Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.     

Screening of immunosuppressive cells from colorectal adenocarcinoma and identification of prognostic markers

Background: Colorectal cancer (CRC) is the most common type of gastrointestinal malignant tumour. Colorectal adenocarcinoma (COAD) – the most common type of CRC – is particularly dangerous. The role of the immune system in the development of tumour-associated inflammation and cancer has received increasing attention recently.Methods: In the present study, we compiled the expression profiles of 262 patients with complete follow-up data from The Cancer Genome Atlas (TCGA) database as an experimental group and selected 65 samples from the Gene Expression Omnibus (GEO) dataset (of which 46 samples were with M0) as a verification group. First, we screened the immune T helper 17 (Th17) cells related to the prognosis of COAD. Subsequently, we identified Th17 cells-related hub genes by utilising Weighted Gene Co-expression Network Analysis (WGCNA) and Least Absolute Shrinkage and Selector Operation (LASSO) regression analysis. Six genes associated with the prognosis in patients with COAD were identified, including: KRT23, ULBP2, ASRGL1, SERPINA1, SCIN, and SLC28A2. We constructed a clinical prediction model and analysed its predictive power.Results: The identified hub genes are involved in developing many diseases and closely linked to digestive disorders. Our results suggested that the hub genes could influence the prognosis of COAD by regulating Th17 cells’ infiltration.Conclusions: These newly discovered hub genes contribute to clarifying the mechanisms of COAD development and metastasis. Given that they promote COAD development, they may become new therapeutic targets and biomarkers of COAD.     

不同神经功能缺损程度急性脑梗死患者Th17/Treg比值、CXCL9、CXCL12、CXCL16的变化及对短期预后的影响

摘要 目的：探讨不同神经功能缺损程度急性脑梗死（ACI）患者辅助性T细胞17（Th17）/调节性T细胞（Treg）比值、趋化因子（CXCL）9、CXCL12、CXCL16的变化及对短期预后的影响。方法：选取2020年4月20日至2021年1月10日期间我院收治的ACI患者96例作为研究对象,根据入院时美国国立卫生研究院卒中量表（NIHSS）评分将患者分为重度组（25例）、中度组（32例）和轻度组（39例）。根据改良Rankin量表（mRS）评分分为预后不良组（31例）和预后良好组（65例）。对比轻度组、中度组和重度组的外周血Th17/Treg比值、血清CXCL9、CXCL12、CXCL16水平;对比预后良好组和预后不良组的外周血Th17/Treg比值、血清CXCL9、CXCL12、CXCL16水平;采用单因素和多因素Logistic回归分析ACI患者预后不良的影响因素。结果：重度组、中度组的外周血Th17/Treg比值、血清CXCL9、CXCL12、CXCL16水平高于轻度组,且重度组高于中度组（P<0.05）。预后良好组的外周血Th17/Treg比值、血清CXCL9、CXCL12、CXCL16水平低于预后不良组（P<0.05）。单因素分析可知,预后不良与性别、年龄、体质量指数（BMI）、吸烟史、饮酒史、冠心病史、疾病梗死部位、治疗方式、发病至入院时间、同型半胱氨酸（Hcy）、总胆固醇（TC）、甘油三酯（TG）、高密度脂蛋白胆固醇（HDL-C）、白细胞计数（WBC）无关（P>0.05）。而与糖尿病史、高血压史、低密度脂蛋白胆固醇（LDL-C）、颈动脉内膜中层厚度（IMT）、NIHSS评分有关（P<0.05）。糖尿病史、高血压史、LDL-C偏高、颈动脉IMT偏厚、NIHSS评分偏高是ACI患者预后的危险因素,而外周血Th17/Treg比值及血清CXCL9、CXCL12、CXCL16水平下降是ACI患者预后的保护因素（P<0.05）。结论：外周血Th17/Treg比值、CXCL9、CXCL12、CXCL16随着ACI病情进展呈升高现象,是影响ACI患者预后的重要影响因素,除此之外,糖尿病史、高血压史、LDL-C偏高、颈动脉IMT偏厚、NIHSS评分偏高也是预后不良的危险因素。     

Identification of functional lncRNAs in gastric cancer by integrative analysis of GEO and TCGA data

