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. 2023 Mar 30:13:1143876.
doi: 10.3389/fonc.2023.1143876. eCollection 2023.

Clinical characterization and genomic landscape of gynecological cancers among patients attending a Chinese hospital

Cen Jiang  1 , Yiyi Lu  1 , Hua Liu  2 , Gang Cai  1 , Zhao Peng  3 , Weiwei Feng  2 , Lin Lin  1
Affiliations

Clinical characterization and genomic landscape of gynecological cancers among patients attending a Chinese hospital

Cen Jiang et al. Front Oncol. .

Abstract

Background: Gynecological cancers are the most lethal malignancies among females, most of which are associated with gene mutations. Few studies have compared the differences in the genomic landscape among various types of gynecological cancers. In this study, we evaluated the diversity of mutations in different gynecological cancers.

Methods: A total of 184 patients with gynecological cancer, including ovarian, cervical, fallopian tube, and endometrial cancer, were included. Next-generation sequencing was performed to detect the mutations and tumor mutational burden (TMB). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were also conducted.

Results: We found that 94.57% of patients had at least one mutation, among which single nucleotide variants, insertions and InDels were in the majority. TP53, PIK3CA, PTEN, KRAS, BRCA1, BRCA2, ARID1A, KMT2C, FGFR2, and FGFR3 were the top 10 most frequently mutated genes. Patients with ovarian cancer tended to have higher frequencies of BRCA1 and BRCA2 mutations, and the frequency of germline BRCA1 mutations (18/24, 75.00%) was higher than that of BRCA2 (11/19, 57.89%). A new mutation hotspot in BRCA2 (I770) was firstly discovered among Chinese patients with gynecological cancer. Patients with TP53, PIK3CA, PTEN, and FGFR3 mutations had significantly higher TMB values than those with wild-type genes. A significant cross was discovered between the enriched KEGG pathways of gynecological and breast cancers. GO enrichment revealed that the mutated genes were crucial for the cell cycle, neuronal apoptosis, and DNA repair.

Conclusion: Various gynecological cancer types share similarities and differences both in clinical characterization and genomic mutations. Taken together with the results of TMB and enriched pathways, this study provided useful information on the molecular mechanism underlying gynecological cancers and the development of targeted drugs and precision medicine.

Keywords: BRCA1; BRCA2; FGFR3; TMB; gynecological cancer; next-generation sequencing.

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Conflict of interest statement

ZP is an employee of Genecast Biotechnology Co., Ltd., Wuxi, China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The proportion of patients with and without mutations. Next-generation sequencing was performed among 184 gynecological cancer patients to detect genomic alterations. OC, ovarian cancer; CC, cervical cancer; EC, endometrial cancer; FTC, fallopian tube cancer; LGSC, low-grade serous cancer; HGSC, high-grade serous cancer.
Figure 2
Figure 2
Landscapes of the top 10 most frequently mutated genes among 184 patients with gynecological cancer. Next-generation sequencing was performed to detect mutations. Frequencies of mutated genes are listed on the left, and mutation types are shown on the right, with annotation bars at the bottom. OC, ovarian cancer; CC, cervical cancer; EC, endometrial cancer; FTC, fallopian tube cancer.
Figure 3
Figure 3
The proportion of BRCA1 (A) and BRCA2 (B) mutations. Somatic and germline mutations were respectively detected by next-generation sequencing among the different types of gynecological cancer. OC, ovarian cancer; CC, cervical cancer; EC, endometrial cancer; FTC, fallopian tube cancer.
Figure 4
Figure 4
Association of gene mutations with tumor mutational burden (TMB). TMB values of patients with TP53 (A), PIK3CA (B), PTEN (C), and FGFR3 (D) mutations are respectively compared with those of patients with wild-type genes. A box plot was used to show the minimum, maximum, median, and interquartile range of the TMB values. The blue box represents patients with mutations, and the red box represents patients with wild-type genes. * p < 0.05.
Figure 5
Figure 5
KEGG (A) and GO (B) enrichment among 184 patients with gynecological cancer. Next-generation sequencing was performed to detect mutations. Overlap of SNVs- and insertions and InDels-associated 29 genes was conducted for KEGG and GO enrichment. The size of each dot in KEGG enrichment indicates the number of genes included. The bigger the dot, the more genes are involved in the pathway. The top five GO enrichments are listed according to their p values.
Figure 6
Figure 6
Interaction between mutated genes. Overlap analysis of SNVs- and insertions and InDels-associated 29 genes were performed on STRING (A) and Chilibot (B). A confidence of 0.7 was used to analyze functional interactions on the STRING website. “Ovarian cancer”, “cervical cancer”, “fallopian tube cancer”, and “endometrial cancer” were used as keywords on Chilibot to analyze the interaction between genes and different gynecological cancers, excluding abstract co-occurrence relationships.
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