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Objective: The prevalence of neurological disorders often varies by sex, with conditions such as Alzheimer’s disease and autism spectrum disorder (ASD) demonstrating notable differences in incidence. The aim of this study is to understand the molecular basis for these divergences in order to facilitate the creation of sex-specific therapeutic strategies. Materials and Methods: This study is a bioinformatic analysis of publicly available RNA sequencing datasets involving autism patients. The study utilized RNA sequencing data from postmortem human brains’ prefrontal cortex, including 38 neurotypical controls and 34 individuals with ASD. The sequencing data was obtained from previously published papers, and we downloaded the raw data from SRA. We investigated the molecular basis of sex-biased presentation in ASD through comprehensive transcriptomic analysis. Results: Comparative analysis of gene expression between male and female subjects, both autistic and unaffected, was conducted, using a significance level of ≤0.01. In autistic individuals, 136 genes demonstrated differential expression between sexes, predominantly upregulated in males, indicating a bias in male gene expression. Among these, 12 genes were identified as risk factors in the SFARI dataset. While most sex-biased genes were autosomal, expression differences on sex chromosomes were also observed in neurotypical subjects. Notable genes included TCF7L2, collagen family genes, and solute carrier family genes. In ASD males, extracellular matrix (ECM) organization emerged as a significant pathway, while immune-related processes were prominent in unaffected individuals. Conclusion: Our study highlights the impact of the ECM pathway in ASD, with notable differences between sexes, particularly in males. MIR424 shows promise as a potential biomarker for ASD in males. Recognizing the importance of sex differences in ASD transcriptomic research is crucial, as these variances provide insights into the disorder’s pathophysiology and may guide the development of more personalized treatments for both sexes. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to detect gene signatures in RNA-sequencing (RNA-seq) data using Pareto-optimal cluster size identification. Background: RNA-seq has emerged as an important technology for transcriptome profiling in recent years. Gene expression signatures involving tens of genes have been proven to be predictive of disease type and patient response to treatment. Methods: Data related to the liver cancer RNA-seq dataset, which included 35 paired hepatocellular carcinoma (HCC) and non-tumor tissue samples, was used in this study. The differentially expressed genes (DEGs) were identified after performing pre-filtering and normalization. After that, a multi-objective optimization technique, namely multi-objective optimization for collecting cluster alternatives (MOCCA), was used to discover the Pareto-optimal cluster size for these DEGs. Then, the k-means clustering method was performed on the RNA-seq data. The best cluster, as a signature for the disease, was found by calculating the average Spearman's correlation score of all genes in the module in a pair-wise manner. All analyses were performed in the R 4.1.1 package in virtual space with 100 Gb of RAM memory. Results: Using MOCCA, eight Pareto-optimal clusters were obtained. Ultimately, two clusters with the greatest average Spearman's correlation coefficient scores were chosen as gene signatures. Eleven prognostic genes involved in HCC's abnormal metabolism were identified. In addition, three differentially expressed pathways were identified between tumor and non-tumor tissues. Conclusion: These identified metabolic prognostic genes help us to provide more powerful prognostic information and enhance survival prediction for HCC patients. In addition, Pareto-optimal cluster size identification is suggested for gene signature in other RNA-Seq data. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) causes serious disease in humans. First identified in November/December 2019 in China, it has rapidly spread worldwide. We analyzed 2790 SARS‐CoV‐2 genome sequences from 56 countries that were available on April 2, 2020, to assess the evolution of the virus during this early phase of its expansion. We aimed to assess sequence variations that had evolved in virus genomes, giving the greatest attention to the S gene. We also aimed to identify haplotypes that the variations may define and consider their geographic and chronologic distribution. Variations at 1930 positions that together cause 1203 amino acid changes were identified. The frequencies of changes normalized to the lengths of genes and encoded proteins were relatively high in ORF3a and relatively low in M. A variation that causes an Asp614Gly near the receptor‐binding domain of S were found at a high frequency, and it was considered that this may contribute to the rapid spread of viruses with this variation. Our most important findings relate to haplotypes. Sixty‐six haplotypes that constitute thirteen haplotype groups (H1–H13) were identified, and 84.6% of the 2790 sequences analyzed were associated with these haplotypes. The majority of the sequences (75.1%) were associated with haplotype groups H1–H3. The distribution pattern of the haplotype groups differed in various geographic regions. A few were country/territory specific. The location and time of emergence of some haplotypes are discussed. Importantly, nucleotide variations that define the various haplotypes and Tag/signature variations for most of the haplotypes are reported. The practical applications of these variations are discussed. Highlights: Variations at 1930 nucleotides and 1203 amino acids identified among 2790 SARS‐CoV‐2 sequences.Frequencies of changes normalized to the lengths of genes and proteins were high in ORF3a and low in M.Asp614Gly near RBD of S may have contributed to the rapid spread of viruses with this variation.84.6% of the 2790 genome sequences defined by 66 haplotypes that constitute 13 haplotype groups.75.1% of 2790 SARS‐CoV‐2 genome sequences were associated with three haplotype groups, H1–H3.The distribution of haplotypes was country/territory/region specific.Sequence variations that define the haplotypes and Tag variations for the haplotypes are reported.The Tag variations can be used as tools for epidemiologic studies. [ABSTRACT FROM AUTHOR]