An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes

Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective

OBJECTIVE

Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies.

METHODS

We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC.

RESULTS

Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets.

CONCLUSIONS

Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.

Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study

OBJECTIVES

Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making.

METHODS

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models.

RESULTS

Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk.

CONCLUSIONS

Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Identification of multiple organ metastasis-associated hub mRNA/miRNA signatures in non-small cell lung cancer

Metastasis remains major cause of treatment failure in non-small cell lung cancer (NSCLC). A comprehensive characterization of the transcriptomic landscape of NSCLC-cells with organ-specific metastatic potentials would advance our understanding of NSCLC metastasis process. In this study, we established NSCLC bone-metastatic (BoM), brain-metastatic (BrM), and lymph-metastatic (LnM) cells by an in vivo spontaneous metastatic model. Subsequently, by analyzing the entire transcriptomic profiles of BoM, BrM, LnM, LuM, in comparison with their parental cell line L9981, we identified miR-660-5p as a key driver that is associated with NSCLC progression and distant metastasis, potentially through its targeting of LIMCH1, SMARCA5 and TPP2. In addition, a six-gene signature (ADRB2, DPYSL2, IL7R, LIMCH1, PIK3R1, and SOX2) was subsequently established to predict NSCLC metastasis based on differentially expressed genes, three of which (DPYSL2, PIK3R1, LIMCH1) along with the transcriptional factors RB1 and TP63, were ultimately validated by experiments. Taken together, aberrant gene signature and miRNA can serve as biomarkers for predicting NSCLC distant metastasis, and targeting them could potentially contribute to the development of novel therapeutic strategies.

USP7-mediated JUND suppresses RCAN2 transcription and elevates NFATC1 to enhance stem cell property in colorectal cancer

Cancer stem cells (CSCs) encompass a subset of highly aggressive tumor cells that are involved in tumor initiation and progression. This study investigates the function of regulator of calcineurin 2 (RCAN2) in the stem cell property in colorectal cancer (CRC). By analyzing four GEO datasets, we obtained RCAN2 as a stemness-related gene in CRC. RCAN2 was poorly expressed in CRC tissues and cells, especially in CSCs. RCAN2 restoration reduced calcineurin activity and promoted phosphorylation and degradation of nuclear factor of activated T cells 1 (NFATC1) protein, leading to reduced stemness of CSCs. JunD proto-oncogene (JUND), whose protein level was increased in CRC samples and CRC stem cells, bound to RCAN2 and suppressed its transcription. The abundant ubiquitin specific peptidase 7 (USP7) in CSCs enhanced JUND protein stability through deubiquitination modification. Lentivirus-mediated knockdown of USP7 or JUND also blocked the calcineurin-NFATC1 signaling and reduced the protein levels of stemness-related proteins. Moreover, the USP7 knockdown weakened the colony/sphere formation ability as well as the tumorigenicity of CSCs, and it reduced the CSC content in xenograft tumors. However, further restoration of JUND rescued the stemness of the CSCs. Overall, this study demonstrates that USP7-mediated JUND suppresses RCAN2 transcription and activates NFATC1 to enhance stem cell property in CRC. 1. RCAN2 is poorly expressed in CRC tissues and cells and especially in CSCs. 2. RCAN2 reduces stemness of CSCs by blocking calcineurin-NFATC1 signal transduction. 3. JUND binds to RCAN2 promoter to suppresses RCAN2 transcription. 4. USP7 enhances JUND protein stability via deubiquitination modification. 5. Downregulation of USP7 or JUND restores RCAN2 level and suppresses stemness of CSCs.

Computational approaches to understand transcription regulation in development

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

Multi-OMICs data analysis identifies molecular features correlating with tumor immunity in colon cancer

BACKGROUND

There is a current need for new markers with higher sensitivity and specificity to predict immune status and optimize immunotherapy use in colon cancer.

OBJECTIVE

We aimed to investigate the multi-OMICs features associated with colon cancer immunity and response to immunotherapy.

METHODS

We evaluated the association of multi-OMICs data from three colon cancer datasets (TCGA, CPTAC2, and Samstein) with antitumor immune signatures (CD8+ T cell infiltration, immune cytolytic activity, and PD-L1 expression). Using the log-rank test and hierarchical clustering, we explored the association of various OMICs features with survival and immune status in colon cancer.

RESULTS

Two gene mutations (TERT and ERBB4) correlated with antitumor cytolytic activity found also correlated with improved survival in immunotherapy-treated colon cancers. Moreover, the expression of numerous genes was associated with antitumor immunity, including GBP1, GBP4, GBP5, NKG7, APOL3, IDO1, CCL5, and CXCL9. We clustered colon cancer samples into four immuno-distinct clusters based on the expression levels of 82 genes. We have also identified two proteins (PREX1 and RAD50), ten miRNAs (hsa-miR-140, 146, 150, 155, 342, 59, 342, 511, 592 and 1977), and five oncogenic pathways (CYCLIN, BCAT, CAMP, RB, NRL, EIF4E, and VEGF signaling pathways) significantly correlated with antitumor immune signatures.

CONCLUSION

These molecular features are potential markers of tumor immune status and response to immunotherapy.

ISLR affects colon cancer progression by regulating the epithelial-mesenchymal transition signaling pathway

This study aims to determine the mechanism of ISLR on the progression of colon cancer. TCGA database was used to analyze ISLR expression in colon cancer tumor tissues. QRT-PCR and western blotting were used to detect ISLR expression in colon cancer cells. CCK-8, colony formation, EDU, wound healing and transwell assays were used to measure cell viability, proliferation, migration and invasion of colon cancer cells, respectively. The signaling pathway enrichment analysis of ISLR was analyzed on the basis of the KEGG database. The protein expression of genes related to signaling pathway was measured by western blotting. Results of TCGA analysis, qRT-PC and western blotting showed that ISLR was upregulated in colon cancer tumor tissues and cells. High level of ISLR was related to low overall survival of patients with colon cancer. ISLR silence significantly inhibited cell viability, proliferation, migration and invasion of colon cancer cells. ISLR overexpression markedly enhanced the cell viability, proliferation, migration and invasion of colon cancer cells. KEGG database analyzed showed that ISLR can activate the EMT signaling pathway. Inhibition of the EMT signaling pathway can suppress the growth, migration, and invasion of colon cancer cells and eliminate the promoted effect of ISLR overexpression on colon cancer progression. ISLR promotes the progression of colon cancer by activating the EMT signaling pathway.

Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

BACKGROUND

Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge.

METHODS

Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively.

RESULTS

Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways.

CONCLUSIONS

Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.