Identification of stage-specific biomarkers in lung adenocarcinoma based on RNA-seq data

A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer

Approximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC.

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

Understanding of molecular basis of histological graded horn cancer by transcriptome profiling

Horn cancer is most devastating and prominent cancer in Indian zebu cattle that affects socio-economic condition of small-scale farmers who depends on their cattle for farm work. Development in the field for genomics through next generation sequencing and bioinformatics advancement have helped to identify genes which have a role in horn cancer development. Histopathological examination of cancerous tissues of horn revealed myxomatous changes, well, moderate and poorly differentiated squamous cell carcinoma. Differential gene expression analysis showed 40, 11, 66 and 29 upregulated genes and 10, 14, 08 and 07 down-regulated genes in myxomatous, well, moderate and poorly differentiated squamous cell carcinoma as compared to normal. Significant differentially expressed genes are related to cell development, cell proliferation, cell-cell communication, cell signaling and angiogenesis which are linked to Akt pathway, mTOR pathway and Wnt pathway. Activity of these genes and related pathways have already been established about their role in development of cancer. Among the candidate genes; keratin family, keratin family related gene, chemokine signaling and cytokines signaling associated genes could be a prominent target for the development of stage specific prognosis marker after further detailed study at large sample population level. CSTA, PTN, SPP1 genes have upregulation in all stages of cancer and they have enrolled as biomarkers for horn cancer.

Evolutionary Analysis of TCGA Data Using Over- and Under- Mutated Genes Identify Key Molecular Pathways and Cellular Functions in Lung Cancer Subtypes

We identify critical conserved and mutated genes through a theoretical model linking a gene’s fitness contribution to its observed mutational frequency in a clinical cohort. “Passenger” gene mutations do not alter fitness and have mutational frequencies determined by gene size and the mutation rate. Driver mutations, which increase fitness (and proliferation), are observed more frequently than expected. Non-synonymous mutations in essential genes reduce fitness and are eliminated by natural selection resulting in lower prevalence than expected. We apply this “evolutionary triage” principle to TCGA data from EGFR-mutant, KRAS-mutant, and NEK (non-EGFR/KRAS) lung adenocarcinomas. We find frequent overlap of evolutionarily selected non-synonymous gene mutations among the subtypes suggesting enrichment for adaptations to common local tissue selection forces. Overlap of conserved genes in the LUAD subtypes is rare suggesting negative evolutionary selection is strongly dependent on initiating mutational events during carcinogenesis. Highly expressed genes are more likely to be conserved and significant changes in expression (>20% increased/decreased) are common in genes with evolutionarily selected mutations but not in conserved genes. EGFR-mut cancers have fewer average mutations (89) than KRAS-mut (228) and NEK (313). Subtype-specific variation in conserved and mutated genes identify critical molecular components in cell signaling, extracellular matrix remodeling, and membrane transporters. These findings demonstrate subtype-specific patterns of co-adaptations between the defining driver mutation and somatically conserved genes as well as novel insights into epigenetic versus genetic contributions to cancer evolution.

The minor allele of rs17427875 in long non-coding RNA-HOXA11-AS influences the prognosis of subarachnoid hemorrhage (SAH) via modulating miR-15a and STAT3 expression

Background: HOAX11-AS was reported to promote the progression of liver cancer via the signaling pathway of miR-15a-3p/STAT3. In this study, we investigated the effect of rs17427875 on the prognosis of subarachnoid hemorrhage (SAH) and its underlying molecular mechanisms.

Methods: 158 SAH patients were recruited and grouped according to their genotypes rs17427875. Peripheral blood and cerebrospinal fluid (CSF) samples were collected for subsequent analysis. Quantitative real-time PCR, luciferase assays, Western blot and ELISA were performed to analyze the correlations between the expression of lncRNA-HOXA11-AS, miR-15a, TNF-α and NF-κB.

Results: The survival rate was remarkably higher in SAH patients carrying the AA genotype of rs17427875 when compared with those carrying the AT genotype. The expression of miR-15a was significantly repressed in the peripheral blood and CSF of SAH patients carrying the AT allele when compared with that in patients carrying the AA allele. MiR-15a showed a remarkable efficacy in inhibiting the luciferase activity of wild type lncRNA-HOXA11-AS and STAT3 in THP-1 cells. P-HOXA11-AS-T showed a stronger ability to suppress the expression of miR-15a and activate the expression of STAT3, TNF-α and NF-κB in THP-1 cells when compared with P-HOXA11-AS-A.

