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Background <p>Cervical cancer (CESC) is a major gynecological malignancy, and abnormalities in phosphoinositide metabolism (PPM) are closely linked to tumor progression. This study aimed to screen phosphoinositide metabolism-related genes (PPM-RGs) with prognostic value for CESC and explore their molecular mechanisms and clinical significance.</p> Methods <p>CESC transcriptomic datasets and PPM-RGs were retrieved from public databases. Differentially expressed genes were intersected with PPM-RGs to obtain candidate genes. Prognostic genes were screened via univariate Cox regression and proportional hazards assumption test, and a risk model was constructed using random survival forest (RSF). Independent prognostic analysis and nomogram establishment were performed, followed by pathway enrichment, immune infiltration, somatic mutation and single-cell analyses to characterize the tumor microenvironment. Immunohistochemical staining was used to verify the protein expression of key genes in CESC tissues.</p> Results <p>Twenty candidate genes were initially identified, and two protective prognostic genes VAC14 and INPP5K were finally determined. The risk model showed favorable predictive performance with area under the curve (AUC) over 0.7, and high-risk patients had poorer survival. The nomogram combining risk score and M stage presented high accuracy. The high-risk group exhibited distinct pathway enrichment, altered immune infiltration and low somatic mutation rates. Monocytes were identified as potential key cells with impaired communication and differentiation in CESC. Immunohistochemistry confirmed downregulated expression of the two genes in CESC epithelial cells.</p> Conclusion <p>VAC14 and INPP5K were PPM-related prognostic genes for CESC. Monocytes played critical roles in CESC progression. The risk model and nomogram aided prognosis prediction, providing new insights for CESC treatment.</p>

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Phosphoinositide metabolism-related prognostic genes and their prognostic value in cervical cancer: an investigation based on transcriptome, single-cell analyses and verification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Hui Meng,
  • Li Tan,
  • Ying Chen,
  • Dongmei Wang,
  • Yong Qi,
  • Chuchao Zhu

摘要

Background

Cervical cancer (CESC) is a major gynecological malignancy, and abnormalities in phosphoinositide metabolism (PPM) are closely linked to tumor progression. This study aimed to screen phosphoinositide metabolism-related genes (PPM-RGs) with prognostic value for CESC and explore their molecular mechanisms and clinical significance.

Methods

CESC transcriptomic datasets and PPM-RGs were retrieved from public databases. Differentially expressed genes were intersected with PPM-RGs to obtain candidate genes. Prognostic genes were screened via univariate Cox regression and proportional hazards assumption test, and a risk model was constructed using random survival forest (RSF). Independent prognostic analysis and nomogram establishment were performed, followed by pathway enrichment, immune infiltration, somatic mutation and single-cell analyses to characterize the tumor microenvironment. Immunohistochemical staining was used to verify the protein expression of key genes in CESC tissues.

Results

Twenty candidate genes were initially identified, and two protective prognostic genes VAC14 and INPP5K were finally determined. The risk model showed favorable predictive performance with area under the curve (AUC) over 0.7, and high-risk patients had poorer survival. The nomogram combining risk score and M stage presented high accuracy. The high-risk group exhibited distinct pathway enrichment, altered immune infiltration and low somatic mutation rates. Monocytes were identified as potential key cells with impaired communication and differentiation in CESC. Immunohistochemistry confirmed downregulated expression of the two genes in CESC epithelial cells.

Conclusion

VAC14 and INPP5K were PPM-related prognostic genes for CESC. Monocytes played critical roles in CESC progression. The risk model and nomogram aided prognosis prediction, providing new insights for CESC treatment.