scholarly journals Analysis of multi-omics differences in left-side and right-side colon cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11433
Author(s):  
Yanyi Huang ◽  
Jinzhong Duanmu ◽  
Yushu Liu ◽  
Mengyun Yan ◽  
Taiyuan Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Gene Mutation ◽  
Somatic Mutation ◽  
Transcriptome Data ◽  
Characteristic Analysis ◽  
Immune Microenvironment ◽  
Mutation Data ◽  
Upregulated Genes ◽  
Testing Set

Background Colon cancer is one of the most common tumors in the digestive tract. Studies of left-side colon cancer (LCC) and right-side colon cancer (RCC) show that these two subtypes have different prognoses, outcomes, and clinical responses to chemotherapy. Therefore, a better understanding of the importance of the clinical classifications of the anatomic subtypes of colon cancer is needed. Methods We collected colon cancer patients’ transcriptome data, clinical information, and somatic mutation data from the Cancer Genome Atlas (TCGA) database portal. The transcriptome data were taken from 390 colon cancer patients (172 LCC samples and 218 RCC samples); the somatic mutation data included 142 LCC samples and 187 RCC samples. We compared the expression and prognostic differences of LCC and RCC by conducting a multi-omics analysis of each using the clinical characteristics, immune microenvironment, transcriptomic differences, and mutation differences. The prognostic signatures was validated using the internal testing set, complete set, and external testing set (GSE39582). We also verified the independent prognostic value of the signature. Results The results of our clinical characteristic analysis showed that RCC had a significantly worse prognosis than LCC. The analysis of the immune microenvironment showed that immune infiltration was more common in RCC than LCC. The results of differential gene analysis showed that there were 360 differentially expressed genes, with 142 upregulated genes in LCC and 218 upregulated genes in RCC. The mutation frequency of RCC was generally higher than that of LCC. BRAF and KRAS gene mutations were the dominant genes mutations in RCC, and they had a strong mutual exclusion with APC, while APC gene mutation was the dominant gene mutation in LCC. This suggests that the molecular mechanisms of RCC and LCC differed. The 4-mRNA and 6-mRNA in the prognostic signatures of LCC and RCC, respectively, were highly predictive and may be used as independent prognostic factors. Conclusion The clinical classification of the anatomic subtypes of colon cancer is of great significance for early diagnosis and prognostic risk assessment. Our study provides directions for individualized treatment of left and right colon cancer.

Analysis of multi-omics differences in left-side and right-side colon cancer

2020 ◽  
Author(s):  
Yanyi Huang ◽  
Jinzhong Duanmu ◽  
Yushu Liu ◽  
Mengyun Yan ◽  
Taiyuan Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Clinical Characteristics ◽  
Transcriptome Data ◽  
Clinical Classification ◽  
Immune Microenvironment ◽  
Mutation Data ◽  
Testing Set

Abstract Background:Colon cancer is one of the common tumors of digestive tract. Studies of left-side colon cancer(LCC) and right-side colon cancer(RCC) show that these two subtypes had different prognosis, outcomes, and clinical response to chemotherapy. Therefore,it is necessary to explore the necessity of clinical classification of anatomic subtypes about colon cancer.Methods:We selected the transcriptome data, clinical information and somatic mutation data of colon cancer patients from the the Cancer Genome Atlas(TCGA )database portal.The transcriptome data included 390 colon cancer patients(172 LCC samples and 218 RCC samples),and the somatic mutation data included 142 LCC samples and 187 RCC samples.By conducting a multi-omics analysis of the LCC and RCC from the four aspects of clinical characteristics, immune microenvironment , transcriptomic differences and mutation differences, so as to compare the expression and prognosis difference of LCC and RCC.We are the first to construct prognostic signatures respectively for LCC and RCC respectively.The prognostic signatures is validated by internal testing set, complete set and external testing set(GSE39582).Additionally we also verified the independent prognostic value of the signature.Results:Clinical characteristics analysis results show that RCC had a significantly worse prognosis than LCC.Analysis the immune microenvironment analysis shows that RCC was more immune infiltration than LCC.The results of differential gene analysis showed that there were 360 differential expressed genes,with 142 up genes in LCC and 218 up genes in RCC.Correlation analysis of mutated genes showed that the expression of mutated genes in RCC was negatively correlated, while the expression of mutated genes in LCC was positively correlated, and the mutation frequency of RCC was generally higher than that of LCC.Meanwhile, our 4-mRNA LCC and 6-mRNA RCC prognostic signatures are highly predictive and can be used as independent prognostic factors.Conclusion:The clinical classification of anatomic subtypes of colon cancer is of great significance for its early diagnosis and prognostic risk assessment.Our study provides directions for individualized treatment of left and right colon cancer.

