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Cited by 1,381Gene expression and cancer classificationBreast Cancer Treatment StudiesCancer Genomics and Diagnostics

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Pancancer survival analysis of cancer hallmark genes

Open access
Nagy, Ádám, Munkácsy, Gyöngyi, Győrffy, Balázs·2021·Scientific Reports
DOI 10.1038/s41598-021-84787-5PMC PMC7961001
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Abstract

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan–Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR 4.26, p < 1E−16), RUNX1 (sustaining proliferative signaling, HR 2.96, p = 3.1E−10) and SERPINE1 (inducing angiogenesis, HR 3.36, p = 1.5E−12) in low grade glioma, CDK1 (cell death resistance, HR = 5.67, p = 2.1E−10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR 0.38, p = 2.4E−05) and EREG (enabling replicative immortality, HR 3.23, p = 2.1E−07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR 0.45, p = 2.8E−07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR 1.81, p = 5.8E−05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

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Figure 1 Overview of cutoff determination and survival distribution in the database. The determination of the best cutoff value in the survival analysis demonstrated with the CDK1 gene in kidney papillary carcinoma (A) and ovarian cancer (B). Survival time characteristics of tumors with observed events (C).
Figure 1 Overview of cutoff determination and survival distribution in the database. The determination of the best cutoff value in the survival analysis demonstrated with the CDK1 gene in kidney papillary carcinoma (A) and ovarian cancer (B). Survival time characteristics of tumors with observed events (C).CC BY · PubMed Central
Figure 2 The prognostic power of cancer hallmark genes.
Figure 2 The prognostic power of cancer hallmark genes.CC BY · PubMed Central
Figure 3 Effect of hallmark signatures (A) and tumor mutation burden (C) on patient survival. Summary of the significant prognostic hallmark signatures in different types of tumors (B).
Figure 3 Effect of hallmark signatures (A) and tumor mutation burden (C) on patient survival. Summary of the significant prognostic hallmark signatures in different types of tumors (B).CC BY · PubMed Central
Figure 4 Best performing genes in at least 10 distinct tumor types.
Figure 4 Best performing genes in at least 10 distinct tumor types.CC BY · PubMed Central

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Metadata

Authors
Nagy, Á., Munkácsy, G., & Győrffy, B.
Affiliations
Semmelweis University (HU)
Corresponding author
Győrffy, Balázs
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2021-03-15
Volume
11
Issue
1
Pages
6047-6047
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-021-84787-5
PMC
PMC7961001
OpenAlex
W3139511935
Cited by
1,381
References
40
Citations by year
2026: 38 · 2025: 84 · 2024: 145 · 2023: 504 · 2022: 311 · 2021: 298
FWCI
101.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Semmelweis Egyetem
  • Innovációs és Technológiai Minisztérium (ÚNKP-19-3, 2018-2.1.17-TET-KR-00001)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
prognostic gene signature, survival analysis, genomic instability, invasion and metastasis, replicative immortality, cell death resistance, angiogenesis, tumor suppressor, BRCA1, RUNX1, SERPINE1, CDK1, E2F1, epiregulin, c-Myc, clear cell renal cell carcinoma, low-grade glioma, cervical cancer, bladder cancer, thyroid cancer, glioblastoma, tissue specificity, targeted therapy
MeSH terms
RNA-Seq, Humans, Neoplasms, Biomarkers, Tumor, Survival Rate, Disease-Free Survival, Databases, Nucleic Acid, Kaplan-Meier Estimate

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