Gastric cancer (GC) is a prevalent malignant cancer of digestive system, identification of novel diagnostic and prognostic biomarkers for GC is urgently demanded. The aim of this study was to determine potential long noncoding RNAs (lncRNAs) associated with the pathogenesis and prognosis of GC. Raw noncoding RNA microarray data (GSE53137, GSE70880, and GSE99417) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by The Cancer Genome Atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway, survival analysis were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. In total of 246 integrated differential genes including 15 lncRNAs and 241 messenger RNAs (mRNAs) were obtained after intersections of differential genes between GEO and TCGA database. ceRNA network comprised of three lncRNAs (UCA1, HOTTIP, and HMGA1P4), 26 microRNAs (miRNAs) and 72 mRNAs. Functional analysis revealed that three lncRNAs were mainly dominated in cell cycle and cellular senescence. Survival analysis showed that HMGA1P4 was statistically related to the overall survival rate. For the first time, we identified that HMGA1P4, a target of miR-301b/miR-508, is involved in cell cycle and senescence process by regulating CCNA2 in GC. Finally, the expression levels of three lncRNAs were validated to be upregulated in GC tissues. Thus, three lncRNAs including UCA1, HOTTIP, and HMGA1P4 may contribute to GC development and their potential functions might be associated with the prognosis of GC.     

幽门螺杆菌感染与胃癌相关基因的筛选及预后分析

目的 通过分析幽门螺杆菌感染胃黏膜组织和胃癌细胞系后的差异基因变化,并在癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库和肿瘤基因芯片(Oncomine)数据库进行验证,探究幽门螺杆菌导致胃癌发生、发展的分子机制。 方法 分析基因表达汇编(Gene Expression Omnibus,GEO)数据库幽门螺杆菌感染相关芯片集GSE5081与GSE70394,绘制维恩图查找幽门螺杆菌感染后共同上调的差异基因。对共同上调的差异基因进行功能富集分析。通过TCGA和Oncomine数据库验证差异基因在胃癌中的表达。利用Kaplan Meier Plotter数据库和GEPIA数据库分析差异基因表达高低与胃癌患者预后是否存在相关性。 结果 通过差异基因筛选和维恩分析,两个芯片集共有21个共同上调差异基因。GO分析发现共同上调差异基因主要富集在对细菌来源分子的反应、趋化因子CXCR受体结合、中性粒细胞趋化作用等相关的基因功能上;KEGG通路主要富集在癌症通路、TNF信号通路、趋化因子信号通路等。STRING以及PPI数据库分析发现21个基因中PRDM1、IL10、NRP1、BIRC3、GNG13、CXCL1、CXCL2、CXCL3、CXCL8基因存在有网络关系,属于关键枢纽基因。通过TCGA和Oncomine数据库筛选及验证,发现在胃癌组织中NRP1、CXCL1、CXCL8 基因明显上调,结果差异有统计学意义(TCGA数据库中,三者P值均小于0.05,Oncomine数据库中,NRP1:t=4.607,P结论 不同的数据库均显示NRP1、CXCL1、CXCL8三个基因与幽门螺杆菌感染相关,同时在胃癌中高表达,并且NRP1的高表达与胃癌的不良预后相关,这些结果为进一步探究幽门螺杆菌导致胃癌发生、发展的分子机制提供了重要基础。     

Commentary on: Screening of immunosuppressive cells from colorectal adenocarcinoma and identification of prognostic markers

Colorectal adenocarcinoma (COAD) is one subtype of colorectal carcinoma (CRC), whose development is associated with genetics, inappropriate immune response, and environmental factors. Although significant advances have been made in the treatment of COAD, the mortality rate remains high. It is a pressing need to explore novel therapeutic targets of COAD. Available evidence indicated that immune cell infiltration was correlated with cancer prognosis. To reveal the roles of immune cells in the COAD prognosis, a study published in Bioscience Reports by Li et al. (Bioscience Reports (2021) 41, https://doi.org/10.1042/BSR20203496) analyzed data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. It demonstrated a beneficial effect of Th17 cells in COAD prognosis. In addition, six hub genes (KRT23, ULBP2, ASRGL1, SERPINA1, SCIN, and SLC28A2) were identified to correlate with Th17 cells and COAD prognosis, suggesting one new therapy strategy and some predictive biomarkers of COAD. These findings reported by Li et al. may pave one way to explore the molecular mechanism of COAD further.     

