Predictor Of Prognosis In Colorectal Cancer Research Articles

Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer.

Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data. Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues. A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival. A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.

A novel Anoikis and immune-related genes marked prognostic signature for colorectal cancer.

Colorectal cancer (CRC) is second most commonly diagnosed cancer with high morbidity and mortality. The heterogeneity of CRC makes clinical treatment tremendously challenging. Here, we aimed to comprehensively analyze the prognosis of CRC patients based on ANOIKIS- and immune-related genes. ANOIKIS-related genes were identified by differentially analysis of high anoikis score group (ANOIKIS_high group) and low anoikis score group (ANOIKIS_low group) divided by the cutoff value of anoikis score. Immune-related genes were screened by differentially analysis of high immune score group (ImmuneScore_high group) and low immune score group (ImmuneScore_low group) classified by the cutoff value of ImmuneScore. Prognostic ANOIKIS- and immune-related genes were identified by univariate Cox regression analysis. Multivariate Cox regression analysis were used for prognostic model construction. Ferroptosis expression profiles, the infiltration of immune cells, and the somatic mutation status were analyzed and compared. Univariate and multivariate Cox-regression analyses were performed to identify independent prognostic factors for CRC patient. Nomogram that contained the independent prognostic factors was established to predict 1-, 3-, and 5-year OS probability of CRC patients. Three ANOIKIS- and immune-related signatures were applied to construct a prognostic model, which divided the CRC patients into high-risk and low-risk groups. The patients with high-risk scores had obviously shorter OSs than those with low-risk scores. The time dependent ROC curve indicated that the risk score model had a stable performance to predict survival rates. Notably, the age, pathologic T, and risk score could be used independent indicators for CRC prognosis prediction. A nomogram containing the independent prognostic factors showed that the nomogram accurately predicted 1-, 3-, and 5-year survival rates of CRC patients. In our research, a novel prognostic model was developed based on ANOIKIS- and immune-related genes in CRC, which could be used for prognostic prediction of CRC patients.

Preoperative carcinoembryonic antigen to body mass index ratio contributes to prognosis prediction in colorectal cancer.

Both carcinoembryonic antigen (CEA) level and body mass index (BMI) are traditional prognostic markers in colorectal cancer (CRC); however, to the best of our knowledge, the value of the CEA to BMI ratio (CBR) has never been addressed. In the present study, 191 patients with CRC treated using radical resection were retrospectively included, and the significance of the CBR in predicting disease-free survival (DFS) or overall survival (OS) rates was calculated. The prognostic efficacy of the CBR in predicting OS was compared with individual CEA and BMI values. The survival differences of the subgroups were calculated by Kaplan-Meier analysis, and corresponding risk factors were then estimated by a Cox proportional hazards model. As a result, 29.84% (57/191) of the patients were assigned to the high CBR group (cut-off, ≥0.28); the CBR had a sensitivity of 56.50 and 68.90%, and a specificity of 80.60 and 80.10% for DFS and OS, respectively. Patients with a high CBR more commonly underwent laparotomy and exhibited advanced T stages, the presence of tumor deposits and advanced Tumor-Node-Metastasis stages (stage II or III). The CBR was more efficient than the CEA or BMI alone in predicting OS. In addition, patients with a high CBR presented with a significantly worse outcome than patients with a low CBR. Finally, the CBR was an independent risk factor for both DFS and OS. In conclusion, the CBR was a more robust prognostic factor in CRC, and patients with a relatively high CBR exhibited poorer survival.

Emerging role of non-coding RNAs in glucose metabolic reprogramming and chemoresistance in colorectal cancer.

Metabolic reprogramming plays a critical role in colorectal cancer (CRC). It contributes to CRC by shaping metabolic phenotypes and causing uncontrolled proliferation of CRC cells. Glucose metabolic reprogramming is common in carcinogenesis and cancer progression. Growing evidence has implicated the modifying effects of non-coding RNAs (ncRNAs) in glucose metabolic reprogramming and chemoresistance in CRC. In this review, we have summarized currently published studies investigating the role of ncRNAs in glucose metabolic alterations and chemoresistance in CRC. Elucidating the interplay between ncRNAs and glucose metabolic reprogramming provides insight into exploring novel biomarkers for the diagnosis and prognosis prediction of CRC.

