Dataset Information

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer.

ABSTRACT:

Background

The Golgi apparatus plays a pivotal role in various aspects of cancer. This study aims to investigate the predictive value of Golgi apparatus-related genes (GARGs) in breast cancer prognosis and immunotherapy response evaluation.

Methods

Transcriptional and clinical data from the TCGA-BRCA cohort and GSE96058 cohort were utilized to construct and validate a prognostic model for breast cancer using Cox regression analysis. Differences in immune landscape, somatic mutations, gene expression, drug sensitivity, and immunotherapy response between different risk groups were assessed. A prognostic nomogram for breast cancer was further developed and evaluated. qPCR and single-cell sequencing analyses were performed to validate the expression of GARGs.

Results

A total of 394 GARGs significantly associated with breast cancer prognosis were identified, leading to the construction of a prognostic risk feature comprising 10 GARGs. This feature effectively stratified breast cancer patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse prognosis. Meanwhile, significant differences in clinicopathological features, immune infiltration, drug sensitivity, and immunotherapy response were observed between the high- and low-risk groups. The constructed nomogram incorporating these factors showed superior performance in prognostic assessment for breast cancer patients. Ultimately, the utilization of qPCR and single-cell sequencing techniques substantiated the disparate expression patterns of these prognostic genes in breast cancer.

Conclusion

Our findings demonstrate that a prognostic risk feature derived from GARGs holds promising application potential for predicting prognosis and evaluating immunotherapy response in breast cancer patients.

SUBMITTER: Chen X

PROVIDER: S-EPMC10834659 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer.

Chen Xin X Tang Pengting P Kong Ying Y Chen Deqin D Tang Kejun K

Journal of cancer research and clinical oncology 20240201 2

<h4>Background</h4>The Golgi apparatus plays a pivotal role in various aspects of cancer. This study aims to investigate the predictive value of Golgi apparatus-related genes (GARGs) in breast cancer prognosis and immunotherapy response evaluation.<h4>Methods</h4>Transcriptional and clinical data from the TCGA-BRCA cohort and GSE96058 cohort were utilized to construct and validate a prognostic model for breast cancer using Cox regression analysis. Differences in immune landscape, somatic mutatio ...[more]

PMID: 38300336

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Project description:IntroductionThe Golgi apparatus (GA) is the center of protein and lipid synthesis and modification in normal cells and is involved in regulating various cellular process as a signaling hub, the dysfunction of which can lead to the development of various pathological conditions, including tumors. Mutations in Golgi apparatus-related genes (GARGs) are prevalent in most tumors, and their mutations can make them pro-tumor metastatic. The aim of this study was to analyze the predictive role of GARGs in the prognosis and immunotherapeutic outcome of hepatocellular carcinoma.MethodsWe used TCGA, GEO and ICGC databases to classify hepatocellular carcinoma samples into two molecular subtypes based on the expression of GARGs. Signature construction was then performed using GARGs, and signature genes were selected for expression validation and tumor phenotype experiments to determine the role of GARGs in the prognosis of hepatocellular carcinoma.ResultsUsing the TCGA, GEO and ICGC databases, two major subtypes of molecular heterogeneity among hepatocellular carcinoma tumors were identified based on the expression of GARGs, C1 as a high-risk subtype (low survival) and C2 as a low-risk subtype (high survival). The high-risk subtype had lower StromalScore, ImmuneScore, ESTIMATEScore and higher TumorPurity, indicating poorer treatment outcome for ICI. Meanwhile, we constructed a new risk assessment profile for hepatocellular carcinoma based on GARGs, and we found that the high-risk group had a worse prognosis, a higher risk of immune escape, and a higher TP53 mutation rate. Meanwhile, TME analysis showed higher tumor purity TumorPurity and lower ESTIMATEScore, ImmuneScore and StromalScore in the high-risk group. We also found that the high-risk group responded more strongly to a variety of anticancer drugs, which is useful for guiding clinical drug use. Meanwhile, the expression of BSG was experimentally found to be associated with poor prognosis of HCC. After interfering with the expression of BSG in HCC cells SMMC-7721, the proliferation and migration ability of HCC cells were significantly restricted.DiscussionThe signature we constructed using GARGs can well predict the prognosis and immunotherapy effect of hepatocellular carcinoma, providing new ideas and strategies for the treatment of hepatocellular carcinoma.