The expression of LRRN4 was correlated with the progression and prognosis of colon adenocarcinoma (COAD) patients

Our present study aims to investigate the value of LRRN4 in the progression and prognosis of COAD patients. All COAD and adjacent sample data was downloaded from TCGA database. Survival analysis was performed according to Kaplan-Meier method. The real-time quantitative PCR and immunohistochemistry analysis were conducted for validation in cell lines and tissues. The GSEA was conducted to find functional KEGG pathways. Multivariate Cox regression proportional hazard mode was used to determine whether LRRN4 expression was an independent prognostic factor. The LRRN4 expression in COAD samples were significantly higher than that in adjacent samples, which was consistent with our experiments in cell lines and tissues. Along with the increase of TNM Stage, LRRN4 expression had an increasing tendency. The COAD patients with high LRRN4 expression showed undesirable prognoses. Additionally, the TGF-β signaling pathway, WNT signaling pathway and other 25 pathways were significantly activated in the high LRRN4 expression group. In conclusion, high LRRN4 expression was closely related to the onset of COAD and it was a poor prognostic factor for COAD patients.Keywords: Colon adenocarcinoma, LRRN4, prognosis, biomarker     

The diagnosis and prognosis values of WNT mRNA expression in colon adenocarcinoma

WNT family genes have participated in the progression and development of many cancers, however, the association between colon adenocarcinoma (COAD) and WNTs have been rarely reported. This study investigated the significance of WNT genes expression in COAD from the standpoint of diagnosis and prognosis. The RNA-sequencing dataset of COAD was downloaded from The Cancer Genome Atlas and University of California, Santa Cruz Xena browser. The biology functions of WNT genes were investigated by biological analysis. Biological analysis of WNT family genes indicated that WNT genes were noticeably enriched in the complex process of WNT signaling pathway. The Pearson correlation analysis suggested WNT1 and WNT9B had a strong correlation. And receiver operating characteristic curves suggested that most of the genes could serve as a significant diagnostic makers in COAD (P < .05), especially WNT2 and WNT7B had high diagnostic values that the area under curve were 0.997 (95% confidence interval [0.994-1.000]) and 0.961 (95%CI [0.939-0.983]), respectively. And our multivariate survival analysis suggested the downregulated of WNT10B (P < .05) showed a favor prognosis in COAD overall survival. And the risk score model predicted that the upregulated expression of WNT10B might increase the risk of death. The very study we had conducted suggested that WNT genes had a certain connection with the diagnosis and prognosis of COAD. The messenger RNA expression of WNT2 and WNT7B might become potentially diagnostic biomarkers, and WNT10B might serve as an independent prognosis indicator for COAD.     

Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

Background

Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future.

Methods

Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms.

Results

After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively.

Conclusions

All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical.

     

Identification and validation of key genes associated with non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the main causes of death induced by cancer globally. However, the molecular aberrations in NSCLC patients remain unclearly. In the present study, four messenger RNA microarray datasets (GSE18842, GSE40275, GSE43458, and GSE102287) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between NSCLC tissues and adjacent lung tissues were obtained from GEO2R and the overlapping DEGs were identified. Moreover, functional and pathway enrichment were performed by Funrich, while the protein–protein interaction (PPI) network construction were obtained from STRING and hub genes were visualized and identified by Cytoscape software. Furthermore, validation, overall survival (OS) and tumor staging analysis of selected hub genes were performed by GEPIA. A total of 367 DEGs (95 upregulated and 272 downregulated) were obtained through gene integration analysis. The PPI network consisted of 94 nodes and 1036 edges in the upregulated DEGs and 272 nodes and 464 edges in the downregulated DEGs, respectively. The PPI network identified 46 upregulated and 27 downregulated hub genes among the DEGs, and six (such as CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M) of that have not been identified to be associated with NSCLC so far. Moreover, the expression differences of the mentioned hub genes were consistent with that in lung adenocarcinoma and lung squamous cell carcinoma in the TCGA database. Further analysis showed that all the six hub genes were associated with tumor staging except MYH11, while only the upregulated DEG CENPE was associated with the worse OS of patients with NSCLC. In conclusion, the current study showed that CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M might be the key genes contributed to tumorigenesis or tumor progression in NSCLC, further functional study is needed to explore the involved mechanisms.     

Identification of a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis

Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R² = −0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.     

