scholarly journals Colon Cancer Classification and Prognosis Prediction Based on Genomics Multi-features

2021 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
Chengfeng Wu ◽  
Qijun Yang ◽  
Hongping Wan ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Cell ◽  
Evaluation Model ◽  
Lasso Regression ◽  
Data Set ◽  
Prognosis Prediction ◽  
External Data ◽  
Core Genes ◽  
Prognosis Evaluation

Abstract Background: To classify colon cancer and predict the prognosis of patients with multiple characteristics of the genome.Methods: We used the mRNA expression profile data and mutation maf files of colon cancer patients in the TCGA database to calculate the TMB value of patients. Combined with CNV, MSI, and corresponding clinical information, the patients were clustered by the "K-means" method to identify different molecular subtypes of colon cancer. Comparing the differences of prognosis, and immune cell infiltration, and other indicators among patients in each subgroup, we used COX and lasso regression analysis to screen out the prognosis difference genes among subgroups and construct the prognosis prediction model. We used the external data set to verify the model, and carried out the hierarchical analysis of the model to compare the immune infiltration of patients in the high and low-risk groups. And detected the expression differences of core genes in tumor tissues of patients with different clinical stages by qPCR and immunohistochemistry.Results: We successfully calculated the TMB value and divided the patients into three subgroups. The prognosis of the second subgroup was significantly different from the other two groups. The mmunoinfiltration analysis showed that the expression of NK.cells.resting increased in cluster1 and cluster 3, and the expression of T.cells.CD4.memory.resting increased in cluster3. By analyzing the differences among subgroups, we screened out eight core genes related to prognoses, such as HYAL1, SPINK4, EREG, and successfully constructed a patient prognosis evaluation model. The test results of the external data set shows that the model can accurately predict the prognosis of patients; Compared with risk factors such as TNM stage and age, the risk score of the model has higher evaluation efficiency. The experimental results confirmed that the differential expression of eight core genes was basically consistent with the model evaluation results.Conclusion: Colon cancer patients were further divided into three subtypes by using genomic multi-features, and eight-core genes related to prognosis were screened out and the prognosis evaluation model was successfully constructed. With external data and experiments, it verified that the model had good evaluation efficiency.

scholarly journals Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2632
Author(s):  
Aparajita Budithi ◽  
Sumeyye Su ◽  
Arkadz Kirshtein ◽  
Leili Shahriyari
Keyword(s):  
Mathematical Model ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Cell ◽  
Treatment Options ◽  
Data Driven ◽  
T Helper ◽  
Primary Tumors ◽  
Cell Fractions

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors’ gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.

scholarly journals Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes

2021 ◽  
Author(s):  
shenglan li ◽  
Zhuang Kang ◽  
jinyi Chen ◽  
Can Wang ◽  
Zehao Cai ◽  
...  
Keyword(s):  
Immune Cell ◽  
Intracranial Tumor ◽  
Cell Infiltration ◽  
Molecular Profile ◽  
Immune Cell Infiltration ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Hub Gene ◽  
External Data

Abstract Background Medulloblastoma is a common intracranial tumor among children. In recent years, research on cancer genome has established four distinct subtypes of medulloblastoma: WNT, SHH, Group3, and Group4. Each subtype has its own transcriptional profile, methylation changes, and different clinical outcomes. Treatment and prognosis also vary depending on the subtype. Methods Based on the methylation data of medulloblastoma samples, methylCIBERSORT was used to evaluate the level of immune cell infiltration in medulloblastoma samples and identified 10 kinds of immune cells with different subtypes. Combined with the immune database, 293 Imm-DEGs were screened. Imm-DEGs were used to construct the co-expression network, and the key modules related to the level of differential immune cell infiltration were identified. Three immune hub genes (GAB1, ABL1, CXCR4) were identified according to the gene connectivity and the correlation with phenotype in the key modules, as well as the PPI network involved in the genes in the modules. Results The subtype marker was recognized according to the immune hub, and the subtype marker was verified in the external data set, the methylation level of immune hub gene among different subtypes was compared and analyzed, at the same time, tissue microarray was used for immunohistochemical verification, and a multi-factor regulatory network of hub gene was constructed. Conclusions Identifying subtype marker is helpful to accurately identify the subtypes of medulloblastoma patients, and can accurately evaluate the treatment and prognosis, so as to improve the overall survival of patients.

scholarly journals Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Colon Cancer Patients and Assist Immunotherapy

2021 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Guanyu Yan ◽  
Ningning Wang ◽  
Moye Chen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Microsatellite Instability ◽  
Cancer Patients ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Macrophage Infiltration ◽  
M2 Macrophage ◽  
High Morbidity ◽  
M2 Macrophages ◽  
Hub Genes