scholarly journals Alteration in the immune microenvironment based on APC status in MSS/pMMR colon cancer data retrieved from TCGA

2020 ◽  
Author(s):  
Haishan Lin ◽  
Hongchao Zhen ◽  
Kun Shan ◽  
Xiaoting Ma ◽  
Bangwei Cao
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Tumor Immunotherapy ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Cancer Data ◽  
T Cell Infiltration ◽  
Colon Cancers ◽  
Tumor Immune Microenvironment

Abstract Immunotherapy is currently the most advanced anti-tumor treatment approach. The efficacy of anti-tumor immunotherapy is closely related to the tumor immune microenvironment, including immune cells, infiltration of immune factors, and expression of immune checkpoints. At present, the biomarkers for predicting the efficacy of colon cancer immunotherapy do not cover all colon cancer patients suitable for immunotherapy. In this study, TCGA database was used to identify tumor genotypes suitable for anti-tumor immunotherapy. We found that some of the MSS/pMMR populations, that were initially considered unsuitable for immunotherapy, might actually be suitable. In APC-wt/MSS colon cancer, the expression of PD-1, PD-L1, CTLA4 and CYT（GZMA and PRF1）were increased. Based on calculations done by ESTIMATE and CIBERSORT algorithms, the ImmunoScore and the proportion of CT8+ T cell infiltration is increased in these patients. Enrichment analysis was done to screen signaling pathways involved in immune response, extracellular matrix, and cell adhesion. Tumors from 42 colon cancer patients, including 22 APC-mt/MSS and 20 APC-wt/MSS, were immunohistochemically evaluated for expression of CD8 and PD-L1. And APC-wt/MSS tumors showed significantly higher expression of CD8 and PD-L1 than APC-mt/MSS tumor. Based on the results, we found that some colon cancers of APC-wt/MSS are classified by Tumor Immune Microenvironment types (TIMTs) TMIT I. So that we speculate that APC-wt/MSS colon cancer patients could benefit from anti-tumor immunotherapy.

Systemic Inflammatory Biomarkers as Surrogate Markers for Stage in Colon Cancer

2021 ◽  
pp. 000313482199505
Author(s):  
Server Sezgin Uludag ◽  
Ahmet Necati Sanli ◽  
Abdullah Kagan Zengin ◽  
Mehmet Faik Ozcelik
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Late Stage ◽  
Early Stage ◽  
Tnm Stage ◽  
Inflammatory Biomarkers ◽  
Monocyte Count ◽  
Characteristic Analysis ◽  
Albumin Ratio ◽  
Lymphocyte Ratio

