Identification and analysis of key genes related to efferocytosis in colorectal cancer

Abstract

The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis.

Background: It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC).

Methods: In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR).

Results: There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187.

Conclusion: Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.

Keywords: Biomarkers; Colorectal cancer; Diagnosis; Efferocytosis; Immune.

Fig. 1

Identification and function of DEGs. (A) Volcano plot and (B) Heat map of DEGs between the CRC group and control group. (C) Venn diagram, (D) PPI network, and (E) Enrichment results for DE-ERGs

Fig. 2

Screening for biomarkers. (A, B) LASSO regression analysis and (C) Boruta to screen characteristic genes. (D) Venn diagram of common characteristic genes. Expression of candidate key genes in (E) training set and (F) GSE71187. ROC curves for three biomarkers in (G) training set and (H) GSE71187. (I) Heat map presents biomarker correlations. (J) Distribution of biomarkers on chromosomes. *P < 0.05, **P < 0.01, ***P < 0.001. LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic

Fig. 3

Creation and verification of Nomogram model. (A) Nomogram of biomarkers. (B) calibration curve, (C) DCA curve, and (D) ROC curves for nomogram. DCA, decision curve analysis; ROC, receiver operating characteristic

Fig. 4

GSEA and IPA results for biomarkers. GSEA results of (A) PDK4, (B) P2RY12, and (C) ELMO3 in GO. GSEA results of (D) PDK4, (E) P2RY12, and (F) ELMO3 in KEGG. (G) IPA results of biomarkers. GSEA, gene set enrichment analysis; IPA, ingenuity pathway analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes

Fig. 5

Immunocorrelation analysis and genetic variation analysis. (A) Stacked graph of immune cell percentage. (B) Comparison of immune cells between CRA and control group. (C) Correlation of biomarkers with immune cells. Correlation of biomarkers with immune checkpoints: (D) PDK4, (E) P2RY12, and (F) ELMO3. (G) Mutation frequency of biomarkers in CRC samples. CRC, Colorectal cancer

Fig. 6

Molecular regulatory networks and drug sensitization. (A) The mRNA-miRNA-lncRNA network for 3 mRNAs (biomarkers), 33 miRNAs, and 22 lncRNAs. (B) The miRNA/TF-mRNA network for 3 mRNAs, 33 miRNAs, and 7 TFs. (C) The miRNA-SNP-mRNA network for 3 mRNAs, 33 miRNAs, and 63 SNP locations of miRNAs. (D) Correlation of biomarker expression with drug sensitization. miRNA, microRNA; TF, transcription factor; SNP, single nucleotide polymorphism

Fig. 7

The expression levels of the biomarkers. (A) ELMO3, (B) P2RY12, and (C) PDK4. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.001