Retracted: Role of CXCL12–CXCR4 axis in ovarian cancer metastasis and CXCL12–CXCR4 blockade with AMD3100 suppresses tumor cell migration and invasion in vitro

Ovarian cancer (OC) is a lethal gynecologic tumor, which brings its mortality to the head. CXCL12 and its receptor chemokine receptor 4 ( CXCR4) have been found to be highly expressed in OC and contribute to the disease progression by affecting tumor cell proliferation and invasion. Here, in this study, we aim to explore whether the blockade of CXCL12–CXCR4 axis with AMD3100 (a selective CXCR4 antagonist) has effects on the progression of OC. On the basis of the gene expression omnibus database of OC gene expression chips, the OC differentially expressed genes were screened by microarray analysis. OC (nonmetastatic and metastatic) and normal ovarian tissues were collected to determine the expressions of CXCL12 and CXCR4. A series of AMD3100, shRNA against CXCR4, and pCNS-CXCR4 were introduced to treat CAOV3 cells with the highest CXCR4 was assessed. Cell viability, apoptosis, migration, and invasion were all evaluated. The microarray analysis screened out the differential expression of CXCL12–CXCR4 in OC. CXCL12 and CXCR4 expressions were increased in OC tissues, particularly in the metastatic OC tissues. Downregulation of CXCR4 by AMD3100 or shRNA was observed to have a critical role in inhibiting cell proliferation, migration, and invasion of the CAOV3 OC cell line while promoting cell apoptosis. Overexpressed CXCR4 brought significantly promoting effects on the proliferation and invasiveness of OC cells. These results reinforce that the blockade of CXCL12–CXCR4 axis with AMD3100 inhibits the growth of OC cells. The antitumor role of the inhibition of CXCL12–CXCR4 axis offers a preclinical validation of CXCL12–CXCR4 axis as a therapeutic target in OC.     

DNA methylation biomarkers for head and neck squamous cell carcinoma

DNA methylation plays an important role in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). The current study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) by a comprehensive bioinformatics analysis. In addition, we screened for DEGs affected by DNA methylation modification and further investigated their prognostic values for HNSCC. We included microarray data of DNA methylation (GSE25093 and GSE33202) and gene expression (GSE23036 and GSE58911) from Gene Expression Omnibus. Aberrantly methylated-DEGs were analyzed with R software. The Cancer Genome Atlas (TCGA) RNA sequencing and DNA methylation (Illumina HumanMethylation450) databases were utilized for validation. In total, 27 aberrantly methylated genes accompanied by altered expression were identified. After confirmation by The Cancer Genome Atlas (TCGA) database, 2 hypermethylated-low-expression genes (FAM135B and ZNF610) and 2 hypomethylated-high-expression genes (HOXA9 and DCC) were identified. A receiver operating characteristic (ROC) curve confirmed the diagnostic value of these four methylated genes for HNSCC. Multivariate Cox proportional hazards analysis showed that FAM135B methylation was a favorable independent prognostic biomarker for overall survival of HNSCC patients.     

Hypermethylated and downregulated MEIS2 are involved in stemness properties and oxaliplatin-based chemotherapy resistance of colorectal cancer

The resistance against oxaliplatin (L-OHP) based regimens remains a major obstacle for its efficient usage in treating metastatic colorectal cancer (mCRC). In this study, we performed weighted gene coexpression network analysis (WGCNA) to systematically screen the relevant hub genes for L-OHP resistance using the raw microarray data of 30 consecutive mCRC samples from our earlier study (GSE69657). The results were further confirmed through datasets from Gene Expression Omnibus (GEO). From L-OHP resistance module, nine genes in both the coexpression and protein–protein interaction networks were chosen as hub genes. Among these genes, Meis Homeobox 2 (MEIS2) had the highest correlation with L-OHP resistance (r = −0.443) and was deregulated in L-OHP resistant tissues compared with L-OHP sensitive tissues in both our own dataset and GSE104645 testing dataset. The receiver operating characteristic curve validated that MEIS2 had a good ability in predicting L-OHP response in both our own dataset (area under the curve [AUC] = 0.802) and GSE104645 dataset (AUC = 0.746). Then, the down expression of MEIS2 was observed in CRC tissue compared with normal tissue in 12 GEO-sourced datasets and The Cancer Genome Atlas (TCGA) and was correlated with poor event-free survival. Furthermore, analyzing methylation data from TCGA showed that MEIS2 had increased promoter hypermethylation. In addition, MEIS2 expression was significantly decreased in CRC stem cells compared with nonstem cells in two GEO datasets (GSE14773 and GSE24747). Further methylation analysis from GSE104271 demonstrated that CRC stem cells had higher MEIS2 promoter methylation levels in cg00366722 and cg00610348 sites. Gene set enrichment analysis showed that MEIS2 might be involved in the Wnt/β-catenin pathway. In the overall view, MEIS2 had increased promoter hypermethylation and was downregulated in poor L-OHP response mCRC tissues. MEIS2 might be involved in the Wnt/β-catenin pathway to maintain CRC stemness, which leads to L-OHP resistance.