Bulk and single-cell transcriptome profiling reveal necroptosis-based molecular classification, tumor microenvironment infiltration characterization, and prognosis prediction in colorectal cancer

BackgroundNecroptosis is a new form of programmed cell death that is associated with cancer initiation, progression, immunity, and chemoresistance. However, the roles of necroptosis-related genes (NRGs) in colorectal cancer (CRC) have not been explored comprehensively.MethodsIn this study, we obtained NRGs and performed consensus molecular subtyping by “ConsensusClusterPlus” to determine necroptosis-related subtypes in CRC bulk transcriptomic data. The ssGSEA and CIBERSORT algorithms were used to evaluate the relative infiltration levels of different cell types in the tumor microenvironment (TME). Single-cell transcriptomic analysis was performed to confirm classification related to NRGs. NRG_score was developed to predict patients’ survival outcomes with low-throughput validation in a patients’ cohort from Fudan University Shanghai Cancer Center.ResultsWe identified three distinct necroptosis-related classifications (NRCs) with discrepant clinical outcomes and biological functions. Characterization of TME revealed that there were two stable necroptosis-related phenotypes in CRC: a phenotype characterized by few TME cells infiltration but with EMT/TGF-pathways activation, and another phenotype recognized as immune-excluded. NRG_score for predicting survival outcomes was established and its predictive capability was verified. In addition, we found NRCs and NRG_score could be used for patient or drug selection when considering immunotherapy and chemotherapy.ConclusionsBased on comprehensive analysis, we revealed the potential roles of NRGs in the TME, and their correlations with clinicopathological parameters and patients’ prognosis in CRC. These findings could enhance our understanding of the biological functions of necroptosis, which thus may aid in prognosis prediction, drug selection, and therapeutics development.

Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer.

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations. Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging. Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4–90.6, p < 0.001; validation cohort: HR: 2.04, 95% CI: 1.44–2.89, p < 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8–69.3, p < 0.001; validation cohort: HR: 1.83, 95% CI: 1.19–2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways. Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

Plasma Extracellular Vesicle Long RNAs Have Potential as Biomarkers in Early Detection of Colorectal Cancer.

BackgroundEarly detection of colorectal cancer (CRC) is crucial to the treatment and prognosis of patients. Traditional screening methods have disadvantages.Methods231 blood samples were collected from 86 CRC, 56 colorectal adenoma (CRA), and 89 healthy individuals, from which extracellular vesicle long RNAs (exLRs) were isolated and sequenced. An CRC diagnostic signature (d-signature) was established, and prognosis-associated cell components were evaluated.ResultsThe exLR d-signature for CRC was established based on 17 of the differentially expressed exLRs. The d-signature showed high diagnostic efficiency of CRC and control (CRA and healthy) samples with an area under the curve (AUC) of 0.938 in the training cohort, 0.943 in the validation cohort, and 0.947 in an independent cohort. The d-signature could effectively differentiate early-stage (stage I–II) CRC from healthy individuals (AUC 0.990), as well as differentiating CEA-negative CRC from healthy individuals (AUC 0.988). A CRA d-signature was also generated and could differentiate CRA from healthy individuals both in the training (AUC 0.993) and validation (AUC 0.978) cohorts. The enrichment of class-switched memory B-cells, B-cells, naive B-cells, and mast cells showed increasing trends between CRC, CRA, and healthy cohorts. Class-switched memory B-cells, mast cells, and basophils were positively associated with CRC prognosis while natural killer T-cells, naive B-cells, immature dendritic cells, and lymphatic endothelial cells were negatively associated with prognosis.ConclusionsOur study identified that the exLR d-signature could differentiate CRC from CRA and healthy individuals with high efficiency and exLR profiling also has potential in CRA screening and CRC prognosis prediction.

