A comprehensive analysis of the diagnostic and prognostic value associated with the SLC7A family members in breast cancer

Yan, Liping; He, Jianxin; Liao, Xiwen; Liang, Tianyi; Zhu, Jia; Wei, Wensong; He, Yongfei; Zhou, Xin; Peng, Tao

doi:10.21037/gs-21-909

Cited by 10 publications

(6 citation statements)

References 63 publications

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“…FOXC1: a therapeutic biomarker specific for basal-like breast cancer, is not only a potential prognostic candidate but also a potential molecular therapeutic target for this subtype of breast cancer [ 37 , 38 ]. SLC7A13: The SLC7A family has good diagnostic efficacy in breast cancer [ 39 ]. UBE2C: Ubiquitin-binding enzyme E2C (UBE2C) may be oncogenic for the progression of breast cancer genes [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

Liu

Feng

et al. 2023

BMC Bioinformatics

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Background Cancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes. Results In this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype. Conclusions Our work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification.

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Section: Resultsmentioning

confidence: 99%

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

Liu

Feng

et al. 2023

BMC Bioinformatics

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“…ACTL8 has been consistently linked with poor BC prognosis in several studies 50–52 . Discoveries related to CACNA1H, 53 SLC7A family members, 54 CLEC3A, 55,56 and others have further enriched our understanding of BC's molecular landscape.…”

Section: Discussionmentioning

confidence: 99%

Development of a novel prognostic signature based on single‐cell combined bulk RNA analysis in breast cancer

Xiao,

Hu,

Xie

et al. 2024

The Journal of Gene Medicine

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BackgroundBreast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single‐Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non‐malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival.MethodsWe analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non‐malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator‐penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan–Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high‐risk and low‐risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes.ResultsThe SCEVAN algorithm identified distinct malignant and non‐malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune‐related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14‐gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan–Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single‐cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high‐ and low‐risk groups and identified 16 significantly correlated drugs.ConclusionOur findings suggest that the 14‐gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

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Section: Glutaminolysis Determines Cytokine Signalling and T Helper C...mentioning

confidence: 99%

“…In breast cancer patients, according to The Cancer Genome Atlas (TCGA) plotter data, Th2 cells were strongly correlated with SLC7A family genes. 45 SLC7A5 inhibitor treatment suppressed allergeninduced skin inflammation in both ovalbumin (OVA)-immunized and OVA-specific Th2 cell-transferred mice and effectively suppressed allergen-induced airway and nasal hyperresponsiveness in immunized and/or Th2-transferred mice. [46][47][48][49] It is noteworthy that in CD4 + T cell differentiation models, glutamine restriction in the presence of all types of exogenous cytokine mixtures induces polarization toward Th2, whereas in the absence of Th2-induced cytokines, glutamine restriction promotes polarization toward Treg.…”

mentioning

confidence: 99%

Glutaminolysis of CD4+ T Cells: A Potential Therapeutic Target in Viral Diseases

Xu,

Li,

Lin

et al. 2024

JIR

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CD4 + T cells play a critical role in the pathogenesis of viral diseases, which are activated by the internal metabolic pathways encountering with viral antigens. Glutaminolysis converts glutamine into tricarboxylic acid (TCA) circulating metabolites by α-ketoglutaric acid, which is essential for the proliferation and differentiation of CD4 + T cells and plays a central role in providing the energy and structural components needed for viral replication after the virus hijacks the host cell. Changes in glutaminolysis in CD4 + T cells are accompanied by changes in the viral status of the host cell due to competition for glutamine between immune cells and host cells. More recently, attempts have been made to treat tumours, autoimmune diseases, and viral diseases by altering the breakdown of glutamine in T cells. In this review, we will discuss the current knowledge of glutaminolysis in the CD4 + T cell subsets from viral diseases, not only increasing our understanding of immunometabolism but also providing a new perspective for therapeutic target in viral diseases.

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A comprehensive analysis of the diagnostic and prognostic value associated with the SLC7A family members in breast cancer

Cited by 10 publications

References 63 publications

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

Development of a novel prognostic signature based on single‐cell combined bulk RNA analysis in breast cancer

Glutaminolysis of CD4+ T Cells: A Potential Therapeutic Target in Viral Diseases

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