Identification of Candidate Biomarkers and Prognostic Analysis in Colorectal Cancer Liver Metastases

Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms

Objective

The purpose of the current study was to develop and validate a biomarker-based prediction model for metastasis in patients with colorectal cancer (CRC).

Methods

Two datasets, GSE68468 and GSE41568, were retrieved from the Gene Expression Omnibus (GEO) database. In the GSE68468 dataset, key biomarkers were identified through a screening process involving differential expression analysis, redundancy analysis, and recursive feature elimination technique. Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). The predictive performance of the algorithm with the highest accuracy was then externally validated on the GSE41568 dataset.

Results

Among 22,283 registered genes in the GSE68468 dataset, the screening process identified 16 key genes including MMP3, CCDC102B, CDH2, SCGB1A1, KRT7, CYP1B1, LAMC3, ALB, DIXDC1, VWF, MMP1, CYP4B1, NKX3-2, TMEM158, GADD45B, SERPINA1 and these genes were used to build the prediction model. On the internal validation dataset, the prediction performance of five ML algorithms was as follows; RF (accuracy = 0.97 and kappa = 0.91), XGBoost (0.93, 0.81), kNN (0.93, 0.81), glmnet (0.93, 0.82) and SVM (0.92, 0.80). Top five biomarkers were MMP3, CCDC102B, CDH2, VWF and MMP1. The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset.

Conclusion

The results of this study have identified biomarkers through ML algorithms which help to identify patients with CRC prone to metastasis.

A simplified and efficient extracellular vesicle-based proteomics strategy for early diagnosis of colorectal cancer

Colorectal cancer (CRC) is a major cause of cancer-related death worldwide and an effective screening strategy for diagnosis of early-stage CRC is highly desired. Although extracellular vesicles (EVs) are expected to become some of the most promising tools for liquid biopsy of early disease diagnosis, the existing EV-based proteomics methods for practical application in clinical samples are limited by technical challenges in high-throughput isolation and detection of EVs. In the current study, we have developed a simplified and efficient EV-based proteomics strategy for early diagnosis of CRC. DSPE-functionalized beads were specifically designed that enabled direct capture of EVs from plasma samples in 10 minutes with good reproducibility and comprehensive proteome coverage. The single-pot, solid-phase-enhanced sample-preparation (SP3) technology was then combined with data-independent acquisition mass spectrometry (DIA-MS) for in-depth analysis and quantification of EV proteomes. From a cohort with 30 individuals including 11 healthy controls, 8 patients with adenomatous polyp and 11 patients with early-stage CRC, our streamlined workflow reproducibly quantified over 800 proteins from their plasma-derived EV samples, from which dysregulated protein signatures for molecular diagnosis of CRC were revealed. We selected a panel of 10 protein markers to train a machine learning (ML) model, which resulted in accurate prediction of polyp and early-stage CRC in an independent and single-blind validation cohort with excellent diagnostic ability of 89.3% accuracy. Our simplified and efficient clinical proteomic strategy will serve as a valuable tool for fast, accurate, and cost-effective diagnosis of CRC that can be easily extended to other disease samples for discovery of unique EV-based biomarkers.

Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning

BACKGROUND

Liver metastasis is one of the primary causes of poor prognosis in colon adenocarcinoma (COAD) patients, but there are few studies on its biomarkers.

METHODS

The Cancer Genome Atlas (TCGA)-COAD, GSE41258, and GSE49355 datasets were acquired from the public database. Differentially expressed genes (DEGs) between liver metastasis and primary tumor samples in COAD were identified by limma, and functional enrichment analysis were performed. MuTect2 and maftools were used to measure somatic mutation rates, while ADTEx was used to measure copy number variations (CNVs). The intersection of three machine learning methods, support vector machine (SVM), Random Forest, and least absolute shrinkage and selection operator (LASSO), is utilized to screen biomarkers, and their diagnostic performance is subsequently validated. The correlation between biomarkers and immune cells infiltration was analyzed by Spearman method.

