A novel prognostic signature in osteosarcoma characterised from the perspective of unfolded protein response.
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ABSTRACT:
SUBMITTER: Shi C
PROVIDER: S-EPMC8958346 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
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A novel prognostic signature in osteosarcoma characterised from the perspective of unfolded protein response.
Shi Chengcheng C Zhao Faming F Zhang Tingting T Xu Denghui D Hao Zhuangyu Z Cui Fengzhen F Shi Ji-Hua JH Jin Yang Y Li Ningning N Yang Caihong C Zhang Yi Y Sheng Xia X
Clinical and translational medicine 20220301 3
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Project description:Background: Osteosarcoma is a highly malignant bone tumor commonly occurring in adolescents with a poor 5-year survival rate. The unfolded protein response (UPR) can alleviate the accumulation of misfolded proteins to maintain homeostasis under endoplasmic reticulum stress. The UPR is linked to the occurrence, progression, and drug resistance of tumors. However, the function of UPR-related genes (UPRRGs) in disease progression and prognosis of osteosarcoma remains unclear. Methods: The mRNA expression profiling and corresponding clinical features of osteosarcoma were acquired from TARGET and GEO databases. Consensus clustering was conducted to confirm different UPRRG subtypes. Subsequently, we evaluated the prognosis and immune status of the different subtypes. Functional analysis of GO, GSEA, and GSVA was used to reveal the molecular mechanism between the subtypes. Finally, four genes (STC2, PREB, TSPYL2, and ATP6V0D1) were screened to construct and validate a risk signature to predict the prognosis of patients with osteosarcoma. Result: We identified two subtypes according to the UPRRG expression patterns. The subgroup with higher immune scores, lower tumor purity, and active immune status was linked to a better prognosis. Meanwhile, functional enrichment revealed that immune-related signaling pathways varied markedly in the two subtypes, suggesting that the UPR might influence the prognosis of osteosarcoma via influencing the immune microenvironment. Moreover, prognostic signature and nomogram models were developed based on UPRRGs, and the results showed that our model has an excellent performance in predicting the prognosis of osteosarcoma. qPCR analysis was also conducted to verify the expression levels of the four genes. Conclusion: We revealed the crucial contribution of UPRRGs in the immune microenvironment and prognostic prediction of osteosarcoma patients and provided new insights for targeted therapy and prognostic assessment of the disease.
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Project description:Molecular pathways regulating the initiation and development of melanoma are potential therapeutic targets for this aggressive skin cancer. Therefore, transcriptome profiles of cutaneous melanoma were obtained from a public database and used to systematically evaluate cancer hallmark pathways enriched in melanoma. Finally, the unfolded protein response pathway was screened out, and the unfolded protein response-related genes were used to develop a robust biomarker that can predict the prognosis of melanoma, especially for younger, metastatic and high Clark level patients. This biomarker was further validated in two other independent datasets. In addition, melanoma patients were divided into high- and low-risk subgroups by applying a risk score system. The high-risk group exhibited higher immune infiltration and higher expression of N6-methyladenosine RNA methylation regulators, and had significantly shorter survival times than the low-risk subgroup. Gene Set Enrichment Analysis revealed that, among the enriched genes, gene sets involved in immune response and the extracellular matrix receptor interaction were significantly activated in the high-risk group. Our findings thus provide a new clinical application for prognostic prediction as well as potential targets for treatment of melanoma.
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Project description:Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.
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Project description:BackgroundOsteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.MethodWe retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.ResultIn our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.ConclusionThe five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.
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Project description:The unfolded protein response (UPR) plays a pivotal role in the pathogenesis and progression of acute myeloid leukemia (AML). This study aims to investigate the prognostic value of UPR-related genes (URGs) and establish a UPR-related gene signature (URGsig) to enhance prognosis prediction and guide therapeutic decision-making in AML. Gene expression profiles of AML patients were obtained from the GDC and GEO databases. Cox regression and LASSO regression analyses were applied to identify key genes for the construction of URGsig. Comprehensive bioinformatics analyses were conducted to elucidate the biological and clinical implications of URGsig. A nomogram integrating URGs and clinical prognostic features was developed to predict survival probability for AML patients. Additionally, the differential expression of core genes within the URGsig was validated in clinical samples. Notably, two distinct UPR-related subtypes were identified, and they displayed significant heterogeneity in clinical outcomes and tumor microenvironment (TME). The URGsig, comprising six URGs, showed a strong correlation with survival outcomes and exhibited robust predictive capabilities. Importantly, patients categorized into the high-risk subgroup based on URGsig were predicted to show lower chemosensitivity but a better response to immunotherapy. The nomogram performed well in prognosis prediction, with an area under the curve (AUC) of 0.912 for 5-year overall survival. In summary, our findings highlight the URGsig as a promising prognostic biomarker and provide novel insights into the mechanism by which UPR influences the immune landscape of AML. This paradigm may lay a foundation for the development of personalized treatment strategies for AML patients.