MiR-509-3p is oncogenic, targets the tumor suppressor PHLPP2, and functions as a novel tumor adjacent normal tissue based prognostic biomarker in colorectal cancer

BackgroundRecently the role of microRNAs has been explored immensely as novel regulators and potential biomarkers in several cancers. MiR-509-3p is one such miRNA that has been observed to show a mixed expression in different cancers, while it’s expression and clinical relevance in colorectal cancer (CRC) has not yet been characterized.MethodsWe used quantitative PCR to evaluate the expression of miR-509-3p in fresh-frozen CRC tumor tissues and the corresponding tumor-adjacent normal (NAT) tissues from 103 patients. Subsequently, functional studies were performed to further interpret the role of the miRNA in CRC.ResultsMiR-509-3p was found to be overexpressed in CRC tissues in nearly 80% of cases and was associated with an aggressive disease presentation. Notably, a higher expression of the miRNA promoted cell proliferation, migration, and invasion of CRC cells in in vitro and in vivo models. Mechanistically, we confirmed that miR-509-3p directly binds the 3’UTR of the tumor suppressor PHLPP2 and inhibits its expression. Furthermore, within the previous 103 clinical tissue specimens, we observed an overexpression of miR-509-3p within the NAT tissue of patients associated with a poor disease prognosis. Using multivariate analysis, it was observed that the expression of miR-509-3p within the NAT tissue was an independent predictor of prognosis in CRC. At the cellular level, through indirect coculture experiments, miR-509-3p was observed to regulate the proliferative, migratory, and invasive behavior of normal colon cells.ConclusionMiR-509-3p strongly contributes to the development and progression of CRC and can potentially function as a prognostic biomarker in the disease.

Circular RNAs as Diagnostic and Prognostic Indicators of Colorectal Cancer: A Pooled Analysis of Individual Studies.

Background: Circular RNAs (circRNAs) have proven as a special subset of endogenous RNAs that are implicated in the tumorigenesis of various cancers. This study sought to evaluate the role of circRNAs in the diagnosis and prognosis of colorectal cancer (CRC). Methods: The online databases were searched for collecting relevant studies on circRNAs as diagnostic and prognostic biomarkers of CRC. Two researchers independently screened literature, extracted data, and evaluated the bias and risks of included studies. The diagnostic and prognostic indicators were merged and analyzed using STATA 12.0 software, and sources of heterogeneity were traced by the sensitivity analysis and the meta-regression test. Results: A total of 29 articles representing 2639 CRC patients were included. The pooled sensitivity, specificity, and area under the curve (AUC) of circRNAs in differentiating CRC from non-tumor control were 0.75 (95% CI: 0.69–0.80) and 0.74 (95% CI: 0.69–0.78) and 0.81, respectively. The survival analysis showed that up-regulations of up-regulated circRNAs were significantly related to dismal survival in CRC patients (HR = 2.38, p < 0.001). A stratified analysis showed that the comprehensive diagnostic value of up-regualted circRNAs in CRC was higher than that of down-regualted circRNAs (AUC: 0.83 vs. 0.77; Z test, p < 0.05). The efficacy of tissue-derived circRNAs in the diagnosis of CRC was equal to that of plasma/serum-derived ones (AUC: 0.81 vs. 0.82; Z test, p > 0.05). Conclusion: Abnormally expressed circRNAs as auxiliary biomarkers present underlying value in the diagnosis and prognosis prediction of CRC.

Differential DNA methylation analysis of SUMF2, ADAMTS5, and PXDN provides novel insights into colorectal cancer prognosis prediction in Taiwan.

