Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

Author(s):  
Jianxing Ma ◽  
Chen Wang

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wanting Song ◽  
Yi Bai ◽  
Jialin Zhu ◽  
Fanxin Zeng ◽  
Chunmeng Yang ◽  
...  

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiang-hui Ning ◽  
Yuan-yuan Qi ◽  
Fang-xin Wang ◽  
Song-chao Li ◽  
Zhan-kui Jia ◽  
...  

Bladder cancer (BLCA) is the most common urinary tract tumor and is the 11th most malignant cancer worldwide. With the development of in-depth multisystem sequencing, an increasing number of prognostic molecular markers have been identified. In this study, we focused on the role of protein-coding gene methylation in the prognosis of BLCA. We downloaded BLCA clinical and methylation data from The Cancer Genome Atlas (TCGA) database and used this information to identify differentially methylated genes and construct a survival model using lasso regression. We assessed 365 cases, with complete information regarding survival status, survival time longer than 30 days, age, gender, and tumor characteristics (grade, stage, T, M, N), in our study. We identified 353 differentially methylated genes, including 50 hypomethylated genes and 303 hypermethylated genes. After annotation, a total of 227 genes were differentially expressed. Of these, 165 were protein-coding genes. Three genes (zinc finger protein 382 (ZNF382), galanin receptor 1 (GALR1), and structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1)) were selected for the final risk model. Patients with higher-risk scores represent poorer survival than patients with lower-risk scores in the training set ( HR = 2.37 , 95% CI 1.43-3.94, p = 0.001 ), in the testing group ( HR = 1.85 , 95% CI 1.16-2.94, p = 0.01 ), and in the total cohort ( HR = 2.06 , 95% CI 1.46-2.90, p < 0.001 ). Further univariate and multivariate analyses using the Cox regression method were conducted in these three groups, respectively. All the results indicated that risk score was an independent risk factor for BLCA. Our study screened the different methylation protein-coding genes in the BLCA tissues and constructed a robust risk model for predicting the outcome of BLCA patients. Moreover, these three genes may function in the mechanism of development and progression of BLCA, which should be fully clarified in the future.


2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hu Qian ◽  
Ting Lei ◽  
Pengfei Lei ◽  
Yihe Hu

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis ( P < 0.0001 ). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.


2021 ◽  
Vol 11 ◽  
Author(s):  
Lianze Chen ◽  
Baohui Hu ◽  
Xinyue Song ◽  
Lin Wang ◽  
Mingyi Ju ◽  
...  

Accumulating evidence has proven that N6-methyladenosine (m6A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m6A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m6A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m6A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m6A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m6A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m6A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m6A RNA modification in PRCC and provide essential guidance for therapeutic strategies.


2021 ◽  
Vol 12 ◽  
Author(s):  
Yucheng Qian ◽  
Jingsun Wei ◽  
Wei Lu ◽  
Fangfang Sun ◽  
Maxwell Hwang ◽  
...  

PurposeWe focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients’ prognosis and to establish a risk assessment model in colorectal cancer.SummaryThis article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients’ survival was firmly established.


2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Xu ◽  
Xingchen Li ◽  
Jingyi Zhou ◽  
Jianliu Wang

BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value.


2021 ◽  
Author(s):  
Renjie Liu ◽  
Guifu Wang ◽  
Chi Zhang ◽  
Dousheng Bai

Abstract Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.


2021 ◽  
Author(s):  
Yuan Li ◽  
Hao Huang ◽  
Jun Feng ◽  
Yulan Zhu ◽  
Tianwei Jiang ◽  
...  

