Comprehensive analysis of the endoplasmic reticulum stress-related long non-coding RNA in bladder cancer

Comprehensive pan-cancer analysis of YBX family reveals YBX2 as a potential biomarker in liver cancer

Background

The Y-box-binding proteins (YBX) act as a multifunctional role in tumor progression, metastasis, drug resistance by regulating the transcription and translation process. Nevertheless, their functions in a pan-cancer setting remain unclear.

Methods

This study examined the clinical features expression, prognostic value, mutations, along with methylation patterns of three genes from the YBX family (YBX1, YBX2, and YBX3) in 28 different types of cancer. Data used for analysis were obtained from Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. A novel YBXs score was created using the ssGSEA algorithm for the single sample gene set enrichment analysis. Additionally, we explored the YBXs score's association with the tumor microenvironment (TME), response to various treatments, and drug resistance.

Results

Our analysis revealed that YBX family genes contribute to tumor progression and are indicative of prognosis in diverse cancer types. We determined that the YBXs score correlates significantly with numerous malignant pathways in pan-cancer. Moreover, this score is also linked with multiple immune-related characteristics. The YBXs score proved to be an effective predictor for the efficacy of a range of treatments in various cancers, particularly immunotherapy. To summarize, the involvement of YBX family genes is vital in pan-cancer and exhibits a significant association with TME. An elevated YBXs score indicates an immune-activated TME and responsiveness to diverse therapies, highlighting its potential as a biomarker in individuals with tumors. Finally, experimental validations were conducted to explore that YBX2 might be a potential biomarker in liver cancer.

Conclusion

The creation of YBXs score in our study offered new insights into further studies. Besides, YBX2 was found as a potential therapeutic target, significantly contributing to the improvement of HCC diagnosis and treatment strategies.

Illuminating Shared Genetic Associations Between Oesophageal Carcinoma and Pulmonary Carcinoma Risk

Background: Lung cancer and oesophageal cancer are prevalent malignancies with rising incidence and mortality worldwide. While some environmental and behavioural risk factors for these cancers are established, the contribution of genetic factors to their pathogenesis remains incompletely defined. This study aimed to interrogate the intricate genetic relationship between lung cancer and oesophageal cancer and their potential comorbidity. Methods: We utilised linkage disequilibrium score regression (LDSC) to analyse the genetic correlation between oesophageal carcinoma and lung carcinoma. We then employed several approaches, including pleiotropic analysis under the composite null hypothesis (PLACO), multi-marker analysis of genomic annotation (MAGMA), cis-expression quantitative trait loci (eQTL) analysis, and a pan-cancer assessment to identify pleiotropic loci and genes. Finally, we performed bidirectional Mendelian randomisation (MR) to evaluate the causal relationship between these malignancies. Results: LDSC revealed a significant genetic correlation between oesophageal carcinoma and lung carcinoma. Further analysis identified shared gene loci including PGBD1, ZNF323, and WNK1 using PLACO. MAGMA identified enriched pathways and 9 pleiotropic genes including HIST1H1B, HIST1H4L, and HIST1H2BL. eQTL analysis integrating oesophageal, lung, and blood tissues revealed 26 shared genes including TERT, NKAPL, RAD52, BTN3A2, GABBR1, CLPTM1L, and TRIM27. A pan-cancer exploration of the identified genes was undertaken. MR analysis showed no evidence for a bidirectional causal relationship between oesophageal carcinoma and lung carcinoma. Conclusions: This study provides salient insights into the intricate genetic links between lung carcinoma and oesophageal carcinoma. Utilising multiple approaches for genetic correlation, locus and gene analysis, and causal assessment, we identify shared genetic susceptibilities and regulatory mechanisms. These findings reveal new leads and targets to further elucidate the genetic basis of lung and oesophageal carcinoma, aiding development of preventive and therapeutic strategies.

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection

Background

Breast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies.

Methods

Through comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy.

Results

The developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC.

Conclusion

Our study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

GPX3-Mediated Oxidative Stress Affects Pyrimidine Metabolism Levels in Stomach Adenocarcinoma via the AMPK/mTOR Pathway

Background

Amino acid metabolism, including ATP production, nucleotide synthesis, and redox homeostatic processes, are associated with proliferation and differentiation of tumor cells. This study aimed to identify novel prognostic biomarkers and potential therapeutic targets of amino acid metabolism-related genes for stomach adenocarcinoma (STAD).

Methods

RNA sequencing transcriptome data in the TCGA-STAD (training set) and GTEx datasets (validation set) were used. The LIMMA R program enabled the differentially expressed amino acid metabolism-related genes (AAMRGs) to be found. A prognostic risk score model based on clinical phenotypic features was built using LASSO regression and step multi-Cox analyses. Gene set enrichment analysis (GSEA) was used to find potential molecular pathways associated with STAD. Hierarchical cluster analysis was used to evaluate pyrimidine metabolism. Cultured STAD cells assessed the proliferation of STAD and upregulation of GPX3 expression by CCK8 and flow cytometry. Transwell and wound healing assays assessed the impact of GPX3 on invasion and migration of STAD cells. Western blot and qRT-PCR were used to measure changes in pyrimidine metabolism-related markers and active molecules involved in the AMPK/mTOR signaling pathway.

Results

Three AAMRGs, DNMT1, F2R, and GPX3, could independently predict the course of STAD. Pyrimidine metabolism appeared to be significantly associated with these by GSEA and clustering analyses. Pyrimidine metabolism was negatively correlated with GPX3. Functional studies using an overexpressed GPX3 plasmid showed an enhanced migration and invasion of STAD cells as well as the expression of genes associated with pyrimidine metabolism and the AMPK/mTOR signaling pathway. By using a CAD siRNA, it was found that that GPX3 affected 5-fluorouracil resistance during de novo synthesis of pyrimidine through the CAD-UMPS signaling axis.