BACKGROUNDPatients with colorectal cancer (CRC) undergo surgery, as well as perioperative chemoradiation or adjuvant chemotherapy primarily based on the tumor–node– metastasis (TNM) cancer staging system. However, treatment responses and prognostic outcomes of patients within the same stage vary markedly. The potential use of novel biomarkers can improve prognostication and shared decision making before implementation into certain therapies.AIMTo investigate whether SUMF2, ADAMTS5, and PXDN methylation status could be associated with CRC prognosis.METHODSWe conducted a Taiwanese cohort study involving 208 patients with CRC recruited from Tri-Service General Hospital and applied the candidate gene approach to identify three genes involved in oncogenesis pathways. A methylation-specific polymerase chain reaction (MS-PCR) and EpiTYPER DNA methylation analysis were employed to detect methylation status and to quantify the methylation level of candidate genes in tumor tissue and adjacent normal tissue from participants. We evaluated SUMF2, ADAMTS5, and PXDN methylation as predictors of prognosis, including recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS), using a Cox regression model and Kaplan–Meier analysis.RESULTSWe revealed various outcomes related to methylation and prognosis. Significantly shorter PFS and OS were associated with the CpG_3+CpG_7 hypermethylation of SUMF2 from tumor tissue compared with CpG_3+CpG_7 hypomethylation [hazard ratio (HR) = 2.24, 95% confidence interval (CI) = 1.03-4.85 for PFS, HR = 2.56 and 95%CI = 1.08-6.04 for OS]. By contrast, a significantly longer RFS was associated with CpG_2 and CpG_13 hypermethylation of ADAMTS5 from normal tissue compared with CpG_2 and CpG_13 hypomethylation [HR (95%CI) = 0.15 (0.03-0.71) for CpG_2 and 0.20 (0.04-0.97) for CpG_13]. The relationship between the methylation status of PXDN and the prognosis of CRC did not reach statistical significance.CONCLUSIONOur study found that CpG_3+CpG_7 hypermethylation of SUMF2 from tumor tissue was associated with significantly shorter PFS and OS compared with CpG_3+CpG_7 hypomethylation. CpG_2 and CpG_13 hypermethylation of ADAMTS5 from normal tissue was associated with a significantly longer RFS compared with CpG_2 and CpG_13 hypomethylation. These methylation-related biomarkers which have implications for CRC prognosis prediction may aid physicians in clinical decision-making.

Clinical utility of a serum glycome analysis in patients with colorectal cancer.

Serum glycans are known to be good markers for the early diagnosis and prognostic prediction in many cancers. The aims of this study were to reveal the serum glycan changes comprehensively during the process of carcinogenesis from colorectal adenoma (CRA) to colorectal cancer (CRC) and to evaluate the usefulness of the glycan profiles as clinical markers for CRC. Serum samples were obtained from 80 histologically proven CRC and 36 CRA cases. The levels of glycans in the serum were examined with a comprehensive, quantitative, high-throughput unique glycome analysis, and their diagnostic and prognostic abilities were evaluated. Among 34 stably detected glycans, nine were differentially expressed between CRC and CRA. Serum levels of hybrid type glycans were increased in patients with CRC compared with those with CRA (P<0.001), and both hybrid-type and multi-antennary glycans were significantly increased in advanced cancer cases. The glycan, m/z 1914, showed the highest diagnostic value among the decreased glycans, whereas m/z 1708 showed the highest among the increased glycans. The glycan ratio m/z 1708/1914 showed a higher area under the receiver operating characteristic curve (0.889) than any other single glycan or conventional tumor marker, such as carcinoembryonic antigen (0.766, P=0.040) and carbohydrate antigen 19-9 (0.615, P<0.001). High m/z 1708/1914 was also correlated with an advanced cancer stage and short overall survival. Serum glycans, especially the m/z 1708/1914 ratio, were useful for the diagnosis, staging, and prognosis prediction of CRC.

Genome-wide association study identifies tumor anatomical site-specific risk variants for colorectal cancer survival

Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10–8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30–1.69, p = 8.47 × 10–9) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65–2.77, p = 9.19 × 10–9 and rs144717887, HR = 2.01, 95% CI 1.57–2.58, p = 3.14 × 10–8), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.

