Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Dysregulated Forkhead Box (FOX) Genes Association with Survival Prognosis, Anti-tumor Immunity, and Key Targeting Drugs in Colon Adenocarcinoma

BACKGROUND

Several studies have revealed that the aberrant expressions of forkhead box (FOX) genes are associated with carcinogenesis. However, the crucial biological functions of the FOX gene in colon adenocarcinoma (COAD) remain unknown.

METHODS

The TCGA-COAD dataset (n=328) was utilized for determining the deregulated FOX genes and their association with functional enrichment, protein-protein interaction (PPI), survival prognosis, anti-tumor immunity, cancer-associated pathways, and biological processes in COAD. In addition, we used GSE166427 (GPL13667) as a validation cohort (n=196). Molecular docking studies were applied to perform the drug interactions.

RESULTS

The FOX genes are deregulated in the COAD (Log2 FC>0.50, P<0.05), and the PPI network of FOX members is substantially related to the enrichment of cancerous signaling, immune responses, and cellular development (FDR<0.05). A worse prognosis for overall survival in COAD individuals is connected with the subgroup of FOX transcripts (P≤0.05). FOXD4, FOXH1, and FOXS1 were identified as predictive variables in the univariate and multivariate Cox regression models (P≤0.05). FOXH1 and FOXS1 are substantially linked to the deregulated immunity in COAD (R>0.20, P<0.01). Furthermore, FOXS1 expression regulates cancer-associated pathways and biological processes (P<0.05). Moreover, FOXD4, FOXH1, and FOXS1 are genetically altered and showed diagnostic efficacy in COAD. We revealed that FOXD4, FOXH1, and FOXS1 are consistently deregulated in GSE166427 (P<0.05). Finally, molecular docking revealed that FOXH1 interacted with various drugs, including belinostat, entinostat, and panobinostat.

CONCLUSION

The FOX genes have a strong correlation with the poor prognosis for survival, tumor immunity, cancer-associated pathways, and biochemical processes that cause the pathogenesis of COAD.

Integrated transcriptomics and proteomics data analysis identifies CDH17 as a key cell surface target in colorectal cancer

Immunotherapy development against colorectal cancer (CRC) is hindered by the lack of cell surface target highly expressed in cancer cells but with restricted presence in normal tissues to minimize off-tumor toxicities. In this in silico analysis, a longlist of genes (n = 13,488) expressed in CRCs according to the Human Protein Atlas (HPA) database were evaluated to shortlist for potential surface targets based on the following prerequisites: (i) Absent from the brain and lung tissues to minimize the likelihood of neurologic and pulmonary toxicities; (ii) Restricted expression profile in other normal human tissues; (iii) Genes that potentially encode cell surface proteins and; (iv) At least moderately expressed in CRC cases. Fifteen potential targets were shortlisted and subsequently ranked according to the combination of their transcript and protein expression levels in CRCs derived from multiple datasets (i.e. DepMap, TCGA, CPTAC-2, and HPA CRCs). The top-ranked target with the highest and homogenous expression in CRCs was cadherin 17 (CDH17). Downstream analysis of CRC transcriptomics and proteomics datasets showed that CDH17 was significantly correlated with carcinoembryonic antigen expression. Moreover, CDH17 expression was significantly lower in CRC cases with high microsatellite instability, as well as negatively associated with immune response gene sets and the expression of MHC class I and II molecules. CDH17 represents an optimal target for therapeutic development against CRCs, and this study provides a novel framework to identify key cell surface targets for therapeutic development against other malignancies.

Multiomics Integration at Single-Cell Resolution Using Bayesian Networks: A Case Study in Hepatocellular Carcinoma

Multiomics data integration is one of the leading frontiers of complex disease research and integrative biology. The advances in single-cell sequencing technologies offer yet another crucial dimension in multiomics research. The single-cell studies enable the study and integration of multiomics data simultaneously in the same cell. We report in this study multiomics data integration in single-cell resolution using Bayesian networks (BNs) in a case study of hepatocellular carcinoma (HCC). A BN encodes the conditional dependencies/independencies of variables using a graphical model with an accompanying joint probability. RNA-seq and Reduced Representation Bisulfite Sequencing data were analyzed separately, and copy number variations were estimated by the hidden Markov model method. Several BN models were constructed to reveal omics' causal and associational relationships. These methods were subjected to a validation study using an independent data set. We show the heterogeneity of the multiple cellular layers of HCC at single-cell omics resolution by identifying best-fitted BN models of 295 genes. We also provide novel insights into the multiomics mechanistic relationships in the human lymphocyte antigen class I genes in HCC. To the best of our knowledge, this is the first study to focus on integrating omics data using a machine learning algorithm, BNs, at the single-cell resolution using a case study of HCC.

Endoplasmic reticulum-resident protein Sec62 drives colorectal cancer metastasis via MAPK/ATF2/UCA1 axis

OBJECTIVE

Metastasis is responsible for the poor prognosis of patients with colorectal cancer (CRC), and the role of aberrant expression of endoplasmic reticulum (ER) receptors in tumour metastasis has not been fully elucidated. The aim of the study is to ensure the role of ER-resident protein Sec62 in CRC metastasis and illuminate associated molecular mechanisms.

MATERIALS AND METHODS

Bioinformatics analysis, qRT-PCR, western blot and immunohistochemistry assays were performed to evaluate the expression level and clinical significance of Sec62 in CRC. The specific role of Sec62 in CRC was identified by a series of functional experiments. We conducted RNA sequencing and rescue experiments to analyse the differentially expressed genes and identified UCA1 as a novel pro-metastasis target of Sec62 in CRC. Besides, the efficacy of MAPK/JNK inhibitor or agonist on Sec62-mediated CRC metastasis was evaluated by trans-well and wound healing assays. Finally, luciferase reporter and ChIP assay were employed to further explore the potential mechanisms.

