Identification and Analysis of Sex-Biased Copy Number Alterations.

Background: Sex difference has long been recognized at cancer incidence, outcomes, and responses to therapy. Analyzing the somatic mutation profiles of large-scale cancer samples between the sexes have revealed several potential drivers of cancer with sex difference. However, it is still a demand for in-depth scrutinizing the sex-biased characteristics of genome instability to link the clinical differences for individual cancer type. Methods: Here, we utilized a published framework devised to specifically compare the copy number profiles between 2 groups to identify the sex-biased copy number alterations (CNAs) across 16 cancer types from the The Cancer Genome Atlas Program database, and dissected the impact of those CNAs. Results: Totally, 81 male-biased CNA regions and 23 female-biased CNA regions in 16 cancer types were found. Functional annotation analysis showed that several critical biological functions associated with sex-biased CNAs are shared in multiple cancer types, including immune-related pathways and regulation of cellular signaling. Most sex-biased CNAs have a substantial effect on transcriptional consequence, where the average of over 68% of genes have a linear relationship with CNAs across cancer types, and 14% of those genes are affected by the combination of the sex and copy number. Furthermore, 29 sex-biased CNA regions show latent capacity to be sex-specific prognostic biomarker such as CNA on 11q13.4 for head and neck cancer and lung cancer. Conclusions: This analysis offers new insights into the role of sex in cancer etiology and prognosis through a detailed characterization of sex differences in genome instability of diverse cancers.

2'-O-methylation at internal sites on mRNA promotes mRNA stability.

2'-O-methylation (Nm) is a prominent RNA modification well known in noncoding RNAs and more recently also found at many mRNA internal sites. However, their function and base-resolution stoichiometry remain underexplored. Here, we investigate the transcriptome-wide effect of internal site Nm on mRNA stability. Combining nanopore sequencing with our developed machine learning method, NanoNm, we identify thousands of Nm sites on mRNAs with a single-base resolution. We observe a positive effect of FBL-mediated Nm modification on mRNA stability and expression level. Elevated FBL expression in cancer cells is associated with increased expression levels for 2'-O-methylated mRNAs of cancer pathways, implying the role of FBL in post-transcriptional regulation. Lastly, we find that FBL-mediated 2'-O-methylation connects to widespread 3' UTR shortening, a mechanism that globally increases RNA stability. Collectively, we demonstrate that FBL-mediated Nm modifications at mRNA internal sites regulate gene expression by enhancing mRNA stability.

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning.

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.1.

TNIK regulation of interferon signaling and endothelial cell response to virus infection.

Background: Traf2 and Nck-interacting kinase (TNIK) is known for its regulatory role in various processes within cancer cells. However, its role within endothelial cells (ECs) has remained relatively unexplored. Methods: Leveraging RNA-seq data and Ingenuity Pathway Analysis (IPA), we probed the potential impact of TNIK depletion on ECs. Results: Examination of RNA-seq data uncovered more than 450 Differentially Expressed Genes (DEGs) in TNIK-depleted ECs, displaying a fold change exceeding 2 with a false discovery rate (FDR) below 0.05. IPA analysis unveiled that TNIK depletion leads to the inhibition of the interferon (IFN) pathway [-log (p-value) >11], downregulation of IFN-related genes, and inhibition of Hypercytokinemia/Hyperchemokinemia [-log (p-value) >8]. The validation process encompassed qRT-PCR to evaluate mRNA expression of crucial IFN-related genes, immunoblotting to gauge STAT1 and STAT2 protein levels, and ELISA for the quantification of IFN and cytokine secretion in siTNIK-depleted ECs. These assessments consistently revealed substantial reductions upon TNIK depletion. When transducing HUVECs with replication incompetent E1-E4 deleted adenovirus expressing green fluorescent protein (Ad-GFP), it was demonstrated that TNIK depletion did not affect the uptake of Ad-GFP. Nonetheless, TNIK depletion induced cytopathic effects (CPE) in ECs transduced with wild-type human adenovirus serotype 5 (Ad-WT). Summary: Our findings suggest that TNIK plays a crucial role in regulating the EC response to virus infections through modulation of the IFN pathway.