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Machine Learning-Driven PCDI Classifier for Invasive PitNETs.

Guanyu Wang1,2, Song Yan1,2, Luyang Zhang1

  • 1Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.

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Summary
This summary is machine-generated.

A novel Programmed Cell Death Index (PCDI) accurately distinguishes aggressive pituitary tumors. This biomarker captures immune-metabolic crosstalk, offering new avenues for personalized therapy in invasive PitNETs.

Keywords:
Pituitary neuroendocrine tumorsgene expression omnibusimmune infiltrationmachine learningpan-cancerprognostic modelsprogrammed cell death-associated index

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Area of Science:

  • Oncology
  • Molecular Biology
  • Immunology

Background:

  • Aggressive Pituitary Neuroendocrine Tumors (PitNETs) present treatment challenges due to invasiveness and therapy resistance.
  • Existing prognostic markers fail to capture molecular heterogeneity, highlighting the need for novel biomarkers.
  • Dysregulated Programmed Cell Death (PCD) pathways are implicated in cancer, but their role in invasive PitNETs is unclear.

Purpose of the Study:

  • To identify novel molecular biomarkers for aggressive PitNETs.
  • To investigate the prognostic relevance of PCD pathways in invasive PitNETs.
  • To develop a predictive index for risk stratification and personalized therapy.

Main Methods:

  • Differential gene expression analysis of GEO datasets (GSE51618, GSE169498, GSE260487) comparing noninvasive and invasive PitNETs.
  • Integration of a 1,548-gene PCD-related gene panel.
  • Machine learning (LASSO, SVM-RFE) to construct a PCD-associated Index (PCDI); validation via ROC analysis, immune infiltration assessment, and RT-qPCR.

Main Results:

  • The 11-gene PCDI accurately differentiated invasive from noninvasive PitNETs.
  • High-PCDI tumors showed enriched metabolic pathways and immune activation.
  • Consensus clustering identified two subtypes; C2 (high-PCDI) exhibited increased immune scores and pathway activity, with key gene expression validated experimentally.

Conclusions:

  • The PCDI surpasses traditional models by integrating PCD-immune-metabolic crosstalk for improved prognostic accuracy in invasive PitNETs.
  • High-PCDI tumors display immune evasion despite checkpoint molecule expression, suggesting combined MAPK inhibitor and immunotherapy potential.
  • The PCDI offers a molecular framework for risk stratification and personalized treatment of invasive PitNETs.