Abstract Background: Colon cancer is a kind of malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divide it into different subtypes in order to achieve individualized treatment. With the rise of immunotherapy, its value in the field of tumor has initially emerged. Based on the above background, from the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored it.Methods: Cibersort was used to analyze the level of immune cell infiltration in colon cancer patients in the TCGA database. WGCNA, Consensus Clustering analysis, Lasso analysis, and univariate KM analysis were used to screen and verify the hub genes associated with M2 macrophages. PCA was used to establish the M2 macrophage-related score—M2I Score. The correlation between M2I Score and somatic cell variation and microsatellite instability were analysed. Furthermore the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored. Results: M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were selected as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in COAD was constructed based on four hub genes. M2I Score was positively correlated with tumor mutation load (TMB). The M2I Score of MSI-H group was higher than that of MSI-L group and MSS group. Combine with the TCIA database, we concluded that patients with a high M2I Score were more sensitive to PD-1 inhibitors and PD-1 inhibitors combined with CTLA-4 inhibitors. The low rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.Conclusion: Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score and divided the patients into two subgroups: high M2I Score group and low M2I Score group. TMB, microsatellite instability and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

scholarly journals SNP Mutation‐related Genes in Colon Cancer for Monitoring and Prognosis of Patients: A Study Based on the TCGA Database

2020 ◽  
Author(s):  
Xin Liao ◽  
jian li ◽  
Yuxiang Chen ◽  
Haibo Ding ◽  
Chen Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Cause Of Death ◽  
Immune Cell ◽  
Tumor Therapy ◽  
Molecular Pathogenesis ◽  
Related Data ◽  
The Core ◽  
Cancer Tumor ◽  
Core Genes

Abstract Background: Cancer is still the leading cause of death in humans, and the fourth leading cause of death is colorectal cancer. Tumor bioinformatics has been developing in recent years, the prognosis and quality of life of patients can be improved by using relevant tools to understand the molecular pathogenesis of colorectal cancer and related prognostic markers. Methods: In this study, Bioinformatics analysis of the snp-related data of colon cancer patients from the TCGA database, it was found that the expression levels of 4 mutated genes (CTTNBP2,DAPK1, DMXL1,SPTBN2) were significantly different from those of wild type and their prognosis. In order to explore how the core genes affect the prognosis of patients, the gene expression of these core genes was analyzed. Results: It was found that the core genes are related to a variety of cancer-related pathway genes, including pi3k-akt pathway and TSC/mTOR pathway. Drug sensitivity analysis showed that SPTBN2 could be inhibited by a variety of drugs, including austocystin D, afatinib, and belinostat. Tumor immunity is closely related to tumor therapy. Through the analysis of immune infiltration of core genes, it was found that DAPK1 and DMXL2 were associated with a variety of immune cell infiltration. Conclusion: Therefore, the detection of genetic mutations and related expressions may be significant in predicting the prognosis of patients with colon cancer. Through the study of high-throughput information excavating, it was discovered that the molecular pathogenesis and prognosis of patients with colon cancer were helpful to the bioinformatics theory.

scholarly journals Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

2021 ◽  
Vol 12 ◽  
Author(s):  
Yunfei Dong ◽  
Tao Shang ◽  
HaiXin Ji ◽  
Xiukou Zhou ◽  
Zhi Chen
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Risk Group ◽  
Univariate Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Lasso Regression ◽  
Independent Predictive Factor

BackgroundThe pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.MethodsFrom the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.ResultsTwo molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.ConclusionThis research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

scholarly journals Effect of Aging-Related Genes on the Prognosis of Colon Cancer

2021 ◽  
Author(s):  
Yushu Liu ◽  
Jiantao Gong ◽  
Yanyi Huang ◽  
Qunguang Jiang
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Internal Validation ◽  
Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

Adjuvant treatment for resected rectal cancer: Effect of postoperative oxaliplatin added to 5-FU in the neoadjuvant era.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 672-672
Author(s):  
Benjamin Garlipp ◽  
Patrick Stuebs ◽  
Hans Lippert ◽  
Karsten Ridwelski ◽  
Henry Ptok ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Colon Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Adjuvant Treatment ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Data Set ◽  
Cox Regression Analysis ◽  
The Impact

