ArticleCellular oncology (Dordrecht, Netherlands)2024
Exploring tumor microenvironment interactions and apoptosis pathways in NSCLC through spatial transcriptomics and machine learning.
Article in Cellular oncology (Dordrecht, Netherlands), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Lens tissue transcriptome analysis in patients with total congenital cataract.Human genomics · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Beyond the membrane: rethinking EGFR signaling in physiology and cancer.Cellular and molecular life sciences : CMLS · 2026Review
- Dissecting regulated cell death states and transformation mechanisms in the evolutionary trajectory of 30 cancers.iScience · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe most common type of lung cancer is non-small cell lung cancer (NSCLC), accounting for 85% of all cases. Programmed cell death (PCD), an important regulatory mechanism for cell survival and homeostasis, has become increasingly prominent in cancer research in recent years. As such, exploring the role of PCD in NSCLC may help uncover new mechanisms for therapeutic targets.
methodsWe utilized the GEO database and TCGA NSCLC gene data to screen for co-expressed genes. To delve deeper, single-cell sequencing combined with spatial transcriptomics was employed to study the intrinsic mechanisms of programmed cell death in cells and their interaction with the tumor microenvironment. Furthermore, Mendelian randomization was applied to screen for causally related genes. Prognostic models were constructed using various machine learning algorithms, and multi-cohort multi-omics analyses were conducted to screen for genes. In vitro experiments were then carried out to reveal the biological functions of the genes and their relationship with apoptosis.
resultsCells with high programmed cell death activity primarily activate pathways related to apoptosis, cell migration, and hypoxia, while also exhibiting strong interactions with smooth muscle cells in the tumor microenvironment. Based on a set of programmed cell death genes, the prognostic model NSCLCPCD demonstrates strong predictive capabilities. Moreover, laboratory experiments confirm that SLC7A5 promotes the proliferation of NSCLC cells, and the knockout of SLC7A5 significantly increases tumor cell apoptosis.
conclusionsOur data indicate that programmed cell death is predominantly associated with pathways related to apoptosis, tumor metastasis, and hypoxia. Additionally, it suggests that SLC7A5 is a significant risk indicator for the prognosis of non-small cell lung cancer (NSCLC) and may serve as an effective target for enhancing apoptosis in NSCLC tumor cells.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.