Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions

Abstract

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

Keywords: artificial intelligence; bioinformatics; biomarkers; cervical cancer; inflammation; proteomics.

Figure 1

The overview of the identifying proteomic signature and their use in patient management in cervical cancer. Proteomic signatures can be identified and developed from blood or the pre-cancerous and cancerous lesions by employing immuno-assays or mass-spectrometry. The proteomic data from a sizeable population are then fed into a bioinformatic or artificial pipeline to reduce the complexity. The output then can be used for monitoring therapeutic benefits in the form of survival or to design novel therapies or treatments with lowered toxicities for improved survival. This figure was created on BioRender.