RESULTS

47 DEGs between liver metastasis and primary tumor samples in COAD were obtained, which were mainly enriched in the complement and coagulation, extracellular matrix (ECM), and peptidase regulator activity, etc. 38 out of 47 DEGs had mutations and exhibited a high frequency of CNV amplification or deletion. Furthermore, 3 biomarkers (MMP3, MAB21L2, and COLEC11) were screened, which showed good diagnostic performance. The proportion of multiple immune cells, such as B cells naive, T cells CD4 naive, Monocytes, and Dendritic cells resting, was higher in liver metastasis samples than that in primary tumor samples. Meanwhile, MMP3, MAB21L2, and COLEC11 exhibited an outstanding correlation with immune cells infiltration.

CONCLUSION

In short, 3 biomarkers with good diagnostic efficacy were identified, providing a new perspective of therapeutic targets for liver metastasis in COAD.

Evaluation of circulating plasma proteins in prostate cancer using mendelian randomization

BACKGROUND

The proteome is an important resource for exploring potential diagnostic and therapeutic targets for cancer. This study aimed to investigate the causal associations between plasma proteins and prostate cancer (PCa), and to explore the downstream phenotypes that plasma proteins may influence and potential upstream intervening factors.

METHODS

Proteome-wide Mendelian randomization was used to investigate the causal effects of plasma proteins on PCa. Colocalization analysis examined the common causal variants between plasma proteins and PCa. Summary-statistics-based Mendelian Randomization (SMR) analyses identified associations between the expression of protein-coding genes and PCa. Phenome-wide association study was performed to explore the effect of target proteins on downstream phenotypes. Finally, a systematic Mendelian randomization analysis between lifestyle factors and plasma proteins was performed to assess upstream intervening factors for plasma proteins.

RESULTS

The findings revealed a positive genetic association between the predicted plasma levels of nine proteins and an elevated risk of PCa, while four proteins exhibited an inverse association with PCa risk. SMR analyses revealed ZG16B, PEX14 in blood and ZG16B, NAPG in prostate tissue were potential drug targets for PCa. The genetic association of PEX14 with PCa was further supported by colocalization analysis. Further Phenome-wide association study showed possible side effects of ZG16B, PEX14 and NAPG as drug targets. 10 plasma proteins (RBP7, TPST1, NFASC, LAYN, HDGF, SERPIMA5, DLL4, EFNA3, LIMA1, and CCL27) could be modulated by lifestyle-related factors.

CONCLUSION

This study explores the genetic associations between plasma proteins and PCa, provides evidence that plasma proteins serve as potential drug targets and enhances the understanding of the molecular etiology, prevention and treatment of PCa.

Survival prediction in sigmoid colon cancer patients with liver metastasis: a prospective cohort study

BACKGROUND

Sigmoid colon cancer is a common type of colorectal cancer, frequently leading to liver metastasis. Predicting cause-specific survival and overall survival in patients with sigmoid colon cancer metastasis to liver is challenging because of the lack of suitable models.

METHODS

Patients with sigmoid colon cancer metastasis to liver (2010-2017) in the Surveillance, Epidemiology, and End Results (SEER) Program were recruited. Patients were split into training and validation groups (7:3). Prognostic factors were identified using competing risk and Cox proportional hazards models, and nomograms for cause-specific survival and overall survival were developed. Model performance was evaluated with the concordance index and calibration curves, with a 2-sided P value less than .05 considered statistically significant.

RESULTS

A total of 4981 sigmoid colon cancer with liver metastasis patients were included, with a median follow-up of 20 months (interquartile range [IQR] = 9-33 months). During follow-up, 72.25% of patients died (68.44% from sigmoid colon cancer, 3.81% from other causes). Age, race, grade, T stage, N stage, surgery, chemotherapy, carcinoembryonic antigen, tumor deposits, lung metastasis, and tumor size were prognostic factors for cause-specific survival and overall survival. The models demonstrated good discrimination and calibration performance, with C index values of 0.79 (95% confidence interval [CI] = 0.78 to 0.80) for cause-specific survival and 0.74 (95% CI = 0.73 to 0.75) for overall survival. A web-based application for real-time cause-specific survival predictions was created, accessible at https://shuaishao.shinyapps.io/SCCLM/.

