It is generally accepted that colorectal cancer (CRC) originates from cancer stem cells (CSCs), which are responsible for CRC progression, metastasis and therapy resistance. The high heterogeneity of CSCs has precluded clinical application of CSC-targeting therapy. Here, we aimed to characterize the stemness landscapes and screen for certain patients more responsive to immunotherapy.Twenty-six stem cell gene sets were acquired from StemChecker database. Consensus clustering algorithm was applied for stemness subtypes identification on 1,467 CRC samples from TCGA and GEO databases. The differences in prognosis, tumor microenvironment (TME) components, therapy responses were evaluated among subtypes. Then, the stemness-risk model was constructed by weighted gene correlation network analysis (WGCNA), Cox regression and random survival forest analyses, and the most important marker was experimentally verified.Based on single-sample gene set enrichment analysis (ssGSEA) enrichments scores, CRC patients were classified into three subtypes (C1, C2 and C3). C3 subtype exhibited the worst prognosis, highest macrophages M0 and M2 infiltrations, immune and stromal scores, and minimum sensitivity to immunotherapies, but was more sensitive to drugs like Bosutinib, Docetaxel, Elesclomol, Gefitinib, Lenalidomide, Methotrexate and Sunitinib. The turquoise module was identified by WGCNA that it was most positively correlated with C3 but most negatively with C2, and five hub genes in turquoise module were identified for stemness model construction. CRC patients with higher stemness scores exhibited worse prognosis, more immunosuppressive components in TME and lower immunotherapeutic responses. Additionally, the model's immunotherapeutic prediction efficacy was further confirmed from two immunotherapy cohorts (anti-PD-L1 in IMvigor210 cohort and anti-PD-1 in GSE78220 cohort). Mechanistically, Gene Set Enrichment Analysis (GSEA) results revealed high stemness score group was enriched in interferon gamma response, interferon alpha response, P53 pathway, coagulation, apoptosis, KRAS signaling upregulation, complement, epithelial-mesenchymal transition (EMT) and IL6-mediated JAK-STAT signaling gene sets.Our study characterized three stemness-related subtypes with distinct prognosis and TME patterns in CRC patients, and a 5-gene stemness-risk model was constructed by comprehensive bioinformatic analyses. We suggest our stemness model has prospective clinical implications for prognosis evaluation and might facilitate physicians selecting prospective responders for preferential use of current immune checkpoint inhibitors. 

/pdf/characterization-of-stem-cell-landscape-and-identification-1eqpkabd.pdf

Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in colorectal cancer

Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The aim of this research study was to identify differentially expressed immune-related genes (DEIRGs) and establish a Cox prediction model for the evaluation of prognosis in patients with non-small cell lung cancer (NSCLC). Transcription expression data, immune gene data, and tumor transcription factor data from The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and the Cistrome Cancer database were analyzed to detect differentially expressed genes (DEGs), DEIRGs, and differentially expressed transcription factors (DETFs). Multivariate Cox regression analysis was used to obtain potential DEIRGs as independent prognostic factors. Oncomine, The Human Protein Atlas (HPA), TIMER databases were performed to validate the mRNA and protein expression level of DEIRGs. TIMER database was performed to explore the immunocytes infiltration of DEIRGs. In total, 7448 DEGs, 536 DEIRGs, 87 DETFs were identified from 1,037 NSCLC tissues and 108 normal tissues in TCGA database. Fifteen-DEIRG signatures (THBS1, S100P, S100A16, DLL4, CD70, DKK1, IL33, NRTN, PDGFB, STC2, VGF, GCGR, HTR3A, LGR4, SHC3) could be perceived as independent prognostic factors for predicting the overall survival of patients with NSCLC (P = 4.89e--09). Immune cell correlation analysis showed that neutrophils and b cells were positively and negatively correlated with the riskscore of the prediction model, respectively. Our study identified a Cox prediction model based on DEIRGs to predict the overall survival of patients with NSCLC. The immunocyte infiltration analysis provided a novel horizon for monitoring the status of the NSCLC immune microenvironment.

Identification and validation of the prognostic value of immune-related genes in non-small cell lung cancer.

Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan-Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan-Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein-protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.

/pdf/construction-and-validation-of-a-metabolic-risk-model-4sws6y3ae0.pdf

Construction and validation of a metabolic risk model predicting prognosis of colon cancer

/pdf/characterization-of-stem-cell-landscape-and-identification-2ue7ae4e.pdf

Purpose The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. Methods High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. Results A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). Conclusion The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

/pdf/a-novel-prognostic-risk-score-model-based-on-immune-related-c4vpp7n7yr.pdf

A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer.

Cancer stem cells (CSCs) with self-renewal play an important role in tumor initiation and progression and are associated with drug resistance in cancer therapy. Here, we investigated the characteristics of stem cell-related genes in colorectal cancer (CRC) based on datasets from The Cancer Genome Atlas (TCGA) and Oncomine. We found that the stemness indices were significantly overexpressed in CRC tissues and were associated with patient survival. Weighted gene co-expression network analysis (WGCNA) was performed to determine the modules of stemness and featured genes. Significant modules and 8 genes (BUB1, BUB1B, CHEK1, DNA2, KIF23, MCM10, PLK4, and TTK) were selected according to the inclusion criteria. Expression analyses of transcription and protein levels confirmed internal correlation and their relevance with the tumor. Functional analysis of these genes demonstrated their enrichment in pathways, including checkpoint, chromosomal region and protein serine/threonine kinase activity. These results suggested that the characteristics of the featured genes fit well with CRC pathology and could provide new strategies for individual prevention and treatment.

Characteristic Analysis of Featured Genes Associated With Stemness Indices in Colorectal Cancer.

Heterogeneity in patients with colorectal cancer (CRC) leads to different strategies in clinical decision making. Identifying distinctive subgroups in patients contributes to develop more individualized treatments. This study constructed a novel prediction model for the prognosis of CRC patients based on the value of risk score combining the expression status of immune-related genes and coefficients. In this study, we built an interactive network of prognosis-related immune genes and transcription factors and adopted several methods to verify the accuracy of model. Moreover, we assessed the correlation between risk score and immune infiltration. The results suggested that the model was well fit and the risk score could be an independent predictive factor for CRC patients. This model has high application value in the clinic.

Assessment for Risk Status of Colorectal Cancer Patients: A Novel Prediction Model Based on Immune-Related Genes.

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Characteristic Analysis of Featured Genes Associated With Stemness Indices in Colorectal Cancer.

Assessment for Risk Status of Colorectal Cancer Patients: A Novel Prediction Model Based on Immune-Related Genes.