A long noncoding RNA, LncRNA-LOC100127888, is associated with poor prognosis in colorectal cancer patients

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Despite great advances in medical technology, the survival rate of CRC patients is still extremely low, mainly due to recurrence and chemotherapy resistance. Therefore, it is particularly important to find valuable biomarkers to predict the prognosis of CRC. Immunohistochemistry was performed to test the expression of LncA in a CRC tissue microarray containing 470 tumor and corresponding normal tissues. Kaplan-Meier survival curves and a Cox proportional hazard model were used to evaluate the correlation between lncRNA-LOC100127888 (LncA) expression and CRC prognosis. Cell proliferation, migration and invasion were detected by CCK-8 and Transwell assays. The expression of LncA was significantly upregulated in CRC cancer tissues compared with the corresponding noncancer tissues. High LncA expression in cancer tissues was associated with pathological classification, depth of invasion, lymph node metastasis, TNM stage and distant metastasis. LncA expression was an unfavorable prognostic factor for CRC patients. Furthermore, LncA combined with clinical variables exhibited synergistic potential for the prediction of CRC prognosis. Low expression of LncA in HT 29 and HCT116 cells could decrease cell proliferation, and the migration and invasion of these cells was inhibited by knockdown of LncA. LncA could be used as an effective biomarker to predict the prognosis of CRC patients. We could predict the prognosis of CRC patients more effectively by combining LncA with clinical indicators.

Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer.

Colorectal cancer (CRC), a seriously threat that endangers public health, has a striking tendency to relapse and metastasize. Redox-related signaling pathways have recently been extensively studied in cancers. However, the study and potential role of redox in CRC remain unelucidated. We developed and validated a risk model for prognosis and recurrence prediction in CRC patients via identifying gene signatures driven by redox-related signaling pathways. The redox-driven prognostic signature (RDPS) was demonstrated to be an independent risk factor for patient survival (including OS and RFS) in four public cohorts and one clinical in-house cohort. Additionally, there was an intimate association between the risk score and tumor immune infiltration, with higher risk score accompanied with less immune cell infiltration. In this study, we used redox-related factors as an entry point, which may provide a broader perspective for prognosis prediction in CRC and have the potential to provide more promising evidence for immunotherapy.

A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.

AimTo develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC).MethodsWe downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Genes with significant prognostic value in CRC were screened through univariate Cox regression analysis, while the LASSO Cox regression method was applied to screen optimal genes to construct the autophagy‐related prognostic signature.Results11 autophagy genes were identified and selected for the establishment of prognosis prediction model for CRC patients. The CRC patients were classified into the low- and high-risk groups according to the optimal cutoff value. The time-dependent ROC curves indicated the good performance of this model in prognosis prediction, with AUC values of 0.66, 0.66, and 0.67 at 1, 3 and 5 years for TCGA samples, as well as AUC values of 0.63, 0.65 and 0.64 for GEO samples, respectively. The multivariate Cox regression analysis results confirmed risk score as the independent marker for prognosis prediction in CRC. Besides, the constructed nomogram also had high predictive value. The results analysis on the tumor infiltrating immune cells (TIICs) relative ratios and mRNA levels of key immune checkpoint receptors indicated the signature was closely related to immune microenvironment of CRC in the context of TIICs and immune checkpoint receptors’ mRNA level. The proportion of MSI-L + MSI-H in the high-risk group was higher than that in the low-risk group. Moreover, the tumor purity was evaluated by estimate function package suggested that lower tumor purity in CRC might lead to a poorer prognosis.ConclusionThe autophagy-related features obtained in this study were able to divide the CRC patients into low- and high-risk groups, which should be contribute to the decision-making of CRC treatment.

The underlying molecular mechanisms and prognostic factors of RNA binding protein in colorectal cancer: a study based on multiple online databases