Background This study aimed to investigate whether the systemic inflammatory parameters currently in use in staging the disease can be used as biomarker tests operated colon cancer patients. Neutrophil, lymphocyte, monocyte, platelet, neutrophil/lymphocyte ratio (NLR), lymphocyte/monocyte ratio (LMR), platelet/lymphocyte ratio (PLR), neutrophil/monocyte ratio (NMR), CRP, albumin, lymphocyte/CRP ratio, CRP/albumin ratio, and neutrophil/albumin ratio as systemic inflammatory biomarkers and prognostic nutritional index (PNI) were evaluated. Methods This retrospective study included 592 patients. Patients with colon cancer in the cohort were divided into 2 subgroups: Tumor, nodes, metastases (TNM) stage 0, TNM stage 1, and TNM stage 2; early stage (n: 332) and TNM stage 3 and TNM stage 4; late stage (n: 260) colon cancer patients. Results LDH ( P < .001), NLR ( P < .001), PLR ( P < .05), CRP/albumin ( P < .01), and neutrophil/albumin ( P < .01) were significantly higher, while monocyte count ( P < .05) and PNI ( P < .01) were found to be significantly lower in late stage colon cancer patients than in early stage colon cancer patients. Moderate negative correlation was found between the PNI and the neutrophil/albumin ratio in late stage colon cancer patients (r: −.568, P < .001). Conclusions Our data suggest that high serum LDH, NLR, PLR, CRP/albumin, and neutrophil/albumin may be useful predictive markers for advanced stage in colon cancer. According to the receiver operating characteristic analysis results, CRP/albumin ratio can be used to discriminate early from late stage. Preoperative low monocyte count and PNI are associated with postoperative staging patients with colon cancer.

scholarly journals Discovering the molecular differences between right- and left-sided colon cancer using machine learning methods

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yimei Jiang ◽  
Xiaowei Yan ◽  
Kun Liu ◽  
Yiqing Shi ◽  
Changgang Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Colon Cancer ◽  
Gene Mutation ◽  
Gene Expression Data ◽  
Expression Patterns ◽  
High Accuracy ◽  
Braf V600e ◽  
Expression Data ◽  
Mutation Data

Abstract Background In recent years, the differences between left-sided colon cancer (LCC) and right-sided colon cancer (RCC) have received increasing attention due to the clinicopathological variation between them. However, some of these differences have remained unclear and conflicting results have been reported. Methods From The Cancer Genome Atlas (TCGA), we obtained RNA sequencing data and gene mutation data on 323 and 283 colon cancer patients, respectively. Differential analysis was firstly done on gene expression data and mutation data between LCC and RCC, separately. Machine learning (ML) methods were then used to select key genes or mutations as features to construct models to classify LCC and RCC patients. Finally, we conducted correlation analysis to identify the correlations between differentially expressed genes (DEGs) and mutations using logistic regression (LR) models. Results We found distinct gene mutation and expression patterns between LCC and RCC patients and further selected the 30 most important mutations and 17 most important gene expression features using ML methods. The classification models created using these features classified LCC and RCC patients with high accuracy (areas under the curve (AUC) of 0.8 and 0.96 for mutation and gene expression data, respectively). The expression of PRAC1 and BRAF V600E mutation (rs113488022) were the most important feature for each model. Correlations of mutations and gene expression data were also identified using LR models. Among them, rs113488022 was found to have significance relevance to the expression of four genes, and thus should be focused on in further study. Conclusions On the basis of ML methods, we found some key molecular differences between LCC and RCC, which could differentiate these two groups of patients with high accuracy. These differences might be key factors behind the variation in clinical features between LCC and RCC and thus help to improve treatment, such as determining the appropriate therapy for patients.

scholarly journals Fusobacteria is Associated with a Th1 and Th17 Immune Microenvironment in Colon Cancer Patients and Germ‐Free Mice

2019 ◽  
Vol 33 (S1) ◽  
Author(s):  
Jada C Domingue ◽  
Julia L Drewes ◽  
Susan Bullman ◽  
Alina Corona ◽  
Christina DeStefano Shields ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Microenvironment ◽  
Germ Free

scholarly journals ROKET: Associating Somatic Mutation with Clinical Outcomes through Kernel Regression and Optimal Transport

2021 ◽  
Author(s):  
Paul Little ◽  
Li Hsu ◽  
Wei Sun
Keyword(s):  
Cancer Patients ◽  
Clinical Outcomes ◽  
Somatic Mutation ◽  
Somatic Mutations ◽  
Optimal Transport ◽  
Kernel Regression ◽  
Multiple Genes ◽  
Cancer Types ◽  
Mutation Data ◽  
Gene Similarity

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analyses is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess the joint association. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene-gene similarity defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least three cancer types harboring associations between somatic mutations and overall survival, progression-free interval or cytolytic activity.

