Cancer-Associated Fibroblasts Influence Survival in Pleural Mesothelioma: Digital Gene Expression Analysis and Supervised Machine Learning Model
The exact mechanism of desmoplastic stromal reaction (DSR) formation is still unclear. The interaction between cancer cells and cancer-associated fibroblasts (CAFs) has an important role in tumor progression, while stromal changes are a poor prognostic factor in pleural mesothelioma (PM). We aimed t...
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Veröffentlicht in: | International journal of molecular sciences 2023-08, Vol.24 (15), p.12426 |
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Sprache: | eng |
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Zusammenfassung: | The exact mechanism of desmoplastic stromal reaction (DSR) formation is still unclear. The interaction between cancer cells and cancer-associated fibroblasts (CAFs) has an important role in tumor progression, while stromal changes are a poor prognostic factor in pleural mesothelioma (PM). We aimed to assess the impact of CAFs paracrine signaling within the tumor microenvironment and the DSR presence on survival, in a cohort of 77 PM patients. DSR formation was evaluated morphologically and by immunohistochemistry for Fibroblast activation protein alpha (FAP). Digital gene expression was analyzed using a custom-designed CodeSet (NanoString). Decision-tree-based analysis using the "conditional inference tree" (CIT) machine learning algorithm was performed on the obtained results. A significant association between FAP gene expression levels and the appearance of DSR was found (
= 0.025). DSR-high samples demonstrated a statistically significant prolonged median survival time. The elevated expression of
,
,
,
, and SOS1 was associated with shortened OS, whereas the upregulation of
,
, and
was associated with prolonged OS. CIT revealed a three-tier system based on
,
, and
expressions. We could outline the prognostic value of CAFs-induced PI3K signaling pathway activation together with FAP-dependent CDK4 mediated cell cycle progression in PM, where prognostic and predictive biomarkers are urgently needed to introduce new therapeutic strategies. |
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ISSN: | 1422-0067 1661-6596 1422-0067 |
DOI: | 10.3390/ijms241512426 |