Conclusions: The findings demonstrated that the presence of the minor allele of rs17427875 in lncRNA-HOXA11-AS could increase the expression level of lncRNA-HOXA11-AS, thus elevating the expression level of STAT3 via down-regulating miR-15a, and increased STAT3 expression could aggravate inflammation to cause poor prognosis of SAH. Therefore, the rs17427875 polymorphism can be used as a potential biomarker for the prognosis of SAH.

Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers Using Machine Learning Techniques Applied to Lung Cancer

Background:

Nowadays, gene expression analysis is one of the most promising pillars for understanding and uncovering the mechanisms underlying the development and spread of cancer. In this sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained from older technologies, such as Microarray, which could still be useful to extract relevant knowledge.

Methods:

Throughout this research, a complete machine learning methodology to cross-evaluate the compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented. In order to show a real application of the designed pipeline, a lung cancer case study is addressed by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic datasets considered for our study have been obtained from the public repositories NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples belonging to these cancer subtypes have been developed.

Results:

The predictive models built using one technology are capable of discerning samples from a different technology. The classification results are evaluated in terms of accuracy, F1-score and ROC curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.

Conclusion:

Our method has the capability of finding strong gene signatures which are also independent of the transcriptomic technology used to develop the analysis. In addition, our article highlights the potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies, increasing the statistical significance of the results.

Abnormal expression profile of plasma-derived exosomal microRNAs in patients with treatment-resistant depression

Whether microRNAs (miRNAs) from plasma exosomes might be dysregulated in patients with depression, especially treatment-resistant depression (TRD), remains unclear, based on study of which novel biomarkers and therapeutic targets could be discovered. To this end, a small sample study was performed by isolation of plasma exosomes from patients with TRD diagnosed by Hamilton scale. In this study, 4 peripheral plasma samples from patients with TRD and 4 healthy controls were collected for extraction of plasma exosomes. Exosomal miRNAs were analyzed by miRNA sequencing, followed by image collection, expression difference analysis, target gene GO enrichment analysis, and KEGG pathway enrichment analysis. Compared with the healthy controls, 2 miRNAs in the plasma exosomes of patients with TRD showed significant differences in expression, among which has-miR-335-5p were significantly upregulated and has-miR-1292-3p were significantly downregulated. Go and KEGG analysis showed that dysregulated miRNAs affect postsynaptic density and axonogenesis as well as the signaling pathway of axon formation and cell growths. The identification of these miRNAs and their target genes may provide novel biomarkers for improving diagnosis accuracy and treatment effectiveness of TRD.

Novel insights for lncRNA MAGI2-AS3 in solid tumors

Long non-coding RNAs (lncRNAs) refer to elements of genomic transcription with more than 200 nucleotides that are not translated into proteins, but have crucial roles in cancer progression. MAGI2-AS3, a novel lncRNA, has been reported to be aberrantly expressed in many solid tumors. Increasingly, studies have demonstrated that MAGI2-AS3 expression is significantly correlated with patient clinical characteristics, and that MAGI2-AS3 can regulate multiple biological processes through target-gene regulation. Furthermore, MAGI2-AS3 may serve as both a diagnostic biomarker and as a promising therapeutic target against solid tumors. In this review, we summarize the current knowledge regarding the biological functions and related molecular mechanisms of MAGI2-AS3 in solid-tumor progression. We conclude that understanding MAGI2-AS3 properties may provide new insights into the diagnoses and treatments of solid tumors.

The long non-coding RNA HOXA11-AS promotes epithelial mesenchymal transition by sponging miR-149-3p in Colorectal Cancer

Background: Metastasis is the primary cause of death in colorectal cancer (CRC); the underlying mechanisms remain partly unknown. In this study, we aim to investigate the value of HOXA11-AS in survival evaluation and the potential role of HOXA11-AS/miR-149-3p axis in the CRC metastasis.