Abstract BackgroundAlthough some advanced colorectal cancer (CRC) patients could select immunotherapy, but still most microsatellite stability (MSS) CRC patients did not respond. Our present study aims to set up a novel system for prognostic prediction and immunotherapeutic responsiveness for MSS CRC patients.MethodsUnivariable Cox regression survival analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were performed to identify prognostic genes and establish immune risk signatures. Multivariate Cox regression analysis was performed to verify whether these clinical features could predict prognosis. R package was used to analyze the relationship between the immune-related risk model and these immune cells, effector molecules, and immune checkpoints.ResultsWe constructed an immune-related signature and verified its predictive capability. Immune-related signature included 12 differentially expressed IRGs (12 DE IR MSSGs), including CXCL1, CD36, FABP4, MS4A2, NRG1, VGF, GRP, HDC, XCL1, NGF, MAGEA1, and IL13. The signature consisting of 12 DE IR MSSGs was an independent and effective prognostic factor for the overall survival of CRC patients. In addition, the signature consisting of 12 DE IR MSSGs reflected the infiltration characteristics of different immunocytes in tumor immune microenvironment. The signature consisting of 12 DE IR MSSGs also had a significant correlation with immune checkpoint molecules.


2021 ◽  
Author(s):  
Shuai Zhang ◽  
Jiali Lv ◽  
Bingbing Fan ◽  
Zhe Fan ◽  
Chunxia Li ◽  
...  

ABSTRACTBackgroundThe tumor immune microenvironment (TIME) plays a key role in occurrence, progression and prognosis of colorectal cancer (CRC). However, the genetic and epigenetic alterations and potential mechanisms in the TIME of CRC are still unclear.MethodsWe investigated the immune-related differences in three types of genetic or epigenetic alterations (gene expression, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and the immune-related biological processes by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step method based on LASSO regression and Cox regression was used to construct the prognostic prediction model. Cross validation was performed to validate the model.ResultsA total of 1,745 differentially expressed genes, 178 differentially mutated genes and 1,961 differentially methylation probes were identified between the high-immunity group and the low-immunity group. We retained 15 genetic and epigenetic variables after using LASSO regression and Cox regression. For the prognostic predictions on the TCGA profiles, the performance of the model with 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.861, 0.797, and 0.875. Finally, the overall risk score model was constructed based on genetic, epigenetic, demographic and clinical characteristics, which comprised 18 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.865), 3-year (AUC = 0.839), and 5-year (AUC = 0.914) survival predictions. Kaplan-Meier survival analysis demonstrated that the overall survival of the high-risk group was significantly poorer compared with the low-risk group. Prognostic nomogram, calibration plot and cross validation showed excellent predictive performance.ConclusionsOur study provides a new clue to explore the TIME of CRC patients in genetic and epigenetic aspects. Meanwhile, the prognostic model also has clinical prognostic value and may provide new indicators for the treatment of CRC patients.


2021 ◽  
Author(s):  
Yonggan Xue ◽  
Bobin Ning ◽  
Hongyi Liu ◽  
Baoqing JIa

Abstract Background Colorectal cancer (CRC) remains one of the most common malignancies across the world, threatening almost millions of lives every year and increasingly adding the social-economical burden. Thus far, a biomarker, which can comprehensively predict the survival outcomes, clinical characteristics, and therapeutic sensitivity, is still lacking. Results This study established a pair-risk model, together with, an exp-risk model to predict biological characteristics of CRC based on immune-related lncRNA (irlncRNA) expression patterns. We retrieved transcriptomic data of CRC, including 473 tumor samples and 41 normal samples, and identified 739 irlncRNA through co-expression analysis, and constructed irlncRNA pairs. After integrating with clinical survival data, we established an 11 irlncRNA pairs signature using Lasso regression analysis. We next drew the 1-, 5-, 10-year curve line of receiver operating characteristic (ROC), calculated the areas under the curve (AUC), and recognized the optimal cutoff point. Patients with CRC were stratified into high- and low-risk groups based on the optimal cutoff value. Then, we validated the pair-risk model in terms of the survival outcomes of the patients. Moreover, we tested the reliability of the pair-risk model for predicting tumor aggressiveness and therapeutic responsiveness of CRC. Additionally, we reemployed the 11 of irlncRNAs involved in the pair-risk model to constructed an expression risk model that was also highly predictive of prognostic outcomes of CRC patients. Importantly, combining the pair-risk model and exp-risk model yielded a more robust approach for predicting the survival outcomes of patients with CRC. Conclusions We suggest that the irlncRNA-based risk models can be utilized as prognostic tools to predict survival outcomes and clinical characteristics and guide treatment regimens of CRC.


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