Hiranuma Kengo, Asami Yuka, Kato Mayumi Kobayashi, Murakami Naoya, Shimada Yoko, Matsuda Maiko, Yazaki Shu, Fujii Erisa, Sudo Kazuki, Kuno Ikumi, Komatsu Masaaki, Hamamoto Ryuji, Makinoshima Hideki, Matsumoto Koji, Ishikawa Mitsuya, Kohno Takashi, Terao Yasuhisa, Itakura Atsuo, Yoshida Hiroshi, Shiraishi Kouya, Kato Tomoyasu
Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan.
Department of Obstetrics and Gynecology, Juntendo University Faculty of Medicine, Tokyo, Japan.
Cancer Med. 2023 Sep;12(17):17835-17848. doi: 10.1002/cam4.6415. Epub 2023 Aug 3.
Although cervical cancer is often characterized as preventable, its incidence continues to increase in low- and middle-income countries, underscoring the need to develop novel therapeutics for this disease.This study assessed the distribution of fusion genes across cancer types and used an RNA-based classification to divide cervical cancer patients with a poor prognosis into subgroups.
RNA sequencing of 116 patients with cervical cancer was conducted. Fusion genes were extracted using StarFusion program. To identify a high-risk group for recurrence, 65 patients who received postoperative adjuvant therapy were subjected to non-negative matrix factorization to identify differentially expressed genes between recurrent and nonrecurrent groups.
We identified three cases with FGFR3-TACC3 and one with GOPC-ROS1 fusion genes as potential targets. A search of publicly available data from cBioPortal (21,789 cases) and the Center for Cancer Genomics and Advanced Therapeutics (32,608 cases) showed that the FGFR3 fusion is present in 1.5% and 0.6% of patients with cervical cancer, respectively. The frequency of the FGFR3 fusion gene was higher in cervical cancer than in other cancers, regardless of ethnicity. Non-negative matrix factorization identified that the patients were classified into four Basis groups. Pathway enrichment analysis identified more extracellular matrix kinetics dysregulation in Basis 3 and more immune system dysregulation in Basis 4 than in the good prognosis group. CIBERSORT analysis showed that the fraction of M1 macrophages was lower in the poor prognosis group than in the good prognosis group.
The distribution of FGFR fusion genes in patients with cervical cancer was determined by RNA-based analysis and used to classify patients into clinically relevant subgroups.
尽管宫颈癌通常被认为是可预防的，但在低收入和中等收入国家，其发病率仍在持续上升，这凸显了开发针对该疾病的新型治疗方法的必要性。本研究评估了融合基因在不同癌症类型中的分布，并使用基于RNA的分类方法将预后不良的宫颈癌患者分为不同亚组。
对116例宫颈癌患者进行了RNA测序。使用StarFusion程序提取融合基因。为了确定复发的高危组，对65例接受术后辅助治疗的患者进行非负矩阵分解，以确定复发组和未复发组之间的差异表达基因。
我们确定了3例携带FGFR3-TACC3融合基因的病例和1例携带GOPC-ROS1融合基因的病例作为潜在靶点。对来自cBioPortal（21,789例）和癌症基因组学与先进治疗中心（32,608例）的公开数据进行搜索发现，FGFR3融合分别存在于1.5%和0.6%的宫颈癌患者中。无论种族如何，FGFR3融合基因在宫颈癌中的频率均高于其他癌症。非负矩阵分解确定患者被分为四个基础组。通路富集分析表明，与预后良好组相比，基础3组中细胞外基质动力学失调更多，基础4组中免疫系统失调更多。CIBERSORT分析表明，预后不良组中M1巨噬细胞的比例低于预后良好组。
通过基于RNA的分析确定了FGFR融合基因在宫颈癌患者中的分布，并用于将患者分类为临床相关的亚组。
