome. Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.

Influence of tamoxifen on cisplatin-sensitivity and estrogen receptors expression in ovarian carcinoma cell lines.

BACKGROUND: Tamoxifen, used in breast cancer treatment, competitively inhibits estrogen receptor (ER) and also demonstrates direct antiproliferative effect on cancer cells even in ER lacking cancer tissue. However its molecular mechanism of action is still unclear MATERIAL AND METHODS: We exposed on tamoxifen 11 ovarian cancer cell lines, including well-documented platinum-sensitive and platinum-resistant ones, and studied tamoxifen-, cisplatin-sensitivity and expression of ERalpha and beta. RESULTS: We observed: no correlation between TAM-sensitivity and ERalpha and ERbeta expressions, no correlation between TAM influence on cisplatin-sensitivity and ERalpha and ERbeta expressions, increase of ERbeta expression after TAM-exposure in 3 cell lines; decrease in the 1 line, no TAM-exposure influence on ERalpha expression and increase of 1050 for cisplatin after TAM-exposure in 5 (45%) cell lines. These results show ovarian cancer cells being affected by TAM have different platinum sensitivity CONCLUSIONS: Our data suggests that ovarian cancer cells platinum-sensitivity are not linked with ER expressions. We claim the necessity of seeking some TAM predicting factors, using DNA microarrays.