Current Bioinformatics - Volume 17, Issue 10, 2022

Background: Technical advances and cost reduction have allowed for the worldwide popularity of array platforms. Otherwise called “molecular karyotyping”, it yields a large amount of CNV data, which is useless without interpretation. Objective: This study aims to review existing CNV interpretation software and algorithms to reveal their possibilities and limitations. Results: Open and user-friendly CNV interpretation software is limited to several options, which mostly do not allow for cross-interpretation. Many algorithms are generally based on the Database of Genomic Variants, CNV size, inheritance data, and disease databases, which currently seem insufficient. Conclusion: The analysis of CNV interpretation software and algorithms resulted in a conclusion that it is necessary to expand the existing algorithms of CNV interpretation and at least include pathway and expression data. A user-friendly freely available CNV interpretation software, based on the expanded algorithms, is yet to be created.

Single-cell RNA-sequencing (scRNA-seq) is a rapidly growing field in transcriptomics, which generates a tremendous amount of gene expression data at the single-cell level. Improved statistical approaches and tools are required to extract informative knowledge from such data. Gene network modeling and analysis is one such approach for downstream analysis of scRNA-seq data. Therefore, newer and innovative methods have been introduced in the literature. These approaches greatly vary in their utility, basic statistical concepts, models fitted to the data, etc. Therefore, we present a comprehensive overview of the available approaches for gene network modeling and analysis in single-cell studies, along with their limitations. We also classify the approaches based on different statistical principles and present a class-wise review. We discuss the limitations that are specific to each class of approaches and how they are addressed by subsequent classes of methods. We identify several biological and methodological challenges that must be addressed to enable the development of novel and innovative single-cell gene network inference approaches and tools. These new approaches will be able to analyze the singlecell data efficiently and accurately to better understand the biological systems, increasing the specificity, sensitivity, utility, and relevance of single-cell studies. Furthermore, this review will serve as a catalog and provide guidelines to genome researchers and experimental biologists for objectively choosing the better gene network modeling approach.

Background: Nowadays, non-small cell lung cancer (NSCLC) is a common and highly fatal malignancy worldwide. Therefore, identifying the potential prognostic markers and therapeutic targets is urgent for patients. Objective: This study aimed at finding hub targets associated with NSCLC using multiple databases. Methods: Differentially expressed genes (DEGs) from Genome Expression Omnibus (GEO) cohorts were employed for the enrichment analyses of Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. Candidate key genes, filtered from the topological parameter 'Degree' and validated using the Cancer Genome Atlas (TCGA) cohort, were analyzed for their association with clinicopathological features and prognosis of NSCLC. Meanwhile, immunohistochemical cohort analyses and biological verification were further evaluated. Results: A total of 146 DEGs were identified following data preprocessing, and a protein-protein interaction (PPI) systematic network was constructed based on them. The top ten candidate core genes were further extracted from the above PPI network by using 'Degree' value, among which COL1A1 was shown to associate with overall survival (OS) of NSCLC as determined by using the Kaplan-Meier analysis (p=0.028), and could serve as an independent prognostic factor for OS in NSCLC patients (HR, 0.814; 95% CI, 0.665-0.996; p=0.046). We then analyzed the clinical stages, PPI, mutations, potential biological functions, and immune regulations of COL1A1 in NSCLC patients using multiple bioinformatics tools, including GEPIA, GeneMANIA, cBioPortal, GESA, and TISIDB. Finally, we further experimentally validated the overexpression of COL1A1 in NSCLC samples and found that inhibition of COL1A1 expression moderately sensitized NSCLC cells to cisplatin. Conclusion: Thus, our results showed that COL1A1 may serve as a potential prognostic marker and therapeutic target in NSCLC.

