Insights regarding novel biomarkers and the pathogenesis of primary colorectal carcinoma based on bioinformatic analysis

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

RNA-Seq transcriptome analysis reveals Maackia amurensis leukoagglutinin has antitumor activity in human anaplastic thyroid cancer cells

Background

Lectins are carbohydrate-binding molecules that can bind specifically to the sugar residues of glycoconjugates and are found in almost all organisms. Plant lectins subjected to many studies reported exhibiting anti-cancer activity. This study aimed to investigate the possible molecular mechanisms of Maackia amurensis leukoagglutinin II (MAL-II) treated ATCCs.

Methods and results

We tested the effects of MAL-II, which is isolated from Amur seeds, on cancerous features of 8505C human anaplastic thyroid cancer cells (ATCCs) on a large scale using RNA-Seq. Transcriptome analysis was performed using Illumina next-generation sequencing technology by using cDNA libraries obtained from total RNA isolates of ATCCs treated with 0.25 µM MAL-II for 24 h. Gene ontology and pathway enrichment analysis were performed for the systematic analysis of gene functions. Moreover, we validated RNA-Seq findings using qPCR. Our results showed that many cancer-related genes such as TENM4, STIM2, SYT12, PIEZO2, ABCG1, SPNS2, ARRB1, and IRX5 were downregulated and many anticancer genes such as HSPA6, G0S2, TNFAIP3, GEM, GADD45G, RND1, SERPINB2, and IL24 were upregulated. Also, pathway enrichment analysis showed that differentially expressed genes were found to be associated with Ras, p53, and apoptosis signaling pathways, which are some important signal transduction pathways in development, proliferation, stem cell control, and carcinogenesis.

Conclusion

Collectively, our results show that MAL-II treatment reveals significant antitumor activity by changing the expression of many cancer-related genes and implies that MAL-II treatment might be a potential candidate molecule to inhibit the malignancy of human anaplastic thyroid cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-022-07759-6.

Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: a bioinformatics analysis

BACKGROUND

Genetic factors account for approximately 35% of colorectal cancer risk. The specificity and sensitivity of previous diagnostic biomarkers for colorectal cancer could not meet the need of clinical application. The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning to build informative and predictive models of the underlying biological processes. The aim of this study is to identify diagnostic genes of colorectal cancer by using machine learning methods.

METHODS

The GSE41328 and GSE106582 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The gene expression differences between colon cancer and normal tissues were analyzed. The key colorectal cancer genes were screened and validated by Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) regression. Immune cell infiltration and the correlation with the key genes in patients with colon cancer were further analyzed by CIBERSORT.

RESULTS

Eleven key genes were identified as biomarkers for colon cancer, namely ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4. The mean area under the receiver operating characteristic (ROC) curve (AUC) of all 11 genes for colon cancer diagnosis were 0.94 with a range of 0.91-0.97. In the validation set, the expression of the 11 key genes was significantly different between colon cancer and normal subjects (P<0.05) and the mean AUCs were 0.82 with a range of 0.70-0.88. Immune cell infiltration analyses demonstrated that the relative quantity of plasma cells, T cells, B cells, NK cells, MO, M1, Dendritic cells resting, Mast cells resting, Mast cells activated, and Neutrophils in the tumor group were significantly different to the normal group.

CONCLUSIONS

ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4 were identified as the key genes for colon cancer diagnosis. These genes are expected to become novel diagnostic markers and targets of new pharmacotherapies for colorectal cancer.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

INTRODUCTION

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

METHODS

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

RESULTS

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

CONCLUSION

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

Identification and verification of HCAR3 and INSL5 as new potential therapeutic targets of colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common cancers of the gastrointestinal tract and ranks third in cancer-related deaths worldwide. This study was conducted to identify novel biomarkers related to the pathogenesis of CRC based upon a bioinformatics analysis, and further verify the biomarkers in clinical tumor samples and CRC cell lines.

Methods

A series of bioinformatics analyses were performed using datasets from NCBI-GEO and constructed a protein–protein interaction (PPI) network. This analysis enabled the identification of Hub genes, for which the mRNA expression and overall survival of CRC patients data distribution was explored in The Cancer Genome Atlas (TCGA) colon cancer and rectal cancer (COADREAD) database. Furthermore, the differential expression of HCAR3 and INLS5 was validated in clinical tumor samples by Real-time quantitative PCR analysis, western blotting analysis, and immunohistochemistry analysis. Finally, CRC cells over-expressing INSL5 were constructed and used for CCK8, cell cycle, and cell apoptosis validation assays in vitro.