CONCLUSION

Prognostic factors for sigmoid colon cancer with liver metastasis patients were identified based on the SEER database, and nomograms for cause-specific survival and overall survival showed good performance. A web-based application was developed to predict sigmoid colon cancer with liver metastasis-specific survival, aiding in survival risk stratification.

Transcriptomic screening of novel targets of sericin in human hepatocellular carcinoma cells

Sericin, a natural protein derived from Bombyx mori, is known to ameliorate liver tissue damage; however, its molecular mechanism remains unclear. Herein, we aimed to identify the possible novel targets of sericin in hepatocytes and related cellular pathways. RNA sequencing analysis indicated that a low dose of sericin resulted in 18 differentially expressed genes (DEGs) being upregulated and 68 DEGs being downregulated, while 61 DEGs were upregulated and 265 DEGs were downregulated in response to a high dose of sericin (FDR ≤ 0.05, fold change > 1.50). Functional analysis revealed that a low dose of sericin regulated pathways associated with the complement and coagulation cascade, metallothionine, and histone demethylate (HDMs), whereas a high dose of sericin was associated with pathways involved in lipid metabolism, mitogen-activated protein kinase (MAPK) signaling and autophagy. The gene network analysis highlighted twelve genes, A2M, SERPINA5, MT2A, MT1G, MT1E, ARID5B, POU2F1, APOB, TRAF6, HSPA8, FGFR1, and OGT, as novel targets of sericin. Network analysis of transcription factor activity revealed that sericin affects NFE2L2, TFAP2C, STAT1, GATA3, CREB1 and CEBPA. Additionally, the protective effects of sericin depended on the counterregulation of APOB, POU2F1, OGT, TRAF6, and HSPA5. These findings suggest that sericin exerts hepatoprotective effects through diverse pathways at different doses, providing novel potential targets for the treatment of liver diseases.

Integrated bioinformatics and network pharmacology identifying the mechanisms and molecular targets of Guipi Decoction for treatment of comorbidity with depression and gastrointestinal disorders

BACKGROUND

Guipi decoction (GPD) not only improves gastrointestinal (GI) function, but also depressive mood. The bioinformatics study aimed to reveal potential crosstalk genes and related pathways between depression and GI disorders. A network pharmacology approach was used to explore the molecular mechanisms and potential targets of GPD for the simultaneous treatment of depression comorbid GI disorders.

METHODS

Differentially expressed genes (DEGs) of major depressive disorder (MDD) were identified based on GSE98793 and GSE19738, and GI disorders-related genes were screened from the GeneCards database. Overlapping genes between MDD and GI disorders were obtained to identify potential crosstalk genes. Protein-protein interaction (PPI) network was constructed to screen for hub genes, signature genes were identified by LASSO regression analysis, and single sample gene set enrichment analysis (ssGSEA) was performed to analyze immune cell infiltration. In addition, based on the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, we screened the active ingredients and targets of GPD and identified the intersection targets of GPD with MDD and GI disorder-related genes, respectively. A "component-target" network was constructed using Cytoscape, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed.

RESULTS

The MDD-corrected dataset contained 2619 DEGs, and a total of 109 crosstalk genes were obtained. 14 hub genes were screened, namely SOX2, CRP, ACE, LEP, SHH, CDH2, CD34, TNF, EGF, BDNF, FN1, IL10, PPARG, and KIT. These genes were identified by LASSO regression analysis for 3 signature genes, including TNF, EGF, and IL10. Gamma.delta.T.cell was significantly positively correlated with all three signature genes, while Central.memory.CD4.T.cell and Central.memory.CD8.T.cell were significantly negatively correlated with EGF and TNF. GPD contained 134 active ingredients and 248 targets, with 41 and 87 relevant targets for the treatment of depression and GI disorders, respectively. EGF, PPARG, IL10 and CRP overlap with the hub genes of the disease.

CONCLUSION

We found that GPD may regulate inflammatory and oxidative stress responses through EGF, PPARG, IL10 and CRP targets, and then be involved in the treatment of both depression and GI disorders.

Expression and function of myelin expression factor 2 in hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) is one of the most common malignant tumours in the world and has a high mortality rate. However, the pathogenesis of HCC remains unclear. This study aimed to investigate the potential biomarkers of HCC.