Methods: The expressions of HOXA11-AS, both in obtained CRC samples and adjacent noncancerous tissues, were analyzed in survival evaluation. Competing endogenous RNAs (CeRNAs) Analysis were employed to reveal the potential relationship between HOXA11-AS and miR-149-3p. It was further confirmed by Quantitative real-time polymerase chain reaction (qRT-PCR) and Dual-luciferase reporter assay. Migration and invasion assay were used to verify the potential role of HOXA11-AS and miR-149-3p in the regulation of CRC metastasis. The potential pathway was explored by Western blot analysis.

Results: The expression of HOXA11-AS in the CRC tissue is significantly higher than the expression in adjacent noncancerous tissue (p<0.0001). High expressions of HOXA11-AS were noticeably correlated with clinicopathologic characteristics including advanced clinical stage (p=0.021), larger tumor size (p<0.001) and frequent tumor recurrence (p=0.001). The overall survival in HOXA11-AS-High group was significantly shorter than the HOXA11-AS-Low group (p<0.001). Advanced clinical stage, tumor size and high expression of HOXA11-AS were showed as independent prognostic prediction factors for the 5-year tumor relapse of CRC patients (p<0.001). HOXA11-AS acts as a potential molecular sponge for miR-149-3p, in the promotion of CRC metastasis. In the miR-149-3p mimic-treated group, the expression of E-cadherin was increased, whereas the expression of N-cadherin, Snail, Slug, TGF-β1, Wnt2b, Twist and C/EBPβ was decreased.

Conclusion: This study demonstrates that high expression of HOXA11-AS is correlated with CRC progression and poor prognosis and may promote metastasis via EMT by modulating miR-149-3p.

The lncRNA HOXA11-AS regulates Rab3D expression by sponging miR-125a-5p promoting metastasis of osteosarcoma

Objective: Many studies have shown that long non-coding RNAs (lncRNAs) are closely related to various cancers. This study aims to explore the roles of lncRNA HOXA11-AS in the development and progression of osteosarcoma (OS).

Methods: The expression levels of HOXA11-AS and miR-125a-5p in tumor tissues and the adjacent tissues were detected by RT-PCR method. The proliferation, migration and invasion of MG-63 and KHOS cells were determined.

Results: It was found that HOXA11-AS expression levels in OS tissues and OS cell lines were higher than those in OS adjacent tissues and normal human osteoblast cell lines. The higher expression level of HOXA11-AS was positively correlated with more severe clinical stage, distant metastasis and poor prognosis of OS. Inhibition of HOXA11-AS expression could reduce metastasis and invasion of OS cell lines. In addition, HOXA11-AS was found to be an endogenous inhibitor of miR-125a-5p, it down regulated the expression level of miR-125a-5p, and this process could promote the expression of Rab3D, the target gene of miR-125a-5p.

Conclusion: Our study elucidated the role of a new HOXA11-AS/miR-125a-5p/Rab3D regulatory pathway in promoting OS metastasis.

A group of long noncoding RNAs identified by data mining can predict the prognosis of lung adenocarcinoma

Long noncoding RNAs (lncRNA) are reported to be potential cancer biomarkers. This study aims to find new lncRNA biomarker relevant to lung adenocarcinoma. Gene expression profile and clinical data of lung adenocarcinoma and lung squamous cell carcinoma patients were downloaded from the UCSC Xena database. These data were analyzed to identify potential lncRNA prognostic biomarkers, and the candidate lncRNAs were analyzed and verified with association analysis, meta-analysis, survival analysis, gene ontology analysis, gene set enrichment analysis, and other statistical methods. A group of 5 lncRNAs was identified from the 1965 differentially expressed (fold-change >2) genes. Four of these 5 lncRNAs were expressed at a lower level in lung adenocarcinoma tissues and the other one at a higher level (P < .0001). A risk score model was constructed using a linear combination of the expression levels of these lncRNAs. High-risk patients showed poorer overall survival (hazard ratio [HR] = 2.14; 95% confidence interval [CI], 1.67-3.06, P < .0001), disease-free survival (HR = 1.84; 95% CI, 1.26-2.35, P = .0007), and recurrence-free survival (HR = 1.51; 95% CI, 1.02-2.40, P = .04). The 5-fold cross-validation and subsequent meta-analysis further verified that patients in the low-risk group had better survival (95% CI, 0.74-1.79, Z = 4.72, P < .00001). Furthermore, both univariate and multivariate Cox regression analyses revealed that the prognostic value of these 5 lncRNAs was independent of other clinical prognostic factors. Further analysis indicated that these 5 lncRNAs might be associated with tumor metastasis. Taken together, our study suggests new prognostic lncRNA biomarkers for lung adenocarcinoma.

