It is well known that cervical cancer poses the fourth most malignancy threat to women worldwide among all cancer types. There is a tremendous improvement in realizing the underlying molecular associations in cervical cancer. Several studies reported pieces of evidence for the involvement of various genes in the disease progression. However, with the ever-evolving bioinformatics tools, there has been an upsurge in predicting numerous genes responsible for cervical cancer progression and making it highly complex to target the genes for further evaluation. In this article, we prioritized the candidate genes based on the sequence similarity analysis with known cancer genes. For this purpose, we used the concept of the moment of inertia tensor, which reveals the similarities between the protein sequences more efficiently. Tensor for moment of inertia explores the similarity of the protein sequences based on the physicochemical properties of amino acids. From our analysis, we obtained 14 candidate cervical cancer genes, which are highly similar to known cervical cancer genes. Further, we analyzed the GO terms and prioritized these genes based on the number of hits with biological process, molecular functions, and their involvement in KEGG pathways. We also discussed the evidence-based involvement of the prioritized genes in other cancers and listed the available drugs for those genes.

中文翻译：

---

使用惯性矩张量对与宫颈癌相关的候选基因进行优先排序

众所周知，宫颈癌是全球所有癌症类型中对女性的第四大恶性肿瘤威胁。在实现宫颈癌的潜在分子关联方面取得了巨大的进步。几项研究报告了各种基因参与疾病进展的证据。然而，随着生物信息学工具的不断发展，在预测导致宫颈癌进展的众多基因方面出现了热潮，并使针对这些基因进行进一步评估变得非常复杂。在本文中，我们根据与已知癌基因的序列相似性分析对候选基因进行了优先排序。为此，我们使用了惯性张量的概念，它更有效地揭示了蛋白质序列之间的相似性。惯性矩张量基于氨基酸的物理化学性质探索蛋白质序列的相似性。从我们的分析中，我们获得了14个候选宫颈癌基因，它们与已知的宫颈癌基因高度相似。此外，我们分析了 GO 术语，并根据具有生物学过程的命中数、分子功能及其参与 KEGG 途径对这些基因进行优先排序。我们还讨论了优先基因在其他癌症中的循证参与，并列出了这些基因的可用药物。我们分析了 GO 术语，并根据具有生物学过程的命中数、分子功能及其参与 KEGG 途径对这些基因进行优先排序。我们还讨论了优先基因在其他癌症中的循证参与，并列出了这些基因的可用药物。我们分析了 GO 术语，并根据具有生物学过程的命中数、分子功能及其参与 KEGG 途径对这些基因进行优先排序。我们还讨论了优先基因在其他癌症中的循证参与，并列出了这些基因的可用药物。