RESULTS

The abnormally elevated expression of Sec62 predicted poor prognosis of CRC patients and facilitated malignant metastasis of CRC cells. Mechanistically, Sec62 enhanced UCA1 expression through activating MAPK/JNK signalling pathway. And the p-JNK activating ATF2 could transcriptionally regulate UCA1 expression. Furthermore, blocking or activating MAPK/JNK signalling with JNK inhibitor or agonist potently suppressed or enhanced Sec62 mediated CRC metastatic process.

CONCLUSIONS

Our study reports for the first time that the Sec62/MAPK/ATF2 /UCA1 axis exists in CRC metastatic process, which could be a potential treatment target of metastatic CRC.

Identification and analysis of a CD8+ T cell-related prognostic signature for colorectal cancer based on bulk RNA sequencing and scRNA sequencing data: A STROBE-compliant retrospective study

Colorectal cancer (CRC) is one of the most common malignancies worldwide, leading to a large number of cancer-related mortalities. Aberrant CD8+ T cell infiltration plays a critical role in tumor progression and patient prognosis. This study aimed to identify a prognostic model for CRC based on CD8+ T cell-related genes. The infiltration levels of immune cells in CRC tissues were accessed by the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) analysis was used to select CD8+ T cell-related genes. Prognostic genes were identified using Cox regression analysis and Kaplan-Meier curves. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct prognostic models. Gene set enrichment analysis (GSEA) was performed to annotate enriched gene sets. Single-cell RNA (scRNA) sequencing analysis was used to examine gene expression in different cell types. We found that the downregulated infiltration level of CD8+ T cells was an independent prognostic factor for CRC and selected a cluster of differentially expressed genes correlated with CD8+ T cell infiltration (CD8TDEGs). Subsequently, we identified 18 prognostic CD8TDEGs, according to which patients were reclassified into two clusters with distinct overall survival. Seven prognostic CD8TDEGs were selected to calculate the constructed prognostic model's risk scores. Interestingly, although CRC tissues with higher risk scores had higher infiltration levels of CD8+ T cells, the level of immune checkpoint genes was also high. Moreover, the scRNA-sequencing analysis showed that the expression levels of CD8TDEGs in the prognostic model varied among different types of cells. This study constructed a novel prognostic model for CRC and provided a foundation for targeting CD8+ T cell infiltration to improve the survival of CRC patients.

Construction of a miRNA-mRNA Network Related to Exosomes in Colon Cancer

Background

The competing endogenous RNA (CeRNA) network plays important roles in the occurrence and development of colon cancer. This research is aimed at constructing a miRNA-mRNA network associated with exosomes in colon cancer.

Methods

We explored the GEO database and then analyzed the RNAs of 722 samples to obtain differentially expressed miRNAs (DEMs) and mRNAs (DEGs) alongside the progress of colon cancer. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEM target genes and DEGs were performed. In addition, a miRNA-mRNA network related to exosomes in colon cancer was constructed based on DEMs and DEGs. Finally, the expression of miRNA and mRNA in the network was verified by GEPIA2 on the base of TCGA database.

Results

Through our analysis, 19 DEMs (17 up and 2 down) and 1672 DEGs (954 up and 718 down) were screened. The GO and KEGG results show that these DEGs were mainly enriched in ribonucleoprotein complex biogenesis, noncoding RNA metabolic process, cell-substrate junction, cadherin binding, transcription coregulator activity, and regulation of the human T-cell leukemia virus 1 infection-related pathway. Besides, a miRNA-mRNA network, including 4 miRNAs (hsa-miR-623, hsa-miR-320c, hsa-miR-486-5p, and hsa-miR-1290) and 7 mRNAs (GNAI1, CADM1, PGRMC2, etc.), was constructed. Three of these seven mRNAs were downregulated in colon cancer. Ultimately, the GNAI1, CADM1, and PGRMC2 expression levels were verified by TCGA database.

Conclusions

This study reveals the network relationship between colon cancer exosome-derived miRNA and targeted mRNA. It deepens our understanding of new molecular mechanisms and pathways that may play a role in the occurrence and metastasis of colon cancer.

Pan-Cancer Transcriptome and Immune Infiltration Analyses Reveal the Oncogenic Role of Far Upstream Element-Binding Protein 1 (FUBP1)

Despite increasing evidence to support the relationship between FUBP1 and tumorigenesis in some types of cancers, there have been no analyses from a pan-cancer perspective. Here, we are the first to investigate the putative oncogenic role of FUBP1 in 33 cancer types based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Dysregulated FUBP1 expression was observed in most cancer types, and high FUBP1 expression suggests poor prognosis in cancers such as ACC, KICH, LIHC, LUAD, LUSC, SARC, CESC, and SKCM. Missense mutation is the most common type of FUBP1 mutation, and R430 in KH_4 is a predominant mutation site. Enhanced phosphorylation of FUBP1 at the S120 site has been observed in clear cell RCC, lung adenocarcinoma, and pediatric brain cancer specimens from African-American and Asian individuals. The expression of FUBP1 was found to be negatively correlated with the infiltration of CD8+ T lymphocytes in GBM, HNSC-HPV- and UCEC but positively correlated with that of tumor-associated fibroblasts in CESC, ESCA, HNSC, LIHC, LUAD, PAAD, and THYM. Furthermore, RNA splicing and spliceosome signaling were predominantly enriched in both GO and KEGG analyses of the functional mechanism of FUBP1. Briefly, this pan-cancer analysis comprehensively revealed the multifaceted characteristics and oncogenic role of FUBP1 in different human cancers.