METHODS

ONCOMINE, HCCDB and THE HUMAN PROTEIN ATLAS were used to identify myelin expression factor 2 (MYEF2) as a potential biomarker for HCC. The Cancer Genome Atlas database was used to further validate and analyse the value of MYEF2. Kaplan-Meier Plotter was used for the prognostic analysis. The COX regression model and Kaplan-Meier method were used to investigate the clinical value of MYEF2 in the prognosis of HCC by reviewing the survival status of patients. Fluorescent quantitative polymerase chain reaction (qPCR) and immunohistochemistry were used to detect the expressions of the MYEF2 mRNA and protein in HCC tissues and cell lines. qPCR and Western blotting were used to validate the efficiency of MYEF2 knockout and overexpression in HCC cells. The invasion and migration abilities regulated by MYEF2 were detected by performing transwell and wound healing assays.

RESULTS

MYEF2 is significantly upregulated in HCC and is mainly located in the nucleus of HCC cells. MYEF2 expression is significantly associated with the tumour stage, histological grade and TNM stage. High MYEF2 expression is an independent prognostic factor for patients with HCC. Functionally, elevated MYEF2 facilitated cell migration and invasion in vitro. In contrast, decreased MYEF2 inhibited cell migration and invasion.

CONCLUSIONS

MYEF2 may be a novel biomarker with potential diagnosis and prognosis values and as a potential therapeutic target for HCC.

Oxaliplatin inhibits colorectal cancer progression by inhibiting CXCL11 secreted by cancer-associated fibroblasts and the CXCR3/PI3K/AKT pathway

PURPOSE

Colorectal cancer (CRC) is a malignant tumor. Oxaliplatin (OXA) can inhibit cancer-associated fibroblasts (CAFs)-induced cancer progression. This study sought to explore the mechanism of OXA in CAFs-induced CRC development.

METHODS

CRC cell lines (Caco-2, SW620), normal fibroblasts (NFs), and CAFs were treated with OXA. NFs and CAFs were cultured. CAFs were treated with/without OXA (0.4 mM), and the supernatant was extracted as the conditioned medium (CM) to culture CRC cells. Cell malignant episodes, E-cadherin and Vimentin levels, CXCL1, CXCL2, CXCL3, CXCL8, and CXCL11 mRNA levels, CXCL11 protein level, and extracellular release were assessed. CAFs were transfected with interfering RNA sh-CXCL11 to silence CXCL11 or transfected with CXCL11 overexpression plasmids and treated with OXA to explore the role of CXCL11 in OXA-mediated CRC cells through CAFs. CXCL11 receptor CXCR3 levels in CRC cells and the PI3K/AKT pathway changes were examined. The xenogeneic tumor was transplanted in nude mice. CXCL11 and CXCR3 levels in tumor tissues, tumor volume, shape, size, weight, and Ki67 positive expressions were assessed.

RESULTS

CRC cell growths and epithelial-mesenchymal transformation were stimulated after culture with CAFs-CM, while OXA averted these trends. CXCL11 mRNA level was elevated most significantly, and its protein and extracellular secretion levels were raised, while OXA diminished the levels. CXCL11 silencing weakened the effects of CAFs-CM on promoting CRC proliferation and malignant episodes and CXCL11 overexpression averted OXA property on inhibiting CAFs-promoted CRC cell growth. CXCR3 and PI3K and AKT1 phosphorylation levels were raised in the CAFs-CM group but diminished by OXA. CXCL11 overexpression in CAFs averted OXA property on inhibiting CAFs-activated CXCR3/PI3K/AKT in CRC cells. OXA also inhibited the progression of xenograft tumors by limiting CAFs-secreted CXCL11.

CONCLUSIONS

OXA repressed CRC progression by inhibiting CAFs-secreted CXCL11 and the CXCR3/PI3K/AKT pathway.

Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker

Background

The liver is the third most common metastatic site for advanced breast cancer (BC), and liver metastases predict poor prognoses. However, the characteristic biomarkers of BC liver metastases and the biological role of secreted protein acidic and rich in cysteine-like 1 (SPARCL1) in BC remain unclear. The present study aimed to identify potential biomarkers for liver metastasis of BC and to investigate the effect of SPARCL1 on BC.