672 Background: Oxaliplatin (Ox) added to postoperative 5-fluorouracil (5FU) based adjuvant treatment has shown a survival benefit in colon cancer. For rectal cancer, the impact of Ox on survival has almost exclusively been tested in studies using 5FU +/- Ox both as a component of preoperative chemoradiotherapy (CRT) and as adjuvant treatment. Only one study (NCT00807911) investigated adjuvant 5FU +/- Ox in patients undergoing preop 5FU based CRT without Ox. Thus, the evidence for the benefit of adding Ox to adjuvant 5FU in patients treated with preop 5FU based CRT is limited. Methods: Data from the prospective German multicenter Quality Assurance in Rectal Cancer observational trial involving more than 300 hospitals of all levels of care throughout Germany were retrospectively analyzed. Patients undergoing R0 total mesorectal excision (TME) for rectal cancer following neoadjuvant 5FU based treatment without oxaliplatin between 01/01/2008 and 12/31/2010 were included. Disease-free survival (DFS) in patients receiving adjuvant treatment with or without Ox was compared using the Kaplan Meier method. The impact of adjuvant treatment with 5FU with or without Ox on DFS was investigated in a Cox regression analysis including open vs. laparoscopic approach, pT stage, pN stage, tumor grading, TME quality grade, and presence of anastomotic leakage as potential confounding factors. Results: The entire data set included 1,861 patients. Data for all variables investigated were available for 599 patients of whom 512 (85%) and 89 (15%) received 5FU based adjuvant treatment without and with Ox, respectively. Mean DFS was not different in patients receiving 5FU only vs. 5FU with Ox (p=0.103). Cox regression analysis revealed no significant impact of adding Ox to adjuvant 5FU on DFS. Of all factors analyzed, only pN2 (vs. pN0) status had an independent adverse effect on DFS (Hazard ratio 4.22, p<0.001). Conclusions: These data indicate that adjuvant Ox added to 5FU does not provide a DFS benefit in rectal cancer patients treated with preoperative 5FU based CRT under routine care conditions. Rectal cancer patients may be different from patients with colon cancer with respect to benefit from adjuvant Ox.

scholarly journals Constructing the ceRNA Regulatory Network and Combining Immune Cells to Evaluate Prognosis of Colon Cancer Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiasheng Xu ◽  
Tianyi Ling ◽  
Siqi Dai ◽  
Shuwen Han ◽  
Kefeng Ding
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Cells ◽  
Regulatory Network ◽  
Clinical Information ◽  
Risk Groups ◽  
Lasso Regression ◽  
Cerna Network ◽  
Significant Difference ◽  
Prognosis Model

Objective: This study was conducted in order to construct a competitive endogenous RNA (ceRNA) network to screen RNA that plays an important role in colon cancer and to construct a model to predict the prognosis of patients.Methods: The gene expression data of colon cancer were downloaded from the TCGA database. The difference was analyzed by the R software and the ceRNA network was constructed. The survival-related RNA was screened out by combining with clinical information, and the prognosis model was established by lasso regression. CIBERSORT was used to analyze the infiltration of immune cells in colon cancer, and the differential expression of immune cells related to survival was screened out by combining clinical information. The correlation between RNA and immune cells was analyzed by lasso regression. PCR was used to verify the expression of seven RNAs in colon cancer patients with different prognoses.Results: Two hundred and fifteen lncRNAs, 357 miRNAs, and 2,955 mRNAs were differentially expressed in colon cancer. The constructed ceRNA network contains 18 lncRNAs, 42 miRNAs, and 168 mRNAs, of which 18 RNAs are significantly related to survival. Through lasso analysis, we selected seven optimal RNA construction models. The AUC value of the model was greater than 0.7, and there was a significant difference in the survival rate between the high- and low-risk groups. Two kinds of immune cells related to the prognosis of patients were screened out. The results showed that the expression of seven RNA markers in colon cancer patients with different prognoses was basically consistent with the model analysis.Conclusion: We have established the regulatory network of ceRNA in colon cancer, screened out seven core RNAs and two kinds of immune cells, and constructed a comprehensive prognosis model of colon cancer patients.

scholarly journals Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes

2021 ◽  
Author(s):  
Shenglan Li ◽  
Zhuang Kang ◽  
Jinyi Chen ◽  
Can Wang ◽  
Zehao Cai ◽  
...  
Keyword(s):  
Immune Cell ◽  
Intracranial Tumor ◽  
Cell Infiltration ◽  
Molecular Profile ◽  
Immune Cell Infiltration ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Hub Gene ◽  
External Data

Abstract Medulloblastoma is a common intracranial tumor among children. In recent years, research on cancer genome has established four distinct subtypes of medulloblastoma: WNT, SHH, Group3, and Group4. Each subtype has its own transcriptional profile, methylation changes, and different clinical outcomes. Treatment and prognosis also vary depending on the subtype. Based on the methylation data of medulloblastoma samples, methylCIBERSORT was used to evaluate the level of immune cell infiltration in medulloblastoma samples and identified 10 kinds of immune cells with different subtypes. Combined with the immune database, 293 Imm-DEGs were screened. Imm-DEGs were used to construct the co-expression network, and the key modules related to the level of differential immune cell infiltration were identified. Three immune hub genes (GAB1, ABL1, CXCR4) were identified according to the gene connectivity and the correlation with phenotype in the key modules, as well as the PPI network involved in the genes in the modules. The subtype marker was recognized according to the immune hub, and the subtype marker was verified in the external data set, Finally, the methylation level of immune hub gene among different subtypes was compared and analyzed, at the same time, tissue microarray was used for immunohistochemical verification, and a multi-factor regulatory network of hub gene was constructed. Identifying subtype marker is helpful to accurately identify the subtypes of medulloblastoma patients, and can accurately evaluate the treatment and prognosis, so as to improve the overall survival of patients.

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