Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

Identification and prognostic analysis of candidate biomarkers for lung metastasis in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent types of malignant tumors. It's vital to explore new biomarkers and potential therapeutic targets in CRC lung metastasis through adopting integrated bioinformatics tools. Multiple cohort datasets and databases were integrated to clarify and verify potential key candidate biomarkers and signal transduction pathways in CRC lung metastasis. DAVID, STRING, UALCAN, GEPIA, TIMER, cBioPortal, THE HUMAN PROTEIN ATLAS, GSEA 4.3.2, FUNRICH 3.1.3, and R 4.2.3 were utilized in this study. The enriched biological processes and pathways modulated by the differentially expressed genes (DEGs) were determined with Gene Ontology, Kyoto Encyclopedia of Genes and Genomes. The search tool Retrieval of Interacting Genes and Cytoscape were used to construct a protein-protein interaction network among DEGs. Four hundred fifty-nine colorectal primary cancer and lung metastatic gene expression profiles were screened from 3 gene expression profiles (GSE41258, GSE68468, and GSE41568). Forty-one upregulated genes and 8 downregulated genes were identified from these 3 gene expression profiles and verified by the transcriptional levels of hub genes in other GEO datasets and The Cancer Genome Atlas database. Two pathways (immune responses and chemokine receptors bind chemokines), 13 key DEGs, 6 hub genes (MMP3, SFTPD, ABCA3, CLU, APOE, and SPP1), and 2 biomarkers (APOE, SPP1) with significantly prognostic values were screened. Forty-nine DEGs were identified as potential candidate diagnostic biomarkers for patients with CRC lung metastasis in present study. Enrichment analysis indicated that immune responses and chemokine receptors bind chemokines may play a leading role in lung metastasis of CRC, and further studies are needed to validate these findings.

Innate Immune Cells in the Tumor Microenvironment of Liver Metastasis from Colorectal Cancer: Contribution to a Comprehensive Therapy

Colorectal cancer (CRC) is the third most prevalent type of cancer, and liver metastasis is the most common site of metastatic development. In the tumor microenvironment (TME), various innate immune cells are known to influence cancer progression and metastasis occurrence. CD274 (PD-L1) and CD206 (MRC1) are proteins that have been associated with poor prognosis and disease progression. We conducted a study on tumoral and non-tumoral biopsies from 47 patients with CRC liver metastasis, using flow cytometry to phenotypically characterize innate immune cells. Our findings showed an increase in the expression of CD274 on classical, intermediate, and non-classical monocytes when comparing tumor with non-tumor samples. Furthermore, tumor samples with a desmoplastic growth pattern exhibited a significantly decreased percentage of CD274- and CD206-positive cells in all monocyte populations compared to non-desmoplastic samples. We found a correlation between a lower expression of CD206 or CD274 on classical, intermediate, and non-classical monocytes and increased disease-free survival, which points to a better prognosis for these patients. In conclusion, our study has identified potential new targets and biomarkers that could be incorporated into a personalized medicine approach to enhance the outcome for colorectal cancer patients.

Identification of hub genes and pathways in lung metastatic colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most prevalent types of malignant tumours. Metastasis is the leading cause of cancer-related mortality, with lung metastases accounting for 32.9% of all metastatic CRCs. However, since the biological mechanism of lung metastatic CRC is poorly understood, limited therapeutic targets are available. In the present study, we aimed to identify the key genes and molecular processes involved in CRC lung metastasis.

METHODS

The differentially expressed genes (DEGs) between primary and lung metastatic CRC patients were obtained from the Gene Expression Omnibus (GEO) database via the GEO2R tool. The enriched biological processes and pathways modulated by the DEGs were determined with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome Gene Sets analyses. The search tool Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct a protein-protein interaction (PPI) network among DEGs.