Conclusions

GPX3 which regulates the level of pyrimidine metabolism through the AMPK/mTOR pathway was found to be closely associated with STAD. Our findings demonstrate GPX3 is a reliable biomarker for the prognosis of amino acid metabolism and a probable target for STAD therapy.

An endoplasmic reticulum stress-related signature featuring ASNS for predicting prognosis and immune landscape in prostate cancer

Prostate cancer (PRAD) is one of the common malignant tumors of the urinary system. In order to predict the treatment results for PRAD patients, this study proposes to develop a risk profile based on endoplasmic reticulum stress (ERS). Based on the Memorial Sloan-Kettering Cancer Center (MSKCC) cohort and the Gene Expression Omnibus database (GSE70769), we verified the predictive signature. Using a random survival forest analysis, prognostically significant ERS-related genes were found. An ERS-related risk score (ERscore) was created using multivariable Cox analysis. In addition, the biological functions, genetic mutations and immune landscape related to ERscore are also studied to reveal the underlying mechanisms related to ERS in PRAD. We further explored the ERscore-related mechanisms by profiling a single-cell RNA sequencing (scRNA-seq) dataset (GSE137829) and explored the oncogenic role of ASNS in PRAD through in vitro experiments. The risk signature composed of eight ERS-related genes constructed in this study is an independent prognostic factor and validated in the MSKCC and GSE70769 data sets. The scRNA-seq data additionally revealed that several carcinogenic pathways were noticeably overactivated in the group with high ERS scores. As one of the prognostic genes, ASNS will significantly inhibit the proliferation, migration and invasion abilities of PRAD cells after its expression is interfered with. In conclusion, this study developed a novel risk-specific ERS-based clinical treatment strategy for patients with PRAD.

Unraveling the activation process and core driver genes of HSCs during cirrhosis by single-cell transcriptome

Worldwide, cirrhosis is a common cause of death, manifesting itself as fibrosis of the liver tissue. When the liver is damaged, the liver produces fibrotic, proliferative myofibroblasts, which are formed by the differentiation of activated hepatic stellate cells. There are no effective antifibrotic treatment options. To deeply explore the activation process of hepatic stellate cells (HSCs) and to discover better therapeutic target genes, single-cell RNA sequencing data on 13 non-cirrhotic liver tissue samples and 10 cirrhotic liver tissue samples were analyzed. We identified activated HSCs from the mesenchymal cell population with high expression of ACTA2. By pseudo-time analysis, we found that the key genes for the differentiation of HSCs into myofibroblasts were C3, CCDC80, COL1A1, COL3A1, DCN, FBLN1, IGFBP3, MXRA5, SERPINE1, and MYH11. Then, we found that the main regulators of HSCs from inactive to activated state were NTF3, NTRK3, NTRK2, JAG1, NOTCH3, ESAM, and CD46 by cell-cell communication analysis. In addition, we found that the top2 hub genes of activated HSCs were CRIP1 and ACTA2. The experimental results show that the top2 hub genes were significantly overexpressed in cirrhotic samples. Our work dissected key intercellular regulators and core driver genes during hepatic stellate cell activation during cirrhosis through single-cell transcriptome data analysis, providing a research strategy to discover rational therapeutic targets for cirrhosis and some important information for gene targeting therapy.

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.

Methods

We applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.

Results

The scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS's prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.

Conclusion

Our research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Background

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).

Methods

We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.

Results

We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.

Conclusion

Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

Abandonment of intravenous volume expansion after preoperative receipt of α-blockers in patients with adrenal pheochromocytoma was not an independent risk factor for intraoperative hemodynamic instability

Background

There is no consensus on whether intravenous rehydration must be added after preoperative phenoxybenzamine (PXB) administration for pheochromocytoma. The aim of this study is to investigate whether abandonment of intravenous volume expansion after PXB administration is associated with intraoperative hemodynamic instability.

Methods

83 Patients with pheochromocytoma received surgical treatment in the Department of Urology, Handan First Hospital, between October 2014 and July 2022. All patients were subclassified into either the hemodynamic stability group (HS group) or the hemodynamic instability group (HU group) according to whether intraoperative hemodynamic instability occurred, with 51 cases in HS group and 32 cases in HU group. Differences in data between the two groups were examined, and the risk factors for intraoperative hemodynamic instability were analyzed using logistic regression.

Results

The results of the analysis showed no statistically significant differences in age, sex, location of the tumor, surgical method, body mass index (BMI) ≥ 24 kg/m², blood and urine catecholamine test results, preoperative oral PXB followed by combined intravenous volume expansion, proportion of patients with hypertension or diabetes mellitus or coronary heart disease between the two groups (P>0.05). The size of the tumor in the HS group was smaller than that in the HU group (5.3 ± 1.9 cm vs 6.2 ± 2.4 cm P=0.010). Multivariate analyses demonstrated that abandonment of intravenous volume expansion after preoperative receipt of α-blockers in patients with adrenal pheochromocytoma was not an independent risk factor for intraoperative hemodynamic instability. Only the tumor size (P=0.025) was an independent risk factor for intraoperative hemodynamic instability.

Conclusion

The purpose of general preoperative intravenous fluid expansion is to prevent hypotension after the tumor has been resected. In the current study, we indicated that preoperative management of pheochromocytomas using the α-blocker PXB in combination with intravenous volume expansion does not further reduce the risk of intraoperative hemodynamic instability or postoperative complications compared with oral PXB alone. Therefore, our study supports preoperative management of pheochromocytoma with a single α-blocker, PXB, as sufficient.