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Project description:PurposeMetabolic reprogramming, as one of the hallmarks of cancer, shows promising translational potential for cancer diagnosis, treatment and prognostic prediction. This study aims to construct and validate a prognostic prediction model for osteosarcoma based on glutamine metabolism-related genes.Materials and methodsA group of glutamine metabolism-related genes was identified from a public database and intersected with a list of osteosarcoma survival-related genes, and a risk score model based on sixteen glutamine metabolism-related genes was developed by using LASSO penalized Cox regression analysis.ResultsThe prognosis of patients in the high-risk group was significantly worse than that of patients in the low-risk group in the training dataset (high- vs low-risk, 5-year overall survival: 11% vs 88%, p < 0.0001) and in two other external validation cohorts (high- vs low-risk, 5-year overall survival: 39% vs 81%, p = 0.015; 50% vs 94%, p = 0.011).In addition, a novel nomogram was constructed by integrating the risk score and clinical characteristics, including age, sex, metastasis status and chemotherapy response. This nomogram had superior predictive power compared with a nomogram composed of only conventional factors. Gene set enrichment analysis indicated that several well-known malignancy-associated gene sets, including MYC targets V1, DNA repair, and unfolded protein response, were enriched in the high-risk subgroup.ConclusionA novel glutamine metabolism-related prognostic prediction model and nomogram for osteosarcoma was developed and validated in the present study, which could predict the survival of patients with osteosarcoma and may facilitate individualized clinical decision-making for patients.
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Project description:Osteosarcoma is the most common malignant bone tumor, and although there has been significant progress in its management, metastases often herald incurable disease. Here we defined genes differentially expressed between primary and metastatic osteosarcoma as metastasis-related genes (MRGs) and used them to construct a novel six-MRG prognostic signature for overall survival of patients with osteosarcoma. Validation in internal and external datasets confirmed satisfactory accuracy and generalizability of the prognostic model, and a nomogram based on the signature and clinical variables was constructed to aid clinical decision-making. Of the six MRGs, FHIT is a well-documented tumor suppressor gene that is poorly defined in osteosarcoma. Consistent with tumor suppressor function, FHIT was downregulated in osteosarcoma cells and human osteosarcoma samples. FHIT overexpression inhibited osteosarcoma proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, FHIT overexpression upregulate the epithelial marker E-cadherin while repressing the mesenchymal markers N-cadherin and vimentin. Our six-MRG signature represents a novel and clinically useful prognostic biomarker for patients with osteosarcoma, and FHIT might represent a therapeutic target by reversing epithelial to mesenchymal transition.
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Project description:BRAFV600E mutations occur in ∼10% of colorectal cancer cases, are associated with poor survival, and have limited responses to BRAF/MEK inhibition with or without EGFR inhibition. There is an unmet need to understand the biology of poor prognostic BRAFMT colorectal cancer. We have used differential gene expression and pathway analyses of untreated stage II and stage III BRAFMT (discovery set: n = 31; validation set: n = 26) colorectal cancer, and an siRNA screen to characterize the biology underpinning the BRAFMT subgroup with poorest outcome. These analyses identified the unfolded protein response (UPR) as a novel and druggable pathway associated with the BRAFMT colorectal cancer subgroup with poorest outcome. We also found that oncogenic BRAF drives endoplasmic reticulum (ER) stress and UPR pathway activation through MEK/ERK. Furthermore, inhibition of GRP78, the master regulator of the UPR, using siRNA or small molecule inhibition, resulted in acute ER stress and apoptosis, in particular in BRAFMT colorectal cancer cells. In addition, dual targeting of protein degradation using combined Carfilzomib (proteasome inhibitor) and ACY-1215 (HDAC6-selective inhibitor) treatment resulted in marked accumulation of protein aggregates, acute ER stress, apoptosis, and therapeutic efficacy in BRAFMT in vitro and xenograft models. Mechanistically, we found that the apoptosis following combined Carfilzomib/ACY-1215 treatment is mediated through increased CHOP expression. Taken together, our findings indicate that oncogenic BRAF induces chronic ER stress and that inducers of acute ER stress could be a novel treatment strategy for poor prognostic BRAFMT colorectal cancer. Mol Cancer Ther; 17(6); 1280-90. ©2018 AACR.
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Project description: Not available
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