Identification and validation of eight estrogen-related genes for predicting prognosis of papillary thyroid cancer.
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ABSTRACT:
SUBMITTER: Zeng Y
PROVIDER: S-EPMC10042678 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
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Identification and validation of eight estrogen-related genes for predicting prognosis of papillary thyroid cancer.
Zeng Yu Y Ma Weike W Li Lijuan L Zhuang Gaojian G Luo Guoqing G Zhou Hong H Hao Weijing W Liu Yu Y Guo Fengli F Tian Mengran M Ruan Xianhui X Gao Ming M Zheng Xiangqian X
Aging 20230313 5
Papillary thyroid cancer (PTC) is one of the most common malignant tumors in female, and estrogen can affect its progression. However, the targets and mechanisms of estrogen action in PTC remain unclear. Therefore, this study focuses on the relationship between estrogen-related genes (ERGs) expression and prognosis in PTC, particularly neuropeptide U (NMU), and its important role in tumor progression. Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differenti ...[more]
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Project description:ObjectiveThe current research aimed to development and validation in signature immune genes for lymphatic metastasis in papillary thyroid cancer (PTC).MethodWeighted correlation network analysis (WGCNA) was performed to identify genes closely correlated with lymphatic metastasis in PTC from TCGA database. Information on immune-related genes (IRGs) was obtained from the ImmPort database. Crossover genes were used with the R package clusterProfiler for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. Key genes in the protein-protein interaction network of cross-targets were obtained using Cytoscape. Lasso and Random Forest (RF) models were utilized to identify pivotal genes. We constructed a nomogram based on the hub genes. The correlation between hub genes and immune cell infiltration was explored. We collected and assessed clinical samples via immunohistochemistry to detect the expression of hub genes.ResultIn total, 122 IRGs were correlated with lymphatic metastases from PTC. There are 10 key IRGs in the protein-protein interaction network. Then, three hub genes including PTGS2, MET, and ICAM1 were established using the LASSO and RF models. The expression of these hub genes was upregulated in samples collected from patients with lymphatic metastases. The average area under the curve of the model reached 0.83 after a 10-fold and 200-time cross-validation, which had a good prediction ability. Immuno-infiltration analysis showed that the three hub genes were significantly positively correlated with resting dendritic cells and were negatively correlated with activated natural cells, monocytes, and eosinophils. Immunohistochemistry results revealed that lymph node metastasis samples had a higher expression of the three hub genes than non-metastasis samples.ConclusionVia bioinformatics analysis and experimental validation, MET and ICAM1 were found to be upregulated in lymph node metastasis from papillary thyroid carcinoma. Further, the two hub genes were closely correlated with activated natural killer cells, monocytes, resting dendritic cells, and eosinophils. Therefore, these two genes may be novel molecular biomarkers and therapeutic targets in lymph node metastasis from papillary thyroid carcinoma.
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Project description:BackgroundThyroid cancer (THCA) has become increasingly common in recent decades, and women are three to four times more likely to develop it than men. Evidence shows that estrogen has a significant impact on THCA proliferation and growth. Nevertheless, the effects of estrogen-related genes (ERGs) on THCA stages, immunological infiltration, and treatment susceptibility have not been well explored.MethodsClinicopathological and transcriptome data of patients with THCA from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were cleaned before consensus clustering. Differential expression analysis was performed on the genes expressed between THCA and paraneoplastic tissues in TCGA, and Wayne analysis was performed on the ERGs obtained from the Gene Set Enrichment Analysis MsigDB and differentially expressed genes (DEGs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were used to identify the set of estrogen-related differentially expressed genes (ERDEGs) associated with progression-free intervals (PFI) and to establish a prediction model. Receiver operating characteristic curves were plotted to calculate the risk scores and PFI status to validate the predictive effect of the model. Enrichment analyses and immune infiltration analyses were performed to analyze DEGs between the high- and low-risk groups, and a nomogram plot was used in the risk model to predict the PFI of THCA.ResultsThe expression of 120 ERDEGs differed significantly between the two groups (P < 0.05). Five (CD24, CAV1, TACC1, TIPARP, and HSD17B10) of the eight ERDEGs identified using univariate Cox and LASSO regression were validated via RT-qPCR and immunohistochemistry analysis of clinical tissue samples and were used for clinical staging and drug sensitivity analysis. Risk-DEGs were shown to be associated with immune modulation and tumor immune evasion, as well as defense systems, signal transduction, the tumor microenvironment, and immunoregulation. In 19 of the 28 immune cells, infiltration levels differed between the high- and low-risk groups. High-risk patients in the immunotherapy dataset had considerably shorter survival times than low-risk patients.ConclusionWe identified and confirmed eight ERDEGs using a systematic analysis and screened sensitive drugs for ERDEGs. These results provide molecular evidence for the involvement of ERGs in controlling the immunological microenvironment and treatment response in THCA.
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Project description:BackgroundOvarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.MethodsA comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.ResultsA risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.ConclusionsA fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.