Results

A total of 286 differentially expressed genes (DEGs) were screened, including 64 genes with increased expression and 143 genes with decreased expression in 2 CRC database, from which 10 key genes were identified: CXCL1, HCAR3, CXCL6, CXCL8, CXCL2, CXCL5, PPY, SST, INSL5, and NPY1R. Among these genes, HCAR3 and INSL5 had not previously been explored and were further verified in vitro.

Conclusions

HCAR3 expression was higher in CRC tissues and associated with better overall survival of CRC patients. INSL5 expression in normal tissue was higher than that in tumor tissue and its high expression was associated with a better prognosis for CRC. The overexpression of INSL5 significantly inhibited the proliferation and promoted the shearing of PARP of CRC cells. This integrated bioinformatics study presented 10 key hub genes associated with CRC. HCAR3 and INSL5 were expressed in tumor tissue and these were associated with poor survival and warrant further studies as potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02335-x.

CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer

Background: Primary colorectal cancer (PCRC) is a common digestive tract cancer in the elderly. However, the treatment effect of PCRC is still limited, and the long-term survival rate is low. Therefore, further exploring the pathogenesis of PCRC, and searching for specific molecular targets for diagnosis are the development trends of precise medical treatment, which have important clinical significance.

Methods: The public data were downloaded from Gene Expression Omnibus (GEO) database. Verification for repeatability of intra-group data was performed by Pearson’s correlation test and principal component analysis. Differentially expressed genes (DEGs) between normal and PCRC were identified, and the protein–protein interaction (PPI) network was constructed. Significant module and hub genes were found in the PPI network. A total of 192 PCRC patients were recruited between 2010 and 2019 from the Fourth Hospital of Hebei Medical University. RT-PCR was used to measure the relative expression of CLCA4 and MS4A12. Furthermore, the study explored the effect of expression of CLCA4 and MS4A12 for overall survival.

Results: A total of 53 DEGs were identified between PCRC and normal colorectal tissues. Ten hub genes concerned to PCRC were screened, namely CLCA4, GUCA2A, GCG, SST, MS4A12, PLP1, CHGA, PYY, VIP, and GUCA2B. The PCRC patients with low expression of CLCA4 and MS4A12 has a worse overall survival than high expression of CLCA4 and MS4A12 (P<0.05).

Conclusion: The research of DEGs in PCRC (53 DEGs, 10 hub genes, especially CLCA4 and MS4A12) and related signaling pathways is conducive to the differential analysis of the molecular mechanism of PCRC.

HDAC3 deteriorates colorectal cancer progression via microRNA-296-3p/TGIF1/TGFβ axis

Background

The mechanism of histone deacetylase 3 (HDAC3) in colorectal cancer (CRC) has already been discussed. However, the feedback loop of HDAC3/microRNA (miR)-296-3p and transforming growth factor β-induced factor 1 (TGIF1) in CRC has not been explained clearly. Thus, the mainstay of this study is to delve out the mechanism of this axis in CRC.

Methods

To demonstrate that HDAC3 regulates the miR-296-3p/TGIF1/TGFβ axis and is involved in CRC progression, a series of cell biological, molecular and biochemical approaches were conducted from the clinical research level, in vitro experiments and in vivo experiments. These methods included RT-qPCR, Western blot assay, cell transfection, MTT assay, EdU assay, flow cytometry, scratch test, Transwell assay, dual luciferase reporter gene assay, chromatin immunoprecipitation, nude mouse xenograft, H&E staining and TUNEL staining.

Results

Higher HDAC3 and TGIF1 and lower miR-296-3p expression levels were found in CRC tissues. HDAC3 was negatively connected with miR-296-3p while positively correlated with TGIF1, and miR-296-3p was negatively connected with TGIF1. Depleted HDAC3 elevated miR-296-3p expression and reduced TGIF1 expression, decreased TGFβ pathway-related proteins, inhibited CRC proliferation, invasion, and migration in vitro and slowed down tumor growth and induction of apoptosis in vivo, which were reversed by miR-296-3p knockdown. Restored miR-296-3p suppressed TGIF1 and reduced TGFβ pathway-related proteins, inhibited CRC proliferation, invasion, and migration in vitro and slowed down tumor growth and induction of apoptosis in vivo, which were reversed by TGIF1 overexpression.

Conclusion

This study illustrates that down-regulation of HDAC3 or TGIF1 or up-regulation of miR-296-3p discourages CRC cell progression and slows down tumor growth, which guides towards a novel direction of CRC treatment.

Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.