This preprint is under consideration at Cancer Cell International. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Primary research
Zhentao Yu
Tianjin Cancer Institute: Tianjin Tumor Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5785-8492
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Esophageal squamous cell carcinoma (ESCC), the major subtype of esophageal cancer in China, has a dismal prognosis. Tumor hypoxia, a typical characteristic of many solid tumors, leads to tumor invasion and poor prognosis. We aimed to develop a hypoxia-based gene signature to assist in the diagnosis and prognosis prediction of ESCC.
Methods
We integrated gene expression files from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to identify potential hypoxia-related genes (HRGs). Euclidean-based consensus clustering was performed on the GSE53625 ESCC samples. Immune cell infiltration, differential expression, and Kaplan-Meier survival analyses were performed. Candidate modules were screened using weighted gene coexpression network analysis and then intersected with differentially expressed genes from clustered subgroups to construct the gene signatures that were verified in internal and external datasets. A nomogram was developed with risk scores and clinicopathological variables. Finally, immunohistochemistry was used to detect the protein expression level of relevant genes. Additionally, the relationships between risk scores and the tumor microenvironment were explored.
Results
A total of 5 potential differentially expressed HRGs and 6 critical HRGs were identified. A prognostic model was constructed by screening key HRGs, and higher risk scores were associated with poorer prognosis. The nomogram based on risk scores predicted the 1-, 3-, and 5-year survival rates well, thus determining prognostic risk stratification. The p53, Wnt, and hypoxia signaling pathways may be some of the regulatory mechanisms of hypoxia associated with the tumor microenvironment. Furthermore, the high expression of BGN and low expression of IL-18 in ESCC tissues was verified and was associated with a shorter survival time.
Conclusions
Our study systematically explored HRGs and determined the prognostic value of a 6-hypoxia gene signature. A prognostic model was developed and validated based on this, providing potential prognostic predictors and therapeutic targets for ESCC.
Keywords
esophageal squamous cell carcinoma, hypoxia, hypoxia-related genes, nomogram, prognosis
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This is a list of supplementary files associated with this preprint. Click to download.
- S.Fig.1.tif
Supplementary Figure 1. Heat maps were used display the differentially expressed genes of GEO dataset (A) and TCGA (B) dataset.
- S.Fig.2.tif
Supplementary Figure 2. Detailed results of the consistency clustering analysis.
- S.Fig.3.tif
Supplementary Figure 3. A heat map was used display the gene expression profiles of the samples sorted by cluster.
- S.Fig.4.tif
Supplementary Figure 4. Detailed results of the weighted gene co-expression network analysis (A-C). The results of module-feature relationship analysis between green module and consensus subgroup C1 (D) and consensus subgroup C2 (E), suggesting that the module is suitable for identifying the hub genes associated with C1/C2.
- S.Fig.5.tif
Supplementary Figure 5. Validating the prognostic hypoxia gene features in the remaining half GSE53625 dataset. The samples were assigned a risk score and ordered to determine whether the expression level (A) and survival time (B) varied systematically with the risk score. (C) Expression levels of the 6 HRGs based on risk scores. (D) Survival curve distribution of the risk score. (E) ROC curves and AUCs of risk score classifications. Abbreviations: HRGs, hypoxia related genes.
- S.Fig.6.tif
Supplementary Figure 6. Validating the prognostic hypoxia gene features in the entire GSE53625 dataset. The samples were assigned a risk score and ordered to determine whether the expression level (A) and survival time (B) varied systematically with the risk score. (C) Expression levels of the 6 HRGs based on risk scores. (D) Survival curve distribution of the risk score. (E) ROC curves and AUCs of risk score classifications. Abbreviations: HRGs, hypoxia related genes.
- S.Table.1.pdf
- S.Table.2.pdf
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This preprint is under consideration at Cancer Cell International. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Primary research
Zhentao Yu
Tianjin Cancer Institute: Tianjin Tumor Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5785-8492
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 02 Nov, 2021
No community comments so far
Reviewers invited
Invitations sent on 17 Nov, 2021
Editor invited
On 26 Oct, 2021
Editor assigned
On 25 Oct, 2021
Submission checks complete
On 24 Oct, 2021
First submitted
On 22 Oct, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Esophageal squamous cell carcinoma (ESCC), the major subtype of esophageal cancer in China, has a dismal prognosis. Tumor hypoxia, a typical characteristic of many solid tumors, leads to tumor invasion and poor prognosis. We aimed to develop a hypoxia-based gene signature to assist in the diagnosis and prognosis prediction of ESCC.
Methods
We integrated gene expression files from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to identify potential hypoxia-related genes (HRGs). Euclidean-based consensus clustering was performed on the GSE53625 ESCC samples. Immune cell infiltration, differential expression, and Kaplan-Meier survival analyses were performed. Candidate modules were screened using weighted gene coexpression network analysis and then intersected with differentially expressed genes from clustered subgroups to construct the gene signatures that were verified in internal and external datasets. A nomogram was developed with risk scores and clinicopathological variables. Finally, immunohistochemistry was used to detect the protein expression level of relevant genes. Additionally, the relationships between risk scores and the tumor microenvironment were explored.
Results
A total of 5 potential differentially expressed HRGs and 6 critical HRGs were identified. A prognostic model was constructed by screening key HRGs, and higher risk scores were associated with poorer prognosis. The nomogram based on risk scores predicted the 1-, 3-, and 5-year survival rates well, thus determining prognostic risk stratification. The p53, Wnt, and hypoxia signaling pathways may be some of the regulatory mechanisms of hypoxia associated with the tumor microenvironment. Furthermore, the high expression of BGN and low expression of IL-18 in ESCC tissues was verified and was associated with a shorter survival time.
Conclusions
Our study systematically explored HRGs and determined the prognostic value of a 6-hypoxia gene signature. A prognostic model was developed and validated based on this, providing potential prognostic predictors and therapeutic targets for ESCC.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This is a list of supplementary files associated with this preprint. Click to download.
- S.Fig.1.tif
Supplementary Figure 1. Heat maps were used display the differentially expressed genes of GEO dataset (A) and TCGA (B) dataset.
- S.Fig.2.tif
Supplementary Figure 2. Detailed results of the consistency clustering analysis.
- S.Fig.3.tif
Supplementary Figure 3. A heat map was used display the gene expression profiles of the samples sorted by cluster.
- S.Fig.4.tif
Supplementary Figure 4. Detailed results of the weighted gene co-expression network analysis (A-C). The results of module-feature relationship analysis between green module and consensus subgroup C1 (D) and consensus subgroup C2 (E), suggesting that the module is suitable for identifying the hub genes associated with C1/C2.
- S.Fig.5.tif
Supplementary Figure 5. Validating the prognostic hypoxia gene features in the remaining half GSE53625 dataset. The samples were assigned a risk score and ordered to determine whether the expression level (A) and survival time (B) varied systematically with the risk score. (C) Expression levels of the 6 HRGs based on risk scores. (D) Survival curve distribution of the risk score. (E) ROC curves and AUCs of risk score classifications. Abbreviations: HRGs, hypoxia related genes.
- S.Fig.6.tif
Supplementary Figure 6. Validating the prognostic hypoxia gene features in the entire GSE53625 dataset. The samples were assigned a risk score and ordered to determine whether the expression level (A) and survival time (B) varied systematically with the risk score. (C) Expression levels of the 6 HRGs based on risk scores. (D) Survival curve distribution of the risk score. (E) ROC curves and AUCs of risk score classifications. Abbreviations: HRGs, hypoxia related genes.
- S.Table.1.pdf
- S.Table.2.pdf
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