Project description:The Golgi apparatus plays a crucial role in intracellular protein transportation, processing, and sorting. Dysfunctions of the Golgi apparatus have been implicated in tumorigenesis and drug resistance. This study aimed to investigate the prognostic and treatment response assessment value of Golgi apparatus-related gene (GARGs) features in gastric cancer patients. Transcriptome data and clinical information of gastric cancer patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Cox regression analysis was employed to assess the prognostic significance of GARGs and construct risk features. The immune landscape, drug sensitivity, immune therapy response, gene expression patterns, and somatic mutation characteristics were analyzed between different risk groups. A nomogram model for predicting gastric cancer prognosis was developed and evaluated. Among 1643 GARGs examined, 365 showed significant associations with gastric cancer prognosis. Five independent prognostic GARGs (NGF, ABCG1, CHAC1, GBA2, PCSK7) were selected to construct risk features for gastric cancer patients. These risk features effectively stratified patients into high-risk and low-risk groups, with the former exhibiting worse prognosis than the latter. Patients in the high-risk group displayed higher levels of immune cell infiltration, while the expression levels of NGF, CHAC1, GBA2, PCSK7 were significantly correlated with immune cell infiltration. Notably, the low-risk group exhibited higher sensitivity to epothilone.B, metformin, and tipifarnib compared to the high-risk group. Moreover, patients in the low-risk group demonstrated greater responsiveness to immune therapy than those in the high-risk group. In terms of biological processes and KEGG pathways related to immunity regulation, significant suppression was observed in the high-risk group compared to the low-risk group; meanwhile cell cycle pathways exhibited significant activation in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden compared to the high-risk group. The risk features derived from GARGs, in conjunction with age, were identified as independent risk factors for gastric cancer. The nomogram incorporating these factors demonstrated improved performance in predicting gastric cancer prognosis. Our study established risk features derived from GARGs that hold potential clinical utility in prognostic assessment and immune therapy response evaluation of gastric cancer patients.

Project description:BackgroundThis study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.MethodsA training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.ResultsSVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.ConclusionsThe HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Project description:BackgroundBladder cancer (BC) is one of the most common malignant diseases and the most common causes of cancer death worldwide. Immunotherapy has opened new avenues for precision treatment of bladder tumours, and immune checkpoint inhibitors (ICIs) have revolutionized the clinical treatment strategy of bladder tumours. In addition, long non-coding RNA (lncRNA) plays an important role in regulating tumour development and immunotherapy efficacy.MethodsWe obtained genes with significant differences between anti-PD-L1 response and non-response from the Imvogor210 data set and combined with the bladder cancer expression data in the TCGA cohort to obtain immunotherapy-related lncRNA. Based on these lncRNAs, the prognostic risk model of bladder cancer was constructed and verified by GEO external data set. The characterization of immune cell infiltration and immunotherapy effects between high-risk and low-risk groups were then analysed. We predicted the ceRNA network and performed molecular docking of key target proteins. The functional experiments verified the function of SBF2-AS1.ResultsThree immunotherapy-related lncRNAs were identified as independent prognostic biomarkers for bladder cancer and a prognostic model of immunotherapy-related prognosis was constructed. Prognosis, immune cell infiltration, and immunotherapy efficacy were significantly different between high- and low-risk groups based on risk scores. Additionally, we established a ceRNA network of lncRNA(SBF2-AS1)-miRNA(has-miR-582-5p)-mRNA (HNRNPA2B1). Targeting the protein HNRNPA2B1 identified the top eight small molecule drugs with the highest affinity.ConclusionWe developed a prognostic risk score model based on immune-therapy-related lncRNA, which was subsequently determined to be significantly associated with immune cell infiltration and immunotherapy response. This study not only helps to promote our understanding of immunotherapy-related lncRNA in the prognosis of BC, but also provides new ideas for clinical immunotherapy and the development of novel therapeutic drugs for patients.

Project description:The Golgi apparatus (GA), an organelle that processes, sorts, and transports proteins synthesized by the endoplasmic reticulum, is also involved in many cellular processes associated with cancer, such as angiogenesis, the innate immune response, and tumor invasion and migration. We aimed to construct a breast cancer (BC) prognosis prediction model based on GA-related genetic information to evaluate the prognosis of patients with BC more accurately than existing models and to stratify patients for clinical therapy. In this study, The Cancer Genome Atlas-breast invasive carcinoma was used as the training cohort, and the Molecular Taxonomy of Breast Cancer International Consortium cohort was used as the validation cohort. Using bioinformatics methods, we constructed a GA-related gene risk score (GRS). The GRS was used to divide BC patients into a high-GRS group and a low-GRS group, and functional analysis, survival analysis, mutation analysis, immune landscape analysis, and metabolic analysis were performed to compare the 2 groups. Finally, a nomogram was constructed for clinical application. The genes in the GRS model were mainly related to the glucose metabolism pathway, and the main mutations in the 2 groups of patients were mutations in TP53 and CHD1. The mutation rate in the high-GRS group was greater than that in the low-GRS group. The high GRS group had higher tumor immune activity glycolysis; the pentose phosphate pathway tended to be the dominant metabolic pathways in this group, while fatty acid oxidation and glutamine catabolism tended to be dominant in the low-GRS group. GA-related genes were used to construct a prediction model for BC patients and had high accuracy in predicting prognosis. The mutations associated with the GRS are mainly TP53 and CDH1. Interestingly, the GRS is correlated with glucose metabolism in terms of gene expression and functional enrichment. In summary, the role of GRS-related genes in glucose metabolism is worthy of further study.

Dataset Information

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer.

Background

Methods

Results

Conclusion

Publications

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer.

Similar Datasets

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