BackgroundRNA binding protein (RBP) is an active factor involved in the occurrence and development of colorectal cancer (CRC). Therefore, the potential mechanism of RBP in CRC needs to be clarified by dry-lab analyses or wet-lab experiments.MethodsThe differential RBP gene obtained from the GEPIA 2 (Gene Expression Profiling Interactive Analysis 2) were performed functional enrichment analysis. Then, the alternative splicing (AS) events related to survival were acquired by univariate regression analysis, and the correlation between RBP and AS was analyzed by R software. The online databases were conducted to analyze the mutation and methylation of RBPs in CRC. Moreover, 5 key RBP signatures were obtained through univariate and multivariate Cox regression analysis and established as RBP prognosis model. Subsequently, the above model was verified through another randomized group of TCGA CRC cohorts. Finally, multiple online databases and qRT-PCR analysis were carried to further confirm the expression of the above 5 RBP signatures in CRC.ResultsThrough a comprehensive bioinformatics analysis, it was revealed that RBPs had genetic and epigenetic changes in CRC. We obtained 300 differentially expressed RBPs in CRC samples. The functional analysis suggested that they mainly participated in spliceosome. Then, a regulatory network for RBP was established to participate in AS and DDX39B was detected to act as a potentially essential factor in the regulation of AS in CRC. Our analysis discovered that 11 differentially expressed RBPs with a mutation frequency higher than 5%. Furthermore, we found that 10 differentially expressed RBPs had methylation sites related to the prognosis of CRC, and a prognostic model was constructed by the 5 RBP signatures. In another randomized group of TCGA CRC cohorts, the prognostic performance of the 5 RBP signatures was verified.ConclusionThe potential mechanisms that regulate the aberrant expression of RBPs in the development of CRC was explored, a network that regulated AS was established, and the RBP-related prognosis model was constructed and verified, which could improve the individualized prognosis prediction of CRC.

Value of prediction models for prognosis prediction of colorectal cancer: an analysis based on TCPA database

To assess the value of the combination of multiple proteins in predicting the prognosis of colorectal cancer (CRC) through bioinformatics analysis. The protein expression and clinical data were downloaded from TCPA database. Perl and R were used to screen the prognostic-related proteins, and through Cox analysis, the proteins that served as independent prognostic factors of CRC were identified to build the prediction model. Survival analyses were conducted for each of the proteins included in the prediction model and the risk score of the model, and risk curves was drawn for the risk score and the patients' survival status to verify the performance of the model. Independent prognosis analysis and ROC analysis were used to assess the value and advantages of the model in prognosis prediction. The interactions between the proteins included in the model and the differential expressions of the key genes related with the proteins were analyzed. Six proteins were screened for model construction. Compared with a single gene, the model showed much greater prognostic value for CRC. Independent prognostic analysis showed that the risk score of the prediction model was significantly related with the prognosis (P < 0.001), and the model could be used as an independent risk factor for prognostic assessment of the patients. ROC analysis showed that the model had good specificity and sensitivity for prognostic prediction (AUC=0.734). Protein interactions showed that BID, SLC1A5 and SRC_pY527 were significantly correlated with other proteins (P < 0.001), and SLC1A5 and SRC_pY527 had the most significant interactions with other proteins (P < 0.001). Except for those of INPP4B, the key genes related with the proteins in the prediction model had significant differential expressions at the mRNA level in CRC (P < 0.05). The prediction model constructed based on 6 proteins has good prognostic value for CRC. The proteins SLC1A5 and SRC_pY527 play key roles in the prognosis of CRC, and SRC_pY527 may regulate the occurrence and progression of CRC through the SRC/AKT/MAPK signal axis and thus may serve as a new therapeutic target of CRC.

Prognostic Signatures Based on Thirteen Immune-Related Genes in Colorectal Cancer.

BackgroundThe immunosuppressive microenvironment is closely related to tumorigenesis and cancer development, including colorectal cancer (CRC). The aim of the current study was to identify new immune biomarkers for the diagnosis and treatment of CRC.Materials and MethodsCRC data were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Sequences of immune-related genes (IRGs) were obtained from the ImmPort and InnateDB databases. Gene set enrichment analysis (GSEA) and transcription factor regulation analysis were used to explore potential mechanisms. An immune-related classifier for CRC prognosis was conducted using weighted gene co-expression network analysis (WGCNA), Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis. ESTIMATE and CIBERSORT algorithms were used to explore the tumor microenvironment and immune infiltration in the high-risk CRC group and the low-risk CRC group.ResultsBy analyzing the IRGs that were significantly associated with CRC in the module, a set of 13 genes (CXCL1, F2RL1, LTB4R, GPR44, ANGPTL5, BMP5, RETNLB, MC1R, PPARGC1A, PRKDC, CEBPB, SYP, and GAB1) related to the prognosis of CRC were identified. An IRG-based prognostic signature that can be used as an independent potentially prognostic indicator was generated. The ROC curve analysis showed acceptable discrimination with AUCs of 0.68, 0.68, and 0.74 at 1-, 3-, and 5- year follow-up respectively. The predictive performance was validated in the train set. The potential mechanisms and functions of prognostic IRGs were analyzed, i.e., NOD-like receptor signaling, and transforming growth factor beta (TGFβ) signaling. Besides, the stromal score and immune score were significantly different in high-risk group and low-risk group (p=4.6982e-07, p=0.0107). Besides, the proportions of resting memory CD4+ T cells was significantly higher in the high-risk groups.ConclusionsThe IRG-based classifier exhibited strong predictive capacity with regard to CRC. The survival difference between the high-risk and low-risk groups was associated with tumor microenvironment and immune infiltration of CRC. Innovative biomarkers for the prediction of CRC prognosis and response to immunological therapy were identified in the present study.

