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Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions. 

Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers. 

Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in co-expression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNA-seq gene expression data of three cancers - Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM) - from The Cancer Genome Atlas (TCGA) are used to construct gene co-expression networks using Pearson Correlation. Six well-known community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic co-variates. The communities that could distinguish the cancer patients between high- and low-risk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered. 

Long noncoding RNA (lncRNA) plays key roles in tumorigenesis. Misexpression of lncRNA can lead to changes in expression profiles of various target genes, which are involved in cancer initiation and progression. So, identifying key lncRNAs for a cancer would help develop the cancer therapy. Usually, to identify key lncRNAs for a cancer, expression profiles of lncRNAs for normal and cancer samples are required. But, this kind of data are not available for all cancers. In the present study, a computational framework is developed to identify cancer specific key lncRNAs using the lncRNA expression of cancer patients only. The framework consists of two state-of-the-art feature selection techniques - Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO); and five machine learning models - Naive Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine, and Deep Neural Network. For experiment, expression values of lncRNAs for 8 cancers - BLCA, CESC, COAD, HNSC, KIRP, LGG, LIHC, and LUAD - from TCGA are used. The combined dataset consists of 3,656 patients with expression values of 12,309 lncRNAs. Important features or key lncRNAs are identified by using feature selection algorithms RFE and LASSO. Capability of these key lncRNAs in classifying 8 different cancers is checked by the performance of five classification models. This study identified 37 key lncRNAs that can classify 8 different cancer types with an accuracy ranging from 94% to 97%. Finally, survival analysis supports that the discovered key lncRNAs are capable of differentiating between high-risk and low-risk patients. 

Breast cancer is highly sporadic and heterogeneous in nature. Even the patients with same clinical stage do not cluster together in terms of genomic profiles such as mRNA expression. In order to prevent and cure breast cancer completely, it is essential to decipher the detailed heterogeneity of breast cancer at genomic level. Putting the cancer patients on a time scale, which represents the trajectory of cancer development, may help discover the detailed heterogeneity. This in turn would help establish the mechanisms for prevention and complete cure of breast cancer. The goal of this study is to discover the heterogeneity of breast cancer by ordering the cancer patients using pseudotime. This is achieved through two objectives: First, a computational framework is developed to place the cancer patients on a time scale, meaning construct a trajectory of cancer development, by inferring pseudotime from static mRNA expression data; Second, discovering breast cancer heterogeneity at different time periods of the trajectory using statistical and machine learning techniques. In this study, the trajectory of breast cancer progression was constructed using static mRNA expression profiles of 1072 breast cancer patients by inferring pseudotime. Three sets of key genes discovered using supervised machine learning techniques are used to develop the trajectories. The first set of genes are PAM50 genes which is available in literature. The second and third sets of genes were discovered in the present study using the clinical stages of breast cancer (Stage-I, Stage-II, Stage-III, and Stage-IV). The proposed computational framework has the capability of deciphering heterogeneity in breast cancer at a granular level. The results also show the existence of multiple parallel trajectories at different time periods of cancer development or progression.