Development of a novel transcription factor signature for accurate cervical cancer prognosis

Cervical cancer (CC) is a leading cause of cancer-related deaths in women. During tumor development, transcriptional factors regulate the transcription of proto-oncogenes and tumor suppressor genes. We examined the possibility of using transcription factors as prognostic biomarkers for patients with cervical cancer. Single-cell RNA-sequencing data were downloaded from the Gene Expression Omnibus database to identify specific activated transcription factors in different types of cells from CC. Publicly available bulk RNA-sequencing and clinical data of CC were obtained to identify associated prognostic transcription factors using survival analysis and the random survival forest methods. Accuracy and effectiveness of the established transcription factor-related predictive random survival forest model were verified using training and test datasets. We identified specific activated transcription factors in tissue cells of cervical cancer. A 3-transcription factors (PBX4 (PBX Homeobox 4), EBF2 (EBF Transcription Factor 2) and ZNF696 (Zinc Finger Protein 696)) prognostic signature for patients with cervical cancer was constructed showing good survival prediction. Gene function enrichment analysis indicated a correlation between the prognostic characteristics and different signaling pathways associated with cancer. Using the random survival forest model based on the 3-transcription factor signature, patients with cervical cancer were stratified into low- and high-risk groups with significant variations in overall survival (p < 0.001). The area under the curve of the time-dependent receiver operator characteristic revealed a strong predictive accuracy for training and test datasets of the corresponding signature. CC has cellular heterogeneity of transcriptional activation. Our analyses provide a novel transcription factor-associated prognostic model for CC. These transcription factors could be used as effective prognostic biomarkers and potential therapeutic targets for patients with cervical cancer.

Cervical cancer; Cancer prognosis; Single-cell RNA-sequencing; Transcription factors; Overall survival

[1] Aalijahan H, Ghorbian S. Long non-coding RNAs and cervical cancer. Experimental and Molecular Pathology. 2019; 106: 7–16.

[2] Gupta SM, Mania-Pramanik J. Retracted article: molecular mechanisms in progression of HPV-associated cervical carcinogenesis. Journal of Biomedical Science. 2019; 26: 28.

[3] Small W, Bacon MA, Bajaj A, Chuang LT, Fisher BJ, Harkenrider MM, et al. Cervical cancer: a global health crisis. Cancer. 2017; 123: 2404–2412.

[4] Naga CH P, Gurram L, Chopra S, Mahantshetty U. The management of locally advanced cervical cancer. Current Opinion in Oncology. 2018; 30: 323–329.

[5] Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, et al. The human transcription factors. Cell. 2018; 172: 650–665.

[6] Fan C, Du J, Liu N. Identification of a transcription factor signature that can predict breast cancer survival. Computational and Mathematical Methods in Medicine. 2021; 2021: 2649123.

[7] Li M, Wang H, Li W, Peng Y, Xu F, Shang J, et al. Identification and validation of an immune prognostic signature in colorectal cancer. International Immunopharmacology. 2020; 88: 106868.

[8] Zhang B, Wang H, Guo Z, Zhang X. A panel of Transcription factors identified by data mining can predict the prognosis of head and neck squamous cell carcinoma. Cancer Cell International. 2019; 19: 297.

[9] Taylor JMG. Random survival forests. Journal of Thoracic Oncology. 2011; 6: 1974–1975.

[10] Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021; 184: 3573–3587.e29.

[11] Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology. 2019; 20: 163–172.

[12] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015; 43: e47.

[13] Yu G, Wang L, Han Y, He Q. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012; 16: 284–287.

[14] Díaz-Coto S, Martínez-Camblor P, Pérez-Fernández S. SmoothROCtime: an R package for time-dependent ROC curve estimation. Computational Statistics. 2020; 35: 1231–1251.

[15] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2018; 68: 394–424.

[16] Pan J, Xu L, Pan H. Development and validation of an m6A RNA methylation regulator-based signature for prognostic prediction in cervical squamous cell carcinoma. Frontiers in Oncology. 2020; 10: 1444.

[17] Gao C, Zhou C, Zhuang J, Liu L, Liu C, Li H, et al. MicroRNA expression in cervical cancer: novel diagnostic and prognostic biomarkers. Journal of Cellular Biochemistry. 2018; 119: 7080–7090.

[18] Ao X, Ding W, Ge H, Zhang Y, Ding D, Liu Y. PBX1 is a valuable prognostic biomarker for patients with breast cancer. Experimental and Therapeutic Medicine. 2020; 20: 385–394.

[19] Liu Y, Ao X, Zhou X, Du C, Kuang S. The regulation of PBXs and their emerging role in cancer. Journal of Cellular and Molecular Medicine. 2022; 26: 1363–1379.

[20] Morgan R, Pandha HS. PBX3 in cancer. Cancers. 2020; 12: 431.

[21] Song Y, Ma R. Identifying the potential roles of PBX4 in human cancers based on integrative analysis. Biomolecules. 2022; 12: 822.

[22] Martinou EG, Moller-Levet CS, Angelidi AM. PBX4 functions as a potential novel oncopromoter in colorectal cancer: a comprehensive analysis of the PBX gene family. American Journal of Cancer Research. 2022; 12: 585.

[23] Badaloni A, Casoni F, Croci L, Chiara F, Bizzoca A, Gennarini G, et al. Dynamic Expression and New Functions of Early B cell factor 2 in cerebellar development. The Cerebellum. 2019; 18: 999–1010.

[24] Chen G, Yu W, Li Z, Wang Q, Yang Q, Du Z, et al. Potential regulatory effects of miR-182-3p in osteosarcoma via targeting EBF2. BioMed Research International. 2019; 2019: 4897905.

[25] Moruzzo D, Nobbio L, Sterlini B, Consalez GG, Benfenati F, Schenone A, et al. The transcription factors EBF1 and EBF2 are positive regulators of myelination in Schwann cells. Molecular Neurobiology. 2017; 54: 8117–8127.

[26] Nikitina AS, Sharova EI, Danilenko SA, Butusova TB, Vasiliev AO, Govorov AV, et al. Novel RNA biomarkers of prostate cancer revealed by RNA-seq analysis of formalin-fixed samples obtained from Russian patients. Oncotarget. 2017; 8: 32990–33001.

[27] Liao Y, Zou X, Wang K, Wang Y, Wang M, Guo T, et al. Comprehensive analysis of transcription factors identified novel prognostic biomarker in human bladder cancer. Journal of Cancer. 2021; 12: 5605–5621.

[28] Li M, Shen Y, Wang Q, Zhou X. MiR-204-5p promotes apoptosis and inhibits migration of osteosarcoma via targeting EBF2. Biochimie. 2019; 158: 224–232.

[29] Yan D, Shen M, Du Z, Cao J, Tian Y, Zeng P, et al. Developing ZNF gene signatures predicting radiosensitivity of patients with breast cancer. Journal of Oncology. 2021; 2021: 9255494.

[30] Kaya IH, Al-Harazi O, Kaya MT, Colak D. Integrated analysis of transcriptomic and genomic data reveals blood biomarkers with diagnostic and prognostic potential in non-small cell lung cancer. Frontiers in Molecular Biosciences. 2022; 9: 774738.