Identification of a Six-Gene SLC Family Signature With Prognostic Value in Patients With Lung Adenocarcinoma

Abstract

Given the importance of solute carrier (SLC) proteins in maintaining cellular metabolic homeostasis and that their dysregulation contributes to cancer progression, here we constructed a robust SLC family signature for lung adenocarcinoma (LUAD) patient stratification. Transcriptomic profiles and relevant clinical information of LUAD patients were downloaded from the TCGA and GEO databases. SLC family genes differentially expressed between LUAD tissues and adjacent normal tissues were identified using limma in R. Of these, prognosis-related SLC family genes were further screened out and used to construct a novel SLC family-based signature in the training cohort. The accuracy of the prognostic signature was assessed in the testing cohort, the entire cohort, and the external GSE72094 cohort. Correlations between the prognostic signature and the tumor immune microenvironment and immune cell infiltrates were further explored. We found that seventy percent of SLC family genes (279/397) were differentially expressed between LUAC tissues and adjacent normal. Twenty-six genes with p-values < 0.05 in univariate Cox regression analysis and Kaplan-Meier survival analysis were regarded as prognosis-related SLC family genes, six of which were used to construct a prognostic signature for patient classification into high- and low-risk groups. Kaplan-Meier survival analysis in all internal and external cohorts revealed a better overall survival for patients in the low-risk group than those in the high-risk group. Univariate and multivariate Cox regression analyses indicated that the derived risk score was an independent prognostic factor for LUAD patients. Moreover, a nomogram based on the six-gene signature and clinicopathological factors was developed for clinical application. High-risk patients had lower stromal, immune, and ESTIMATE scores and higher tumor purities than those in the low-risk group. The proportions of infiltrating naive CD4 T cells, activated memory CD4 T cells, M0 macrophages, resting dendritic cells, resting mast cells, activated mast cells, and eosinophils were significantly different between the high- and low-risk prognostic groups. In all, the six-gene SLC family signature is of satisfactory accuracy and generalizability for predicting overall survival in patients with LUAD. Furthermore, this prognostics signature is related to tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

Construction and Validation of a CNV-Driven Ferroptosis-Related Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma

“…SLC2A1 is a glucose transport protein coding gene that controls the absorption of glucose, and the encoded protein is closely related to glucose metabolism. SLC2A1 is connected with tumor progression and metastasis [29] that upregulated with poorer prognosis, including in LUAD [30]. AURKA is a recognized tumor susceptibility gene, which is essential for the normal process of cell mitosis [31] and overexpressed in many cancers and consists of breast cancer [32], colorectal cancer [33], gastrointestinal cancer [34], bladder cancer [35], and lung cancer [36].…”

“…The inhibition of SLC7A11-mediated cystine uptake results in intracellular glutathione deficiency, leading to ferroptosis-mediated cell death [7]. Xuan et al [30] reported that upregulated SLC7A11 was shown in NSCLC patients and is related to worse prognosis. ALOX5 is an initiation enzyme that mainly mediates the production of inflammatory mediators leukotriene and lipoxin.…”

Assessing the Link between Diabetic Metabolic Dysregulation and Breast Cancer Progression

“…A different study performed an in silico analysis on data derived from TCGA to study the association of SLC genes with lung adenocarcinoma. Low SLC26A11 expression was linked to worse overall survival [92]. However, according to the Human Protein Atlas, SLC26A11 gene expression is unfavorable in both liver and endometrial cancers [93].…”

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

“…Multi-omics analyses of SLCs in cancer have been crucial in detecting aberrant mechanisms leading to dysregulated transport rates (Figure 3c). Most commonly, the abnormal expression of SLCs beyond glucose transporters has been associated with the increased transport of metabolites and building blocks necessary for cancer development, which has been used to identify SLCs as prognostic biomarkers in pan-cancer [121,122] and cancerspecific studies [123][124][125]. In this context, ML has also helped further discriminate between the genes with the biggest effect within the cancer signatures [126].…”