Methods

The publicly available GSE124648 dataset was used to identify differentially expressed genes (DEGs) between BC and liver metastases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to annotate these DEGs and understand the biological functions in which they are involved. A protein-protein interaction (PPI) network was constructed to identify metastasis-related hub genes and further validated in a second independent dataset (GSE58708). Clinicopathological correlation of hub gene expression in patients with BC was determined. Gene set enrichment analysis (GSEA) was performed to explore DEG-related signaling pathways. SPARCL1 expression in BC tissues and cell lines was verified by RT-qPCR. Further in vitro experiments were performed to investigate the biological functions of SPARCL1 in BC cells.

Results

We identified 332 liver metastasis-related DEGs from GSE124648 and 30 hub genes, including SPARCL1, from the PPI network. GO and KEGG enrichment analyses of liver-metastasis-related DEGs revealed several enriched terms associated with the extracellular matrix and pathways in cancer. Clinicopathological correlation analysis of SPARCL1 revealed that its expression in BC was associated with age, TNM stage, estrogen receptor status, progesterone receptor status, histological type, molecular type, and living status of patients. GSEA results suggested that low SPARCL1 expression in BC was related to the cell cycle, DNA replication, oxidative phosphorylation, and homologous recombination. Lower expression levels of SPARCL1 were detected in BC tissues compared to adjacent tissues. The in vitro experiments showed that SPARCL1 knockdown significantly increased the proliferation and migration of BC cells, whereas the proliferation and migration were suppressed after elevating the expression of SPARCL1.

Conclusion

We identified SPARCL1 as a tumor suppressor in BC, which shows potential as a target for BC and liver metastasis therapy and diagnosis.

Inhibition of EZH2 Ameliorates Sepsis Acute Lung Injury (SALI) and Non-Small-Cell Lung Cancer (NSCLC) Proliferation through the PD-L1 Pathway

(1) Background: Both sepsis acute lung injury (SALI) and non-small-cell lung cancer (NSCLC) are life-threatening diseases caused by immune response disorders and inflammation, but the underlining linking mechanisms are still not clear. This study aimed to detect the shared gene signature and potential molecular process between SALI and NSCLC. (2) Methods: RNA sequences and patient information on sepsis and NSCLC were acquired from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to build a co-expression network associated with sepsis and NSCLC. Protein-protein interaction (PPI) analysis of shared genes was intuitively performed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The involvement of EZH2 in the tumor immune microenvironment (TIME) and sepsis immune microenvironment (IME) was assessed by R software. Western blot, flow cytometry, and other in vitro assays were performed to further confirm the function and mechanism of EZH2 in NSCLC and SALI. (3) Results: WGCNA recognized three major modules for sepsis and two major modules for NSCLC, and there were seven shared genes identified for the two diseases. Additionally, the hub gene EZH2 was screened out. It was shown that EZH2 was closely related to the IME in the two diseases. In the validation assay, our data showed that EZH2 was expressed at a higher level in peripheral blood mononuclear cells (PBMCs) of septic patients than those of healthy donors (HDs), and EZH2 was also expressed at a higher level in lipopolysaccharide (LPS)-induced PBMCs and non-small cell lung cancer (A549) cells. EZH2 inhibitor (GSK343) downregulated the proliferation ability of A549 cells in a concentration-dependent manner, parallel with the decreased expression level of PD-L1. Similarly, GSK343 inhibited PD-L1 protein expression and downregulated the level of proinflammatory factors in LPS-induced PBMCs. In the co-culture system of PBMCs and human type II alveolar epithelial cells (ATIIs), the addition of GSK343 to PBMCs significantly downregulated the apoptosis of LPS-induced ATIIs. (4) Conclusions: This study illustrated that EZH2 inhibition could ameliorate A549 cell proliferation and LPS-induced ATII apoptosis in parallel with downregulation of PD-L1 protein expression, which provided new insights into molecular signaling networks involved in the pathogenetics of SALI and NSCLC.

