Integrative stemness characteristics associated with prognosis and the immune microenvironment in esophageal cancer

Yi, Lilan; Huang, Ping; Zou, Xiaofang; Guo, Longhua; Gu, Yang; Wen, Chunling; Wu, Guowu

doi:10.1016/j.phrs.2020.105144

Cited by 41 publications

(32 citation statements)

References 40 publications

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“…Choi et al reported that the expression level of EMT markers (E-cadherin, b-catenin, Snail, vimentin) was correlated with stem cell marker expression level (34). CSC and EMT processes were proven to cause disease progression, poor prognosis, and drug resistance in lung cancer, esophageal cancer, breast cancer, and also GC (35)(36)(37)(38). Thus, considering the common pathways and mechanisms two processes share and the same clinical impact they have, identification of transcriptional markers for these two processes will achieve a better prognosis and may give guidance to targeted therapies in GC patients.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

Jia

Chen

et al. 2022

Front. Oncol.

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BackgroundGastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.MethodsThe gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.ResultsA prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.ConclusionsOur SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.

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Section: Discussionmentioning

confidence: 99%

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

Jia

Chen

et al. 2022

Front. Oncol.

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“…The associations between SRG expression and immune-infiltration or clinical outcomes have been detected before but has not been explored in HCC [ 10 – 12 ]. In our analysis, based on the expression profile of prognostic SRGs, we demonstrated that the Cluster 2 subtype is associated with the highest infiltration of immune cells, and we identified that tumors with more active SRG expression have higher immune-infiltration in tumor microenvironment (TME) and significantly worse prognosis, which means that increased expression of stemness-related genes and increased immune infiltration may contribute to a poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

et al. 2021

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Background Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. Methods Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. Results We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. Conclusions Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

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“…To evaluate the prognostic associations, Kaplan-Meier plots for overall survival (OS) or disease-free survival (DFS) in high-or low-immune score groups were depicted based on the optimized immune score value (−1.322856 and −11.99095 for OS and DFS, respectively) of each patient, with a log-rank test for statistical significance. Besides, given that the IME might be correlated with tumor stemness (Yi et al, 2020), we also obtained the available mRNA expression-based stem index (mRNAsi) of 528 EC patients as previously reported (Malta et al, 2018), following with the exploration of the spearman correlation between immune score and miRNAsi.…”

Section: Data Acquisition Immune Score Generation and Clinical Relamentioning

confidence: 99%

Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis

Guo

Tang

Zhang

et al. 2021

Front. Mol. Biosci.

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Background: Endometrial cancer (EC) is one of the most lethal gynecological cancers around the world. The aim of this study is to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC.Methods: RNA-seq data and clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify the interesting module and potential key genes concerning the immune score. The expression patterns of the key genes were then verified via the GEPIA database. Finally, CIBERSORT was applied to evaluate the relative abundances of 22 immune cell types in EC.Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WGCNA indicated that the black module was significantly correlated with the immune score. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes (APOL3, C10orf54, CLEC2B, GIMAP1, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GYPC, IFFO1, TAGAP) were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC.Conclusion: In this study, 11 key genes showed abnormal expressions and strong correlations with immune infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

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Integrative stemness characteristics associated with prognosis and the immune microenvironment in esophageal cancer

Cited by 41 publications

References 40 publications

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis

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