scholarly journals Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Ying Jiang ◽  
Baotong Zheng ◽  
Yang Yang ◽  
Xiangmei Li ◽  
Junwei Han
Keyword(s):  
Gene Mutation ◽  
Somatic Mutation ◽  
Immune Cells ◽  
Cell Response ◽  
Somatic Mutations ◽  
Immune Cell ◽  
Transcriptome Data ◽  
Mutation Status ◽  
Binary Matrices ◽  
Immune Cell Response

Tumor somatic mutations in protein-coding regions may generate neoantigens which may trigger antitumor immune cell response. Increasing evidence supports that immune cell response may profoundly influence tumor progression. However, there are no calculated tools to systematically identify immune cells driven by specific somatic mutations. It is urgent to develop a calculated method to comprehensively detect tumor-infiltrating immune cells driven by the specific somatic mutations in cancer. We developed a novel software package (SMDIC) that enables the automated identification of somatic mutation-driven immune cell. SMDIC provides a novel pipeline to discover mutation-specific immune cells by integrating genomic and transcriptome data. The operation modes include inference of the relative abundance matrix of tumor-infiltrating immune cells, detection of differential abundance immune cells with respect to the gene mutation status, conversion of the abundance matrix of significantly dysregulated cells into two binary matrices (one for upregulated and one for downregulated cells), identification of somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and visualization of immune cell abundance of samples in different mutation status for each gene. SMDIC provides a user-friendly tool to identify somatic mutation-specific immune cell response. SMDIC may contribute to understand the mechanisms underlying anticancer immune response and find targets for cancer immunotherapy. The SMDIC was implemented as an R-based tool which was freely available from the CRAN website https://CRAN.R-project.org/package=SMDIC.

Alteration in the Immune Microenvironment Based on APC Status in MSS/pMMR Colon Cancer Data Retrieved from TCGA

2021 ◽  
Author(s):  
Haishan Lin ◽  
Nina Ma ◽  
Hongchao Zhen ◽  
Kun Shang ◽  
Xiaoting Ma ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Tumor Immunotherapy ◽  
Immune Checkpoints ◽  
Sequencing Data ◽  
Immune Microenvironment ◽  
Cancer Data ◽  
Colon Cancers ◽  
Tumor Immune Microenvironment

Abstract BackgroundImmunotherapy is currently the most advanced anti-tumor treatment approach. The efficacy of anti-tumor immunotherapy is closely related to the tumor immune microenvironment, including immune cells, infiltration of immune factors, and expression of immune checkpoints. At present, the biomarkers for predicting the efficacy of colon cancer immunotherapy do not cover all colon cancer patients suitable for immunotherapy. In this study, TCGA database was used to identify tumor genotypes suitable for anti-tumor immunotherapy. MethodsWe downloaded 390 samples with RNA-sequencing data and somatic mutation status data from TCGA. We applied the ESTIMATE, Package limma, the CIBERSORT and some other algorithms to analyze the cellular immune microenvironment. And validated with immunohistochemistry in tumor tissues of colon cancer patients. ResultsWe found that some of the MSS/pMMR populations, that were initially considered unsuitable for immunotherapy, might actually be suitable. In APC-wt/MSS colon cancer, the expression of PD-1, PD-L1, CTLA4 and CYT（GZMA and PRF1）were increased. Based on calculations done by ESTIMATE and CIBERSORT algorithms, the ImmunoScore and the proportion of CT8+ T cell infiltration is increased in these patients. Enrichment analysis was done to screen signaling pathways involved in immune response, extracellular matrix, and cell adhesion. Tumors from 42 colon cancer patients, including 22 APC-mt/MSS and 20 APC-wt/MSS, were immunohistochemically evaluated for expression of CD8 and PD-L1. And APC-wt/MSS tumors showed significantly higher expression of CD8 and PD-L1 than APC-mt/MSS tumor. ConclusionsBased on the results, we found that some colon cancers of APC-wt/MSS are classified by Tumor Immune Microenvironment types (TIMTs) TMIT I. So that we speculate that APC-wt/MSS colon cancer patients could benefit from anti-tumor immunotherapy.

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