Advances in colorectal cancer genomics and transcriptomics drive early detection and prevention

Functional annotation with expression validation identifies novel metastasis-relevant genes from post-GWAS risk loci in sporadic colorectal carcinomas

BACKGROUND

Colorectal cancer (CRC) is the third highest incidence cancer and is the leading cause of cancer mortality worldwide. Metastasis to distal organ is the major cause of cancer mortality. However, the underlying genetic factors are unclear. This study aimed to identify metastasis-relevant genes and pathways for better management of metastasis-prone patients.

METHODS

A case-case genome-wide association study comprising 2677 sporadic Chinese CRC cases (1282 metastasis-positive vs 1395 metastasis-negative) was performed using the Human SNP6 microarray platform and analysed with the correlation/trend test based on the additive model. SNP variants with association testing -log10 p value ≥5 were imported into Functional Mapping and Annotation (FUMA) for functional annotation.

RESULTS

Glycolysis was uncovered as the top hallmark gene set. Transcripts from two of the five genes profiled, hematopoietic substrate 1 associated protein X 1 (HAX1) and hyaluronan-mediatedmotility receptor (HMMR), were significantly upregulated in the metastasis-positive tumours. In contrast to disease-risk variants, HAX1 appeared to act synergistically with HMMR in significantly impacting metastasis-free survival. Examining the subtype datasets with FUMA and Ingenuity Pathway Analysis (IPA) identified distinct pathways demonstrating sexual dimorphism in CRC metastasis.

CONCLUSIONS

Combining genome-wide association testing with in silico functional annotation and wet-bench validation identified metastasis-relevant genes that could serve as features to develop subtype-specific metastasis-risk signatures for tailored management of patients with stage I-III CRC.

Identification of crucial genes related to heart failure based on GEO database

BACKGROUND

The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.

METHODS

First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.

RESULTS

In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.

CONCLUSIONS

Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Mechanistic insights into the anti-tumor and anti-metastatic effects of Patrinia villosa aqueous extract in colon cancer via modulation of TGF-β R1-smad2/3-E-cadherin and FAK-RhoA-cofilin pathways

BACKGROUND

Patrinia villosa, a traditional medicinal herb commonly used for treating intestinal-related diseases, has been commonly prescribed by Chinese medicine practitioners as a key component herb to treat colon cancer, although its anti-tumor effect and mechanisms of action have not been fully elucidated.

HYPOTHESIS/PURPOSE

This study aimed to investigate the anti-tumor and anti-metastatic effects of Patrinia villosa aqueous extract (PVW), and its underlying mechanisms.

METHOD

The chemical profile of PVW was analysed by high-performance liquid chromatography with photodiode-array detection (HPLC-DAD) method. Cell-based functional assays MTT, BrdU, scratch, and transwell were conducted to evaluate the effects of PVW on human colon cancer HCT116 and murine colon26-luc cells, assessing cytotoxicity, cell proliferation, motility, and migration, respectively. Western blotting was performed to assess the effect of PVW on the expression of key intracellular signaling proteins. In vivo studies were conducted using zebrafish embryos and tumor-bearing mice to evaluate the anti-tumor, anti-angiogenesis, and anti-metastatic effects of PVW in colon cancer.

RESULTS

Five chemical markers were identified and quantified in PVW. PVW exhibited significant cytotoxicity and anti-proliferative activity, as well as inhibitory effects on cell motility and migration in both HCT116 and colon 26-luc cancer cells via modulating protein expressions of TGF-β R1, smad2/3, snail, E-cadherin, FAK, RhoA, and cofilin. PVW (0.01-0.1 mg/ml) could significantly decrease the length of subintestinal vessels of zebrafish embryos through decreasing mRNA expressions of FLT1, FLT4, KDRL, VEGFaa, VEGFc, and Tie1. PVW (> 0.05 mg/ml) also significantly suppressed colon cancer cells migration in the zebrafish embryos. Furthermore, oral administration of PVW (1.6 g/kg) significantly inhibited tumor growth by decreasing the expressions of tumor activation marker Ki-67 and CD 31 in tumor tissues of HCT116 tumor-bearing mice. PVW could also significantly inhibit lung metastasis in colon 26-luc tumor-bearing mice by modulating their tumor microenvironment, including immune cells populations (T cells and MDSCs), levels of cytokines (IL-2, IL-12, and IFN-γ), as well as increasing the relative abundance of gut microbiota.

CONCLUSION

This study revealed for the first time the anti-tumor and anti-metastatic effects of PVW through regulation of TGF-β-smad2/3-E-cadherin, and FAK-cofilin pathways in colon cancer. These findings provide scientific evidence to support the clinical use of P. villosa in patients with colon cancer.

Identifying driver modules based on multi-omics biological networks in prostate cancer

The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors' method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.