Frontiers in oncology · 2023
Gagan Chhabra、Wen-Qing Li、Colette Pameijer
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Frontiers in endocrinology · 2024
Toru Tateno、Roxana-Ioana Dumitriu Stan、Stephanie Du Four
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Journal of Hepatocellular Carcinoma · 2023
S. Lin、Yi Cao、Ke Zhu 等
Background The complex tumor microenvironment of hepatocellular carcinoma (HCC) has led to a low response to immune checkpoints inhibitors (ICIs) and a poor prognosis. PD-L1, as one of the indications for ICIs, is rich in glycosylation modifications, which result in untimely ICIs. Our study constructed a prognostic model based on N-linked glycosylation related genes for predicting the prognosis and the response to ICIs. Methods The list of N-linked glycosylation related genes is from the AmiGO2 database. The patients in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were enrolled. The Cox regression was performed to develop a prognostic model and patients were divided into a low- and high-risk subgroups. The role of signature in HCC was well investigated by prognostic analysis, gene set enrichment analysis, and immune infiltration analysis. 21 recurrent HCC patients who received postoperative adjuvant ICIs were recruited to evaluate the relationship between immunotherapy response and the signature. In vitro studies were conducted to investigate the oncogenic effects of DDOST, STT3A and TMEM165 in HCC. Results 59 N-linked glycosylation related differentially expressed genes were screened from HCC and normal tissues in the TCGA cohort. The prognostic model was developed with DDOST, STT3A and TMEM165. The risk score could be an independent prognostic factor. Patients in the high-risk subgroup showed a worse prognosis than patients in the low-risk one. ssGSEA showed that patients in the low-risk subgroup tended to be in the immune-activated state, with higher levels of B cell and macrophage cell infiltrations and lower levels of regulatory T cell (Treg) infiltrations in both TCGC and GEO cohorts. Immunohistochemistry studies showed that DDOST, STT3A and TMEM165 are highly expressed in tumor tissues and patients with a high-risk score correlated with poor progression free survival and worse immunotherapeutic response. Furthermore, the proliferation of HCC cells was reduced after the knockdown of DDOST, as well as upon the knockdown of STT3A and TMEM165. Conclusion In this study, we establish that the risk model based on N-linked glycosylation related genes could efficiently predict the prognosis and tumor microenvironment immune state of HCC patients, and the risk score could serve as a novel indicator of immunotherapy.
Discover Oncology · 2026
Shu Wu
Colorectal cancer (CRC) is a leading cause of cancer mortality. The Golgi apparatus (GA) mediates protein glycosylation and secretion, and its dysfunction is implicated in cancer invasion and immune evasion [1]. The prognostic relevance of GA-related genes in CRC remains unclear. Transcriptomic and clinical data from TCGA-COAD/READ and GSE39582 were analysed. A set of 1 686 GA-related genes from MSigDB was filtered by differential expression, and prognostic candidates were selected using univariate Cox, LASSO and random forest approaches to construct a multi-gene risk score. Functional enrichment, immune infiltration (ESTIMATE/CIBERSORT) and drug-sensitivity (oncoPredict) analyses were performed, and a nomogram combining the GA score with clinical covariates was built. Among 5580 differentially expressed genes, 434 were GA-related. A six-gene signature (PCSK5, RAB36, CD36, DPP7, KPNA2, HEPACAM2) stratified patients into high- and low-risk groups with significant survival differences and retained independent prognostic value after adjusting for age and stage. High-risk tumors showed enrichment of proteoglycan and G-protein-coupled receptor pathways, higher immune and stromal scores with abundant monocytes and macrophages but fewer dendritic and T cells, and were predicted to respond poorly to immune checkpoint blockade yet displayed increased sensitivity to the IGF-1R inhibitor BMS-754,807. GA related gene expression delineates CRC subtypes with distinct biology, immunity and therapeutic vulnerabilities. Integrating the GA derived signature with clinical factors may help refine risk stratification and generate hypotheses regarding glycosylation-/proteoglycan-related therapeutic vulnerability and IGF-1R inhibition.