LncRNA Expression Signature in Prediction of the Prognosis of Lung Adenocarcinoma

OBJECTIVE

To determine if lncRNA expression can be used for the prognoses of patients diagnosed with lung adenocarcinoma (LUAD) patients.

METHODS

The Cancer Genome Atlas database was used to identify 409 LUAD patients for whom there were both, gene expression data and relevant clinical information available. LncRNAs were then selected from the expression data through record linkage between the National Center for Biotechnology Information (NCBI) and Ensemble databases. lncRNAs with significantly different expression levels between normal and tumor tissues were screened, and those whose levels correlated with a positive LUAD prognosis were identified. Based on the expression of the selected lncRNAs, an unsupervised learning method was used to cluster these patients into two groups, and survival analyses were performed to assess the overall survival (OS) between the two groups. Receiver operating characteristic curves were used to calculate the specificity and sensitivity of the models based on the presence of these lncRNAs, and the model was tested with another dataset from the Gene Expression Omnibus.

RESULTS

A total of 151 lncRNAs were found to be differentially expressed between tumor and normal tissues (permutation p-values <0.05) based on the Cancer Genome Atlas dataset, and 20 lncRNAs were associated with OS. Two lncRNAs (DKFZP434 L187 and LOC285548) were correlated with LUAD. All patients with high expression of these two lncRNAs from the two datasets exhibited poor OS compared with those with low expression (p < 0.05).

CONCLUSIONS

The expression of DKFZP434 L187 and LOC285548 may have prognostic value for the OS of LUAD patients.

[Omics technologies in diagnostics of lung adenocarcinoma]

To date lung adenocarcinoma (LAC) is the most common type of lung cancer. Numerous studies on LAC biology resulted in identification of crucial mutations in protooncogenes and activating neoplastic transformation pathways. Therapeutic approaches that significantly increase the survival rate of patients with LAC of different etiology have been developed and introduced into clinical practice. However, the main problem in the treatment of LAC is early diagnosis, taking into account both factors and mechanisms responsible in tumor initiation and progression. Identification of a wide biomarker repertoire with high specificity and reliability of detection appears to be a solution to this problem. In this context, proteins with differential expression in normal and pathological condition, suitable for detection in biological fluids are the most promising biomarkers. In this review we have analyzed literature data on studies aimed at search of LAC biomarkers. The major attention has been paid to protein biomarkers as the most promising and convenient subject of clinical diagnosis. The review also summarizes existing knowledge on posttranslational modifications, splice variants, isoforms, as well as model systems and transcriptome changes in LAC.

The lncRNA HOXA11-AS functions as a competing endogenous RNA to regulate PADI2 expression by sponging miR-125a-5p in liver metastasis of colorectal cancer

Several long non-coding RNAs (lncRNAs) play important roles in the regulation of liver metastasis in colorectal cancer (CRC) patients. We previously described the potential involvement of HOMEOBOX A11 (HOXA11) antisense RNA (HOXA11-AS), miR-125a-5p, and peptidyl arginine deiminase 2 (PADI2) in promoting liver metastasis in CRC patients. In the present study, we verified the significant upregulation of HOXA11-AS and PADI2, as well as the downregulation of miR-125a-5p, in CRC patients with liver metastasis. Overexpression and knockdown studies of HOXA11-AS or PADI2, as well as gain-/loss-of-function studies of miR-125a-5p, revealed a positive correlation between HOXA11-AS and PADI2 and a negative correlation with miR-125a-5p in the regulation of liver metastasis in CRC cell lines. Overall, we conclude that HOXA11-AS promotes liver metastasis in CRC by functioning as a miR-125a-5p sponge and describe a novel HOXA11-AS–miR-125a-5p–PADI2 regulatory network involved in CRC liver metastasis.

