Abstract
Background: 5-Methylcytidine (m5C) is the most common RNA modification and plays an important role in multiple tumors including cervical cancer (CC). We aimed to develop a novel gene signature by identifying m5C modification subtypes of CC to better predict the prognosis of patients.Methods: We obtained the expression of 13 m5C regulatory factors from The Cancer Genome Atlas (TCGA all set, 257 patients) to determine m5C modification subtypes by the “nonnegative matrix factorization” (NMF). Then the “limma” package was used to identify differentially expressed genes (DEGs) between different subtypes. According to these DEGs, we performed Cox regression and Kaplan-Meier (KM) survival analysis to establish a novel gene signature in TCGA training set (128 patients). We also verified the risk prediction effect of gene signature in TCGA test set (129 patients), TCGA all set (257 patients) and GSE44001 (300 patients). Furthermore, a nomogram including this gene signature and clinicopathological parameters was established to predict the individual survival rate. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, colony formation, migration and invasion assays.Results: Based on consistent clustering of 13 m5C-modified genes, CC was divided into two subtypes (C1 and C2) and the C1 subtype had a worse prognosis. The 4-gene signature comprising FNDC3A, VEGFA, OPN3 and CPE was constructed. In TCGA training set and three validation sets, we found the prognosis of patients in the low-risk group was much better than that in the high-risk group. A nomogram incorporating the gene signature and T stage was constructed, and the calibration plot suggested that it could accurately predict the survival rate. The expression levels of FNDC3A, VEGFA, OPN3 and CPE were all high in cervical cancer tissues. Downregulation of FNDC3A, VEGFA or CPE expression suppressed the proliferation, migration and invasion of SiHa cells.Conclusions: Two m5C modification subtypes of CC were identified and then a 4-gene signature was established, which provide new feasible methods for clinical risk assessment and targeted therapies for CC.
Highlights
It is estimated that 310,000 people die of cell composition (CC) every year worldwide, CC is fourth most common cause of cancer-related death in women and constitutes a major public health problem (Bray et al, 2018; Arbyn et al, 2020)
The optimal number of clusters was determined according to cophenetic, rss and silhouette analyses, the optimal number of clusters was 2 (Figures 2A,B)
The KM curve revealed that overall survival (OS) rates of the C1 and C2 subtypes were significantly different (p < 0.05), and the prognosis of the C1 group was worse than that of the C2 group (Figure 2D)
Summary
It is estimated that 310,000 people die of CC every year worldwide, CC is fourth most common cause of cancer-related death in women and constitutes a major public health problem (Bray et al, 2018; Arbyn et al, 2020). Human papillomavirus (HPV) infection is a major risk factor for CC, with approximately 90% of cases occurring in lowincome and middle-income countries lacking organized screening and HPV vaccination programs (Lagheden et al, 2018; Cohen et al, 2019). Patients with CC often have social difficulties, constipation, diarrhea, severe lymphedema, menopausal symptoms and major financial problems (Cohen et al, 2019). The conventional treatment of CC includes radiotherapy, chemotherapy and surgery. Patients with advancedstage disease are prone to resistance to radiotherapy and chemotherapy.
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