A 10-gene-methylation-based signature for prognosis prediction of colorectal cancer

BackgroundColorectal cancer (CRC) is a common malignant tumor of digestive tract which has high incidence and mortality rates. Accurate prognosis prediction of CRC patients is pivotal to reduce the mortality and disease burden. MethodsIn this study, we comprehensively analyzed the gene expression and methylation data of CRC samples from The Cancer Genome Atlas (TCGA). Differential expression genes (DEGs) and methylation CpGs (DMCs) in tumor tissues compared with adjacent normal tissues of CRC were first identified. Functional enrichment analysis of DEGs and DMCs was performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Spearman correlation analysis was used to screen DMCs that negatively correlated with gene expressions which were subsequently applied to sure independence screening (SIS) along with stepwise regression for screening optimal CpGs for CRC prognosis prediction model construction by Cox regression analysis. ResultsWe identified a total of 1774 DEGs (663 upregulated and 1111 downregulated) and 11,975 DMCs (7385 hypermethylated and 4590 hypomethylated) in CRC tumor samples compared with adjacent normal samples. The hypermethylated loci were mainly located on CpG island, while the hypomethylated loci were mainly located on N-shore. Spearman correlation analysis screened 321 DMCs that negatively correlated with expressions of their annotated genes. Cox regression model consist of 10 CpGs was finally established which could effectively stratified CRC patients that exhibited significantly different overall survival probability independent of age, gender, and pathological staging. ConclusionWe established a prognosis prediction model based on 10 methylation sites, which could evaluate the prognosis of CRC patients.

Using Evolutionary Trees for the Colorectal Cancer Prognosis Prediction

The measurement of tree similarity based on structure comparison has been long used in diverse fields. We applied the evolutionary tree method to study the cancer genome. Cancer evolutionary trees, representing cancer diversity, provide information on the clonal evolution and the clinical outcome of cancer patients. This study considered 107 colorectal cancer (CRC) patients who received deep targeted sequencing of cancer tissues. The evolutionary trees of individual cancer patients were reconstructed from genome sequencing data based on variant allele frequencies (VAFs) of point mutations and small insertions or deletions (indels). The main purpose of this study was to predict cancer recurrence. We mapped the structure of a cancer evolutionary tree to a rooted tree and developed a canonical-form transformation for solving tree isomorphism to ensure that each patient has a unique tree structure. We proposed an algorithm for comparing tree similarity in terms of cost calculation between evolutionary structure trees. The cost was calculated using the node position, tree size (or number of nodes), tree height, node depth, number of descendants of the node (the size of the subtree with the node as a root), and relationship of the node with other nodes. After tree similarity comparison, the cancer patients were clustered into two groups through k-means clustering. The clustering information indicated that the evolutionary structure trees were associated with gender and tumor invasion stage. Several machine-learning strategies including random forest, support vector machine (SVM), bagging, and boosting were used to predict cancer recurrence in these patients. Our results revealed that combining the clustering information of evolutionary structure trees increased the prediction performance compared with using clinical information alone, and tree similarity comparison can help in the prognostic analysis of cancer patients.