Development of an immune-related gene pairs index for the prognosis analysis of metastatic melanoma

Abstract

Melanoma is a skin cancer with great metastatic potential, which is responsible for the major deaths in skin cancer. Although the prognosis of melanoma patients has been improved with the comprehensive treatment, for patients with metastasis, the complexity and heterogeneity of diffuse diseases make prognosis prediction and systematic treatment difficult and ineffective. Therefore, we established a novel personalized immune-related gene pairs index (IRGPI) to predict the prognosis of patients with metastatic melanoma, which was conducive to provide new insights into clinical decision-making and prognostic monitoring for metastatic melanoma. Through complex analysis and filtering, we identified 24 immune-related gene pairs to build the model and obtained the optimal cut-off value from receiver operating characteristic curves, which divided the patients into high and low immune-risk groups. Meantime, the Kaplan–Meier analysis, Cox regression analysis and subgroup analysis showed that IRGPI had excellent prognostic value. Furthermore, IRGPI was shown that was closely associated with immune system in the subsequent tumor microenvironment analysis and gene set enrichment analysis. In addition, we broken through the data processing limitations of traditional researches in different platforms through the application of gene pairs, which would provide great credibility for our model. We believe that our research would provide a new perspective for clinical decision-making and prognostic monitoring in metastatic melanoma.

TimiGP: inferring inter-cell functional interactions and clinical values in the tumor immune microenvironment through gene pairs

“…Immune-related gene pairs have been reported as emerging biomarkers to predict clinical outcomes 36, 37, 52 . Traditional methods select signatures from prognostic marker pairs, while TimiGP yields the enriched IMGPs that not only provide clinical insights but also reflect the functional cell interactions (Figure 4A), which is supposed to facilitate the model construction.…”

“…Despite this, it remains statistically and computationally challenging to identify the best combination of gene pair signatures due to the quadratically combinatorial complexity. While recent studies have revealed the advantage of immune-related gene pairs in developing prognostic signatures 36, 52 , the quadratic number of candidate genes still complicates the modeling process. Recent efforts focused on the improvement of machine-learning algorithms for variable selection and model fittings, which remain of poor quality in oncology 77 .…”

Identification and Validation of a Prognostic Immune-Related Gene Signature in Esophageal Squamous Cell Carcinoma

“…Multiple prognostic hub genes still cannot easily prompt the prognosis and risk of ESCC patients. To solve this problem, a prognostic risk model was established using the immune-related gene prognostic index, which could effectively predict the prognostic risk and immune response of ESCC patients ( Huang et al, 2021 ).…”

"Analysis of Immune-Related Genes Based on Lung Adenocarcinoma, Predict Immune Escape and Immunotherapy Effect"

“…Considering the cancer is closely related to immune, clinical treatment of tumor immune microenvironment [42][43][44][45]. IRGPI must be considered and immune cell infiltration between the relevant assessment can be used to reflect the state of microenvironment [46][47]. Here, we show that IRGPI and Plasma cells, Monocytes, Macrophages The infiltration level of M0 was significantly correlated (P <0.05), and it was mainly enriched in immune-related functions such as pDCs,Type II IFN, REponse and TIL.…”