RESULTS

The DEGs were enriched in surfactant metabolism, cell-cell communication and chemokine signaling pathways. The defined hub genes were included CLU, SFTPD, CCL18, SPP1, APOE, BGN and MMP3. Among them, CLU, SFTPD and CCL18 might be associated with the specific lung tropism metastasis in CRC. In addition, the expression and prognostic values of the hub genes in CRC patients were verified in database of The Cancer Genome Atlas (TCGA) and GEO. Moreover, the protein levels of the hub genes were detected in primary and lung metastatic CRC cells, serum or tissues. Furthermore, SFTPD was confirmed to facilitate cellular proliferation and lung metastasis in CRC.

CONCLUSION

This bioinformatics study may provide a better understanding of the candidate therapeutic targets and molecular mechanisms for CRC lung metastasis.

A Prognostic Signature for Colon Adenocarcinoma Patients Based on m6A-Related lncRNAs

N6-methyladenosine (m6A) modification is a common epigenetic modification. It is reported that lncRNA can be regulated by m6A modification. Previous studies have shown that lncRNAs associated with m6A regulation (m6A-lncRNAs) serve as ideal prognostic biomarkers. However, whether lncRNAs are involved in m6A modification in colon adenocarcinoma (COAD) needs further exploration. The objective of this study was to construct an m6A-lncRNAs-based signature for patients with COAD. We obtained the RNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA). Pearson correlation analysis was employed to recognize lncRNAs associated with m6A regulation (m6A-lncRNAs). 24 prognostic m6A-lncRNAs was identified by univariate Cox regression analysis. Gene set enrichment analysis (GSAE) was used to investigate the potential cellular pathways and biological processes. We have also explored the relationship between immune infiltrate levels and m6A-lncRNAs. Then, a predictive signature based on the expression of 13 m6A-lncRNAs was constructed by the Lasso regression algorithm, including UBA6-AS1, AC139149.1, U91328.1, AC138207.5, AC025171.4, AC008760.1, ITGB1-DT, AP001619.1, AL391422.4, AC104532.2, ZEB1-AS1, AC156455.1, and AC104819.3. ROC curves and K M survival curves have shown that the risk score has a well-predictive ability. We also set up a quantitative nomogram on the basis of risk score and prognosis-related clinical characteristics. In summary, we have identified some m6A-lncRNAs that correlated with prognosis and tumor immune microenvironment in COAD. In addition, a potential alternative signature based on the expression of m6A-lncRNAs was provided for the management of COAD patients.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Colorectal liver metastasis: molecular mechanism and interventional therapy

Colorectal cancer (CRC) is one of the most frequently occurring malignancy tumors with a high morbidity additionally, CRC patients may develop liver metastasis, which is the major cause of death. Despite significant advances in diagnostic and therapeutic techniques, the survival rate of colorectal liver metastasis (CRLM) patients remains very low. CRLM, as a complex cascade reaction process involving multiple factors and procedures, has complex and diverse molecular mechanisms. In this review, we summarize the mechanisms/pathophysiology, diagnosis, treatment of CRLM. We also focus on an overview of the recent advances in understanding the molecular basis of CRLM with a special emphasis on tumor microenvironment and promise of newer targeted therapies for CRLM, further improving the prognosis of CRLM patients.

AVL9 promotes colorectal carcinoma cell migration via regulating EGFR expression

Background

Despite advanced treatments could inhibit progression of colorectal carcinoma (CRC), the recurrence and metastasis remain challenging issues. Accumulating evidences implicated that AVL9 played a vital role in human cancers, but it’s biological function and mechanism in CRC remain unclear.

Aim

To investigate the biological role and mechanism of AVL9 in colorectal carcinoma.

Results

AVL9 expression was significantly upregulated in tumor tissues than that in matched normal tissues both at mRNA and protein levels. High expression of AVL9 was closely correlated with M status, stages and poor prognosis of colorectal carcinoma (CRC) patients. Functionally, AVL9 overexpression promoted cell migration rather than cell proliferation in vitro, whereas AVL9 knockdown exhibited the contrary results. Mechanistically, AVL9 regulated EGFR expression, and knockdown of EGFR restrained AVL9-induced cell migration.

Conclusion

These findings demonstrated that AVL9 contributed to CRC cell migration by regulating EGFR expression, suggesting a potential biomarker and treatment target for CRC.