Screening for aberrantly methylated and differentially expressed genes in nonalcoholic fatty liver disease of hepatocellular carcinoma patients with cirrhosis

Background

Nonalcoholic fatty liver disease (NAFLD) as the leading chronic liver disease worldwide causes hepatic fibrosis, cirrhosis and hepatocellular carcinoma (HCC). The aim of this study was to find potential aberrantly methylated and differentially expressed genes in NAFLD of HCC patients with cirrhosis.

Methods

DNA methylation data, mRNA expression data, and the corresponding clinical information of HCC were downloaded from the Cancer Genome Atlas (TCGA, tissue sample) database. HCC patients with cirrhosis were divided into two groups according to the presence of NAFLD. The differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were obtained.

Results

By overlapping 79 up-regulated genes and 1020 hypomethylated genes, we obtained 5 hypomethylated-highly expressed genes (Hypo­HGs). By overlapping 365 down-regulated genes and 481 hypermethylated genes, we identified 13 hypermethylated-lowly expressed genes (Hyper-LGs). Survival analysis of these 18 MDEGs indicated that the expression of DGKK and HOXD9 was significantly correlated with the overall survival time of NAFLD patients.

Conclusions

We identified several candidate genes whose expressions were regulated by DNA methylation of NAFLD of HCC with cirrhosis, which may provide a new field in understanding the clinical pathological mechanism of NAFLD of HCC with cirrhosis.

SERPINA5 promotes tumour cell proliferation by modulating the PI3K/AKT/mTOR signalling pathway in gastric cancer

SERPINA5 belongs to the serine protease inhibitor superfamily and has been reported to be lowly expressed in a variety of malignancies. However, few report of SERPINA5 in gastric cancer has been found. The purpose of this study was to determine the role of SERPINA5 in GC and to investigate potential tumorigenic mechanisms. We performed qPCR to determine the level of SERPINA5 expression in GC. We used public databases to evaluate whether SERPINA5 could be utilized to predict overall survival and disease‐free survival in GC patients. We also knocked down the expression of SERPINA5 and evaluated its effect on cell proliferation and migration. Furthermore, we explored the signal pathways and regulatory mechanisms related to SERPINA5 functions. According to our findings, SERPINA5 was shown to exhibit high expression in GC. Notably, SERPINA5 was prognostic in GC with high expression being unfavourable. SERPINA5 was further observed to promote GC tumorigenesis by modulating GC cell proliferation ability. Mechanically, SERPINA5 could inhibit CBL to regulate the PI3K/AKT/mTOR signalling pathway, thereby promoting GC carcinogenesis progression. These results highlight the important role of SERPINA5 in GC cell proliferation and suggest that SERPINA5 could be a novel target for GC treatment and a predictor for GC prognosis.

MiR-320d Inhibits Progression of EGFR-Positive Colorectal Cancer by Targeting TUSC3

Background: The mechanism of miR-320d in EGFR-positive colorectal cancer (CRC) has not been fully elucidated. The aim of the present study was to explore the molecular mechanism of miR-320d in CRC.

Methods: The miRNA microarray analysis was conducted to identify differential expressed miRNAs. The expression of miR-320d was validated using quantitative real-time PCR. EGFR-positive CRC cells were transfected with miR-320d mimic and inhibitor, after which cell proliferation, migration, and invasion were assayed. The relationship between miR-320d and TUSC3 was confirmed using bioinformatics and dual-luciferase reporter gene assays. Proteins involved in signaling pathways and the epithelial–mesenchymal transition were detected with Western blot.

Results: We found that the miR-320d expression is associated with tumor size and distant metastasis in colorectal cancer. Overexpression of miR-320d in EGFR-positive HCT-116 and SW480 cells decreased not only the proliferation ability but also the invasion and migration ability. In addition, miR-320d had the ability to inhibit epithelial-to-mesenchymal transition. Luciferase assays revealed that miR-320d directly targets the 3′-UTR of TUSC3. TUSC3 was downregulated by miR-320d at both the protein and mRNA levels in EGFR-positive CRC cell lines.

Conclusion: Generally, our results demonstrated that miR-320d could inhibit the malignant phenotype of EGFR-positive CRC through targeting TUSC3. The miR-320d might be a potential therapeutic target for EGFR-positive CRC.