Background: Molecular alterations have been recognized as valuable diagnostic biomarkers for papillary thyroid carcinoma (PTC). Objectives: This study aimed to identify immune-related gene signatures associated with PTC progression using a computational pipeline and to develop an expression-based panel for rapid PTC risk classification. Methods: RNA-seq data and clinical information for PTC samples were downloaded from The Cancer Genome Atlas, followed by an analysis of differentially expressed (DE) RNAs among high-risk PTC, low-risk PTC, and normal groups. Immune cell infiltration and protein–protein interaction analyses were performed to obtain DE RNAs related to immunity. Then, a competing endogenous RNA (ceRNA) network was constructed to identify hub genes for the construction of a diagnostic model, which was evaluated by a receiver operator characteristic curve. A manually curated independent sample cohort was constructed to validate the model. Results: By analyzing the immune cell infiltration, we found that the infiltration of plasma cells and CD8+ T cells was more abundant in the high-risk groups, and 68 DE mRNAs were found to be significantly correlated with these immune cells. Then a ceRNA network containing 10 immune-related genes was established. The ten-gene panel (including DEPDC1B, ELF3, VWA1, CXCL12, SLC16A2, C1QC, IPCEF1, ITM2A, UST, and ST6GAL1) was used to construct a diagnostic model with specificity (66.3%), sensitivity (83.3%), and area under the curve (0.762) for PTC classification. DEPDC1B and SLC16A2 were experimentally validated to be differentially expressed between high-risk and low-risk patients. Conclusion: The 10 immune-related gene panels can be used to evaluate the risk of PTC during pointof- care testing with high specificity and sensitivity.

Background: Shizi Sanhua Decoction, a traditional Chinese medicine (TCM) prescription, shows a treatment advantage on male oligospermia. While due to the complexity of the compatibility (multiple herbs composition), the underlying mechanism remains to be deciphered. Methods: Herbs-ingredients-target genes were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SWISSADME database, intersecting with oligospermia-related derived from DisGeNET to obtain co-target genes. The protein-protein interaction (PPI) network of co-target genes was constructed based on the STRING database, and highly condensed sub-networks and top 10 Hub genes were identified with the CytoHubba plug-in. Herbs, ingredients and KEGG enrichment information were projected onto the identified highly condensed subnetwork to build Herbs-ingredients and disease co-target genes sub-network. Results: After integration of herbs-ingredients-target genes (n=453) with disease genes (n=329), 29 cotarget genes were obtained. Among them, PARP1, AR, CYP17A1, ESR1, ABCB1, STS, CFTR, SOAT1, NR5A1, and HIF1A were related to male infertility (WP4673-WikiPathways). Sub-network analysis further revealed the top 10 Hub genes, and the relation with the herbs and ingredients was demonstrated in the sub-network of herbs-ingredients and disease co-target genes. As expected, reproductive- related biological processes (mammary gland epithelium development, GO:0061180; Oocyte meiosis, hsa04114; Progesterone-mediated oocyte maturation, hsa04914) were enriched. Thyroid hormone signaling pathway (hsa04919), Serotonergic synapse (hsa04726), Chemical carcinogenesisreactive oxygen species (hsa05208), and Endocrine resistance (hsa01522) may contribute to the development of male oligospermia. Conclusion: Constructed herbs-ingredients and disease co-target genes sub-network discloses specific bioprocesses and molecular targets of Shizi Sanhua Decoction in oligospermia treatment.

Background: There are correlations between the multiple types of data stored in the tensor space. The matrix formed by the data in the high-dimensional space is of low rank. Therefore, the potential association between genes and cancers can be explored in low-rank space. Tensor robust principal component analysis (TRPCA) is used to extract information by obtaining coefficient tensors with low-rank representation. In practical applications, global features and sparse structure are ignored, which leads to incomplete analysis. Objective: This paper proposes an adaptive reweighted TRPCA method (ARTRPCA) to explore cancer subtypes and identify conjoint abnormally expressed genes (CAEGs). Methods: ARTRCA analyzes data based on adaptive learning of primary information. Meanwhile, the weighting scheme based on singular value updates is used to learn global features in low-rank space. The reweighted I1 algorithm is based on prior knowledge, which is used to learn about sparse structures. Moreover, the sparsity threshold of Gaussian entries has been increased to reduce the influence of outliers. Results: In the experiment of sample clustering, ARTRPCA has obtained promising experimental results. The identified CAEGs are pathogenic genes of various cancers or are highly expressed in specific cancers. Conclusion: The ATRPCA method has shown excellent application prospects in cancer multiomics data.