Frontiers in immunology · 2025
Chunyang Li、Haiyan Xue、Lihe Chen 等
Sepsis is a life-threatening condition caused by a dysregulated host response to infection and is one of the leading causes of morbidity and mortality worldwide. Glycosylation is one of the key modes of protein modification, affecting protein folding, transportation, and localization. Glycosylation patterns are closely related to sepsis, but their specific impact still needs further investigation. This study explored the role of glycosylation-related genes in sepsis through bioinformatics analysis and machine learning, and validated the expression value of the key genes. We identified 38 differentially expressed glycosylation-related genes in sepsis datasets, which divided sepsis patients into two subgroups with different survival outcomes, thus highlighting their prognostic value. Subsequently, we constructed prognostic models using various machine learning methods, classifying patients into high-risk and low-risk groups with significantly different survival rates. We conducted biological analysis of the key genes in the model at the single-cell level and also validated the expression of these key genes in sepsis patient samples. Our study not only enhances the understanding of sepsis glycosylation but also provides a new strategy for clinical diagnosis and prognosis.
Journal of Hepatocellular Carcinoma · 2023
Tianxing Dai、Jing Li、R. Liang 等
Purpose Glycosylation has been demonstrated to be involved in tumorigenesis, progression, and immunoregulation, and to present specific profiles in different tumors. In this study, we aimed to explore the specific glycosylation-related gene (GRG) signature and its potential immunological roles and prognostic implications in hepatocellular carcinoma (HCC). Patients and Methods The GRG expression profile was defined using the transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and the least absolute shrinkage and selection operator Cox analyses were performed to develop a GRG-based risk score model. A nomogram was subsequently established and validated. Its correlation with cancer immune microenvironment and drug susceptibility was further analyzed. The role and immunological correlation of ST6GALNAC4 were further experimentally validated at the tissue and cellular levels in HCC. Results A total of 87 GRGs were identified to be significantly dysregulated in HCC, and a novel risk score model was constructed using eight critical GRGs, which demonstrated superior prognostic discrimination and predictive power in both training and validation groups. High risk scores in HCC patients were associated with lower OS. The model was also identified as an independent risk factor for HCC, and a novel nomogram was subsequently constructed and validated. Notably, significant correlations were found in risk scores with immune cells infiltration, tumor immunophenotyping, immune checkpoint genes’ expression, and sensitivities to multiple drugs. Furthermore, we validated in local HCC samples that ST6GALNAC4 was significantly upregulated and its knockdown significantly inhibited the tumor proliferation, migration and invasion ability and affected the expression of immune checkpoints on hepatoma cells. Conclusion We identified a novel GRG-based model which showed significant prognostic and immunological correlations in HCC, and the oncogenic role of ST6GALNAC4 has been validated and may serve as a potential drug target.
Journal of surgical oncology · 2018
David M Hyams、Robert W Cook、Antonio C Buzaid
New therapeutic modalities for melanoma promise benefit in selected individuals. Efficacy appears greater in patients with lower tumor burden, suggesting an important role for risk-stratified surveillance. Robust predictive markers might permit optimization of agent to patient, while low-risk prognostic markers might guide more conservative management. This review evaluates protein, gene, and multiplexed marker panels that may contribute to better risk assessment and improved management of patients with cutaneous melanoma.
Experimental biology and medicine (Maywood, N.J.) · 2020
Jinfeng Zhang、Xuedi Cheng、Junzheng Wang 等
Although new diagnostic techniques and treatments are increasingly updated for CRC, the clinical outcomes of CRC patients are still not encouraging with a low survival rate. N6-methyladenosine (m6A) as a popular modification on mRNA is associated with multiple types of cancers. Our purpose is to identify gene signature and prognostic ability of m6A modulators in CRC. For the first time, we identified genetic changes of m6A modulators and built prognostic gene signature in CRC, which may provide effective targets for the diagnosis and management of CRC.
Frontiers in Immunology · 2023
Wentao Zhang、Haizeng Qu、Xiaoqing Ma 等
Background Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. Methods RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). Results We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.
Scientific Reports · 2023
Yihuan Qiao、Xun-Liang Jiang、Y. Li 等
Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 ( HK1 ) and glucose-6-phosphate isomerase ( GPI ) gene expression in natural killer (NK) and CD8^+ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature’s six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature’s six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells.
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