Integrated analysis of dosage effect lncRNAs in lung adenocarcinoma based on comprehensive network

Accumulating evidences indicate that cancer-related lncRNAs occur frequent somatic copy number alternation (SCNA). Although individual SCNA lncRNAs have been implicated in tumor biology, their regulatory mechanism has not been assessed in a systematic way. In order to explore the expression characteristics and biological functions of SCNA lncRNAs in cancer, we built a computational framework based on lncRNA expression profiles, lncRNA copy numbers and dosage sensitivity score (DSS). First, we found that the lncRNAs with different DSS were involved in distinct biological processes, while those with the same DSS had similar functions. Second, some of the lncRNAs participated in the progression and metastasis of lung adenocarcinoma (LUAD) through cis-acting regulation. In lncRNA-TF-mRNA network, lncRNAs interacted with 4 TFs and affected the immune system, and further influenced LUAD progression. Third, competing endogenous RNA network analysis inferred that lncRNA ENSG00000240990 competed with HOXA10 to absorb hsa-let-7a/b/f/g-5p and affected patient prognosis in LUAD. Last but not least, by integrating target information of miRNA we also provided a new perspective for the discovery of potential small molecule drugs. In summary, we systematically analyzed the regulatory role of SCNA lncRNAs. This work may facilitate cancer research and serve as the basis for future efforts to understand the role of SCNA lncRNAs, develop novel biomarkers and improve knowledge of tumor biology.

A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples

BACKGROUND

RNA-sequencing (RNA-seq) has emerged as one of the most sensitive tool for gene expression analysis. Among the library preparation methods available, the standard poly(A) + enrichment provides a comprehensive, detailed, and accurate view of polyadenylated RNAs. However, on samples of suboptimal quality ribosomal RNA depletion and exon capture methods have recently been reported as better alternatives.

METHODS

We compared for the first time three commercial Illumina library preparation kits (TruSeq Stranded mRNA, TruSeq Ribo-Zero rRNA Removal, and TruSeq RNA Access) as representatives of these three different approaches using well-established human reference RNA samples from the MAQC/SEQC consortium on a wide range of input amounts (from 100 ng down to 1 ng) and degradation levels (intact, degraded, and highly degraded).

RESULTS

We assessed the accuracy of the generated expression values by comparison to gold standard TaqMan qPCR measurements and gained unprecedented insight into the limits of applicability in terms of input quantity and sample quality of each protocol. We found that each protocol generates highly reproducible results (R ² > 0.92) on intact RNA samples down to input amounts of 10 ng. For degraded RNA samples, Ribo-Zero showed clear performance advantages over the other two protocols as it generated more accurate and better reproducible gene expression results even at very low input amounts such as 1 ng and 2 ng. For highly degraded RNA samples, RNA Access performed best generating reliable data down to 5 ng input.

CONCLUSIONS

We found that the ribosomal RNA depletion protocol from Illumina works very well at amounts far below recommendation and over a good range of intact and degraded material. We also infer that the exome-capture protocol (RNA Access, Illumina) performs better than other methods on highly degraded and low amount samples.

Expression and clinicopathological significance of the lncRNA HOXA11-AS in colorectal cancer

HOXA11 antisense RNA (HOXA11-AS) is a long non-coding RNA (lncRNA) that is important in determining cancer progression. HOXA11-AS was recently identified as a novel biomarker in lung cancer progression. However, its role in colorectal cancer (CRC) remains poorly understood. The present study aimed to analyze lncRNA HOXA11-AS expression in CRC and investigate a possible association between HOXA11-AS and clinicopathological factors. HOXA11-AS expression was examined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in 84 CRC tissues and adjacent non-cancerous tissues, in addition to 3 CRC cell lines and 1 human normal colorectal cell line. The results demonstrated that HOXA11-AS expression was decreased in the CRC tissues and cell lines compared with that of the controls (P<0.05). Clinicopathological analysis indicated that low HOXA11-AS expression was significantly correlated with tumor size, advanced tumor-node-metastasis stage, lymph node metastasis and carcinoembryonic antigen level of patients with CRC (P<0.05). Furthermore, the areas under the curve (AUC) were 0.613 and 0.628 for HOXA11-AS, indicating that the lncRNA is able to distinguish CRC tissue from non-cancerous tissue, and CRC tissue with lymph node metastasis from CRC without lymph node metastasis. Therefore, HOXA11-AS may function as a potential biomarker and target for novel therapeutic strategies to treat CRC.