A novel glycosyltransferase-related lncRNA signature correlates with lung adenocarcinoma prognosis

Identification and validation of glycosyltransferase-related gene signatures to predict prognosis and immunological characteristics of renal clear cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is more prone to metastasis and is associated with a poorer prognosis than renal cell carcinoma (RCC). Numerous studies have reported a correlation between the expression of glycosyltransferases (GTs)-related genes and tumor. We aimed to establish a risk model based on GTs-related genes in ccRCC, and explore their correlation with tumor immune characteristics and treatment sensitivity.

Methods

The messenger ribonucleic acid (mRNA) expression data were retrieved from The Cancer Genome Atlas (TCGA). Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct prognostic model. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the model. Calibration curves and decision curve analysis (DCA) curves were used to evaluate the model. The quantitative real-time polymerase chain reaction (qRT-PCR) was applied to detect the expression of the signature genes in human renal epithelial cells and human renal cancer cells. The ESTIMATE algorithm was used to estimate the immune scores in tumor tissues. Single-sample gene set enrichment analysis (ssGSEA) was used to evaluate the immune microenvironment. Tumor Immune Dysfunction and Exclusion (TIDE) and immune checkpoint analysis were used to assess the benefit of immunotherapy. Tumor mutational burden (TMB) analysis was used to calculate the frequency of gene mutations. Susceptibility to anticancer drugs in different risk groups was also analyzed.

Results

Four signature genes were identified as potential biomarkers, and the prognostic model demonstrated good predictive performance. qRT-PCR results were consistent with the actual predictions, confirming the credibility of the signature genes. The high- and low-risk groups exhibited different abundance and enrichment of immune cell infiltration. The high-risk group exhibited a higher frequency of tumor mutations than the low-risk group. TIDE and drug sensitivity analysis results demonstrated appropriate treatments for different risk groups, respectively.

Conclusions

A prognostic model for ccRCC with four signature genes, was established and demonstrated high predictive performance. Four signature genes provided a foundation for studying the mechanism of GTs-related genes in ccRCC progression.

Glycosylation in cancer as a source of biomarkers

INTRODUCTION

Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

AREAS COVERED

This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

EXPERT OPINION

Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.

Results from a real-world study: a novel glycosyltransferase risk score for prognosis, tumor microenvironment phenotypes and immunotherapy in bladder cancer

BACKGROUND

Although immunotherapy shows tremendous potential in the treatment of bladder cancer (BLCA), the overall prognosis and response rates to immunotherapy in BLCA remain suboptimal.

METHODS

We performed an extensive evaluation of glycosyltransferase expression patterns in BLCA patients by analyzing 210 glycosyltransferase-related genes. Subsequently, we established correlations between these glycosyltransferase patterns, prognosis, and tumor microenvironment (TME) phenotypes. To offer personalized patient assessments, we developed a glycosyltransferase risk score that accurately predicts prognosis, TME phenotypes, and molecular subtypes. Importantly, we developed a RNA-seq cohort, named Xiangya cohort, to validate our results.

RESULTS

Two distinct patterns of glycosyltransferase expression were identified, corresponding to inflamed and noninflamed TME phenotypes, and demonstrated the potential to predict prognosis. We developed and validated a comprehensive risk score that accurately predicted individual patient prognosis in the TCGA-BLCA cohort. Additionally, we constructed a nomogram that integrated the risk score with several key clinical factors. Importantly, this risk score was successfully validated in external cohorts, including the Xiangya cohort and GSE48075. Furthermore, we discovered a positive correlation between this risk score and tumor-infiltrating lymphocytes in both the TCGA-BLCA and Xiangya cohorts, suggesting that patients with a higher risk score exhibited an inflamed TME phenotype and were more responsive to immunotherapy. Finally, we observed that the high and low risk score groups were consistent with the luminal and basal subtypes of BLCA, respectively, providing further validation of the risk score's role in the TME in terms of molecular subtypes.

CONCLUSIONS

Glycosyltransferase patterns exhibit distinct TME phenotypes in BLCA. Our comprehensive risk score provides a promising approach for prognostic prediction and assessment of immunotherapy efficacy, offering valuable guidance for precision medicine.

An anoikis-related lncRNA signature is a useful tool for predicting the prognosis of patients with lung adenocarcinoma

Background

Anoikis-related long non-coding RNAs (ARLs) play a critical role in tumor metastasis and progression, suggesting that they may serve as risk markers for cancer. This study aimed to investigate the prognostic value of ARLs in patients with lung adenocarcinoma (LUAD).

Methods

Clinical data, RNA sequencing (RNA-seq) data, and mutation data from the LUAD project were obtained from The Cancer Genome Atlas (TCGA) database. The Molecular Signatures Database (MSigDB) and the GeneCard database were used to collect an anoikis-related gene (ARG) set. Pearson correlation analysis was performed to identify ARLs. LASSO and Cox regression were then used to establish a prognostic risk signature for ARLs. The median risk score served as the basis for categorizing patients into high and low-risk groups. Kaplan-Meier analysis was utilized to compare the prognosis between these two groups. The study also examined the associations between risk scores and prognosis, clinicopathological characteristics, immune status, tumor mutation burden (TMB), and chemotherapeutic agents. LncRNA expression was assessed using quantitative real-time PCR (qRT-PCR).

Results

A total of 480 RNA expression profiles, 501 ARGs, and 2698 ARLs were obtained from the database. A prognostic ARL signature for LUAD was established, consisting of 9 lncRNAs. Patients in the low-risk group exhibited significantly better prognosis compared to those in the high-risk group (P < 0.001). The 9 lncRNAs from the ARL signature were identified as independent prognostic factors (P < 0.001). The signature demonstrated high accuracy in predicting LUAD prognosis, with area under the curve values exceeding 0.7. The risk scores for ARLs showed strong negative correlations with stroma score (P = 5.9E-07, R = -0.23), immune score (P = 9.7E-09, R = -0.26), and microenvironment score (P = 8E-11, R = -0.29). Additionally, the low-risk group exhibited significantly higher TMB compared to the high-risk group (P = 4.6E-05). High-risk status was significantly associated with lower half-maximal inhibitory concentrations for most chemotherapeutic drugs.

Conclusion

This newly constructed signature based on nine ARLs is a useful instrument for the risk stratification of LUAD patients. The signature has potential clinical significance for predicting the prognosis of LUAD patients and guiding personalized immunotherapy.