Advanced search
Publication Cover
Expert Review of Anticancer Therapy Latest Articles
Submit an article Journal homepage
92
Views
0
CrossRef citations to date
0
Altmetric
Original Research

Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis

Min Chena School of Information Engineering, Henan University of Science and Technology, Luoyang, China;b State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;c Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;d Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;e Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;f College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China;g Medical College, Henan University of Science and Technology, Luoyang, ChinaView further author information
,
Yijun Qib State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;c Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;d Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;e Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;f College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China;g Medical College, Henan University of Science and Technology, Luoyang, ChinaView further author information
,
Shenghua Zhangb State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;c Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;d Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;e Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;f College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China;g Medical College, Henan University of Science and Technology, Luoyang, ChinaView further author information
,
Yubo Dub State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;c Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;d Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;e Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;f College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China;g Medical College, Henan University of Science and Technology, Luoyang, ChinaView further author information
,
Haodong Chengb State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;c Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;d Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;e Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;f College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China;g Medical College, Henan University of Science and Technology, Luoyang, ChinaView further author information
&
Shegan Gaoa School of Information Engineering, Henan University of Science and Technology, Luoyang, China;b State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;c Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;d Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;e Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China;f College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China;g Medical College, Henan University of Science and Technology, Luoyang, ChinaCorrespondence[email protected]
View further author information
Received 29 Feb 2024, Accepted 19 Jun 2024, Published online: 17 Jul 2024

Figures & data

Table 1. Primers of genes.

Figure 1. Biological processes and mutational landscapes involved in DEPCDGs in ESCC. (a) Volcano plot of 4497 DEGs. Orange in the plot is up-regulated DEGs and green is down-regulated DEGs. (b) Wayne diagram of PCD genes and DEGs taking the intersection of 340 DEPCDGs obtained. (c) Upregulation of biological processes in which DEPCDGs are involved. (d) Down-regulation of biological processes involved in DEPCDGs. (e) Waterfall plot showing the mutational landscape of the top 20 DEPCDGs in terms of mutation frequency.

Figure 1. Biological processes and mutational landscapes involved in DEPCDGs in ESCC. (a) Volcano plot of 4497 DEGs. Orange in the plot is up-regulated DEGs and green is down-regulated DEGs. (b) Wayne diagram of PCD genes and DEGs taking the intersection of 340 DEPCDGs obtained. (c) Upregulation of biological processes in which DEPCDGs are involved. (d) Down-regulation of biological processes involved in DEPCDGs. (e) Waterfall plot showing the mutational landscape of the top 20 DEPCDGs in terms of mutation frequency.

Figure 2. Prognostic modeling of PCD-related features in ESCC. (a) Trends in the coefficient trajectories of the independent variables in the LASSO COX analysis. (b) Confidence interval for the penalty parameter λ based on 10-fold cross-validation. (c) LASSO COX regression coefficients for prognosis-related DEPCDGs. (d-f) K-M curves for patients in the high/low RiskScore group in TCGA-ESCC, training set, and validation set. (g-i) ROC curves for TCGA-ESCC, training set, and RiskScore in the validation set to predict 1-, 3-, and 5-year patient survival.

Figure 2. Prognostic modeling of PCD-related features in ESCC. (a) Trends in the coefficient trajectories of the independent variables in the LASSO COX analysis. (b) Confidence interval for the penalty parameter λ based on 10-fold cross-validation. (c) LASSO COX regression coefficients for prognosis-related DEPCDGs. (d-f) K-M curves for patients in the high/low RiskScore group in TCGA-ESCC, training set, and validation set. (g-i) ROC curves for TCGA-ESCC, training set, and RiskScore in the validation set to predict 1-, 3-, and 5-year patient survival.

Figure 3. Correlation of RiskScore with clinical phenotypes. (a) Age distribution of samples in the high/low RiskScore. (b) Distribution of the number of cigarettes smoked per day for samples in the high/low RiskScore. (c) Barretts esophagus pathology for samples in high/low RiskScore. (d) The alcohol history of the samples in the high/low RiskScore. (e-h) T stage, N stage, M stage, Clinical stage distribution of samples in high/low RiskScore.

Figure 3. Correlation of RiskScore with clinical phenotypes. (a) Age distribution of samples in the high/low RiskScore. (b) Distribution of the number of cigarettes smoked per day for samples in the high/low RiskScore. (c) Barretts esophagus pathology for samples in high/low RiskScore. (d) The alcohol history of the samples in the high/low RiskScore. (e-h) T stage, N stage, M stage, Clinical stage distribution of samples in high/low RiskScore.

Figure 4. Independence of Nomogram and RiskScore. (a,b) Univariate COX and multivariate COX analyses integrating RiskScore, T stage, N stage, M stage, clinical stage, alcohol history, cigarettes per day, age, and Barrett's esophagus. (c) Nomogram predicting 1-, 3-, and 5-year survival in ESCC patients. (d) Calibration curves for Nomogram prediction of 1-, 3-, and 5-year survival and actual survival in ESCC patients.

Figure 4. Independence of Nomogram and RiskScore. (a,b) Univariate COX and multivariate COX analyses integrating RiskScore, T stage, N stage, M stage, clinical stage, alcohol history, cigarettes per day, age, and Barrett's esophagus. (c) Nomogram predicting 1-, 3-, and 5-year survival in ESCC patients. (d) Calibration curves for Nomogram prediction of 1-, 3-, and 5-year survival and actual survival in ESCC patients.

Figure 5. Correlation between RiskScore and immune cell infiltration microenvironment. (a) ssGSEA results for 28 immune cell activity scores in the high/low RiskScore group. (b) CIBERSORT results for the abundance of 22 immune cell infiltrates in the high/low RiskScore group.

Figure 5. Correlation between RiskScore and immune cell infiltration microenvironment. (a) ssGSEA results for 28 immune cell activity scores in the high/low RiskScore group. (b) CIBERSORT results for the abundance of 22 immune cell infiltrates in the high/low RiskScore group.

Figure 6. Correlation between RiskScore and immunotherapy. (a) TIDE score for the high/low RiskScore group. (b) Expression levels of immune checkpoint genes in the high/low RiskScore group. (c) Proportion of patients with high/low RiskScore in the CR/PR and SD/PD groups in the IMvigor210 cohort. (d) K-M survival curves for the high/low RiskScore group in the IMvigor210 cohort.

Figure 6. Correlation between RiskScore and immunotherapy. (a) TIDE score for the high/low RiskScore group. (b) Expression levels of immune checkpoint genes in the high/low RiskScore group. (c) Proportion of patients with high/low RiskScore in the CR/PR and SD/PD groups in the IMvigor210 cohort. (d) K-M survival curves for the high/low RiskScore group in the IMvigor210 cohort.
Supplemental material

Figure S1.jpg

Download JPEG Image (785.2 KB)

Description of supplementary materials.docx

Download MS Word (14.5 KB)

Table S2.xlsx

Download MS Excel (6.4 KB)

Table S1.xlsx

Download MS Excel (7.1 KB)

Data availability statement

The full analysis of raw data is available in the Github (https://github.com/1MinChen/Raw-data-for-article.git).

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.