An artificial intelligence network‐guided signature for predicting outcome and immunotherapy response in lung adenocarcinoma patients based on 26 machine learning algorithms

Abstract

The immune cells play an increasingly vital role in influencing the proliferation, progression, and metastasis of lung adenocarcinoma (LUAD) cells. However, the potential of immune cells' specific genes-based model remains largely unknown. In the current study, by analysing single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data, the tumour-infiltrating immune cell (TIIC) associated signature was developed based on a total of 26 machine learning (ML) algorithms. As a result, the TIIC signature score could predict survival outcomes of LUAD patients across five independent datasets. The TIIC signature score showed superior performance to 168 previously established signatures in LUAD. Moreover, the TIIC signature score developed by the immunofluorescence staining of the tissue array of LUAD patients showed a prognostic value. Our research revealed a solid connection between TIIC signature score and tumour immunity as well as metabolism. Additionally, it has been

HGF facilitates methylation of MEG3, potentially implicated in vemurafenib resistance in melanoma

“…Additionally, the present study investigated the immune characteristics of the MEG3‐related score as analyzed in previous studies 31,32 . It was found that melanoma patients with high MEG3‐related scores had an immune‐cold microenvironment.…”

“…Additionally, the present study investigated the immune characteristics of the MEG3-related score as analyzed in previous studies. 31,32 It was found that melanoma patients with high MEG3-related scores had an immune-cold microenvironment. Following our findings, by lowering p53 ubiquitination, MEG3 mediates the miR-149-3p/ FOXP3 axis, suppressing regulatory T cell development and immunological escape in esophageal cancer.…”

What's new in thoracic oncology

“…AI technologies are also being investigated in combination with existing tissue and liquid biopsy techniques to augment these diagnostic procedures. One example of this work is the development of a machine learning system to assign a tumor‐infiltrating immune cell signature score to lung adenocarcinomas based on RNA sequencing data, which was shown to forecast tumor susceptibility to chemo‐ and immunotherapy drugs 37 . Improvements in the accuracy and prognostic value of liquid biopsy thanks to AI have been reported in other, nonthoracic malignancies, such as ovarian cancer 38 .…”

“…One example of this work is the development of a machine learning system to assign a tumor-infiltrating immune cell signature score to lung adenocarcinomas based on RNA sequencing data, which was shown to forecast tumor susceptibility to chemo-and immunotherapy drugs. 37 Improvements in the accuracy and prognostic value of liquid biopsy thanks to AI have been reported in other, nonthoracic malignancies, such as ovarian cancer. 38 Similar investigations are currently underway in lung cancer, analyzing biomarkers which can be drawn from peripheral blood samples to guide diagnosis and individualize treatment recommendations.…”

Immune infiltration and clinical significance analyses of the cancer‐associated fibroblast‐related signature in skin cutaneous melanoma

“…To uncover the relationship between the CAF risk score and tumorinfiltrating immune cells, currently accepted methods, including CIBER-SORT, MCP-Counter, EPIC and xCELL were executed to calculate the immune infiltration values among the samples in the TCGA-SKCM cohort. [23][24][25] Single sample gene set enrichment analysis (ssGSEA) with "GSVA" R package was used to quantitate the differences in the relative infiltrations of immunocytes. 22 Additionally, ESTIMATE algorithm was used to predict ESTIMATE score, immune score and stromal score for each SKCM sample with TCGA expression data.…”