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Project description:Bladder cancer (BC) is fatal during muscle invasion and treatment progress is limited. In this study, we aimed to construct and validate basement membrane (BM)-associated gene prognosis to predict BC progression and tumor immune infiltration correlation. We choreographed BM-related genes in the Cancer Genome Atlas (TCGA) database using COX regression and least absolute shrinkage and selection operator (LASSO) analysis, and the predictive value of BM-related genes was further validated by the GSE32548, GSE129845, and immunohistochemistry staining. All analyses were performed with R-version 4.2.2, and its appropriate packages. Three genes were identified to construct a gene signature to predictive of BC prognosis. We divided the TCGA database into 2 groups, and patients in the high-risk group had worse overall survival (OS) than those in the low-risk group. In GSE32548, we confirmed that patients in the high-risk group had a poorer prognosis compared to those in the low-risk group in terms of OS. Immunohistochemical staining of EPEMP1, GPC2, and ITGA3 showed significantly higher expression at the protein level in BC tissues than in normal tissues. The Spearman analysis showed risk score was positively correlated with B cell naïve, Macrophages M2, and Mast cells resting. stromal score, immune score, and ESTIMATE scores were significantly higher in the high-risk group. drugs sensitivity analysis showed IC50 of Cisplatin, Gemcitabine, and Methotrexate in the high-risk group was significantly higher than that in the low-risk group. We identified 3 prognostic genes from a novel perspective of BM genes as effective risk stratification tools for BC patients.
| S-EPMC11398827 | biostudies-literature
Project description:BackgroundPapillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation.MethodsThe thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.ResultsWe identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.ConclusionsOur study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.
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Project description:BackgroundThyroid cancer is the most common malignant tumor of the endocrine system. Most patients with thyroid cancer have a good prognosis, although a small proportion experience recurrence and metastasis and have a poor prognosis. Ferroptosis is a novel form of regulated cell death (RCD); previous studies have confirmed that ferroptosis was associated with thyroid cancer. The purpose of this study was to investigate the key ferroptosis-related genes in thyroid cancer and their relationship with prognosis and immune cell infiltration.MethodsIn this study, 497 thyroid cancer RNA expression datasets were downloaded from the cancer genome atlas (TCGA) cohort and a prognostic risk model for eight ferroptosis-related genes (FRGs) was constructed by Lasso-Cox regression. The prognostic value of the risk model and the correlation of prognostic features with immune scores and tumor immune cell infiltration were systematically analyzed.ResultsThe prognostic risk model for eight FRGs (DPP4, TYRO3, TIMP1, CDKN2A, SNCA, NR4A1, IL-6 and FABP4) were constructed and validated in training and testing cohorts. Kaplan-Meier curve and receiver operating characteristic (ROC) curve analysis confirmed that that the ferroptosis-related eight gene signature had good predictive value for the prognosis of thyroid cancer (THCA) patients. Multivariate regression analysis further showed that the risk score of the prognostic model could be used as an independent prognostic factor for THCA patients. Functional enrichment analysis showed that DEGs in high risk and low risk groups were involved in immune-related biological processes and that there were significant differences in immune cell infiltration between the two risk groups.ConclusionWe identified eight key genes related to ferroptosis in THCA patients. Further studies are now needed to investigate the mechanisms involved; these genes may represent clinical diagnostic and prognostic biomarkers.
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Project description:To identify immune-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC). The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to obtain the gene expression profile. Immune-related lncRNAs were screened from the Molecular Signatures Database v4.0 (MsigDB). We performed a survival analysis of critical lncRNAs. Further, the function of prognostic lncRNAs was inferred using the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) to clarify the possible mechanisms underlying their predictive ability. The assessment was performed in clinical samples and PTC cells. We obtained 4 immune-related lncRNAs, 15 microRNAs (miRNAs), and 375 mRNAs as the key mediators in the pathophysiological processes of PTC from the GEO database. Further, Lasso regression analysis identified seven prognostic markers (LINC02550, SLC26A4-AS1, ACVR2B-AS1, AC005479.2, LINC02454, and AL136366.1), most of which were related to tumor development. The KEGG pathway enrichment analysis showed different, changed genes mainly enriched in the cancer-related pathways, PI3K-Akt signaling pathway, and focal adhesion. Only SLC26A4-AS1 had an intersection in the results of the two databases. LncRNA SLC26A4-AS1, which is the most associated with prognosis, may play an oncogenic role in the development of PTC.
| S-EPMC7902395 | biostudies-literature
Project description:Background: Necroptosis, a type of programmed cell death, has been implicated in a variety of cancer-related biological processes. However, the roles of necroptosis-related genes in thyroid cancer yet remain unknown. Methods: A necroptosis-related gene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression analysis. The predictive value of the prognostic signature was validated in an internal cohort. Additionally, the single-sample gene set enrichment analysis (ssGSEA) was used to examine the relationships between necroptosis and immune cells, immunological functions, and immune checkpoints. Next, the modeled genes expressions were validated in 96 pairs of clinical tumor and normal tissue samples. Finally, the effects of modeled genes on PTC cells were studied by RNA interference approaches in vitro. Results: In this study, the risk signature of seven necroptosis-related genes was created to predict the prognosis of papillary thyroid cancer (PTC) patients, and all patients were divided into high- and low-risk groups. Patients in the high-risk group fared worse in terms of overall survival than those in the low-risk group. The area under the curve (AUC) of the receiving operating characteristic (ROC) curves proved the predictive capability of created signature. The risk score was found to be an independent risk factor for prognosis in multivariate Cox analysis. The low-risk group showed increased immune cell infiltration and immunological activity, implying that they might respond better to immune checkpoint inhibitor medication. Next, GEO database and qRT-PCR in 96 pairs of matched tumorous and non-tumorous tissues were used to validate the expression of the seven modeled genes in PTCs, and the results were compatible with TCGA database. Finally, overexpression of IPMK, KLF9, SPATA2 could significantly inhibit the proliferation, invasion and migration of PTC cells. Conclusion: The created necroptosis associated risk signature has the potential to have prognostic capability in PTC for patient outcome. The findings of this study could pave the way for further research into the link between necroptosis and tumor immunotherapy.
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Project description:Lower-grade gliomas (LrGG), characterized by invasiveness and heterogeneity, remain incurable with current therapies. Clinicopathological features provide insufficient information to guide optimal individual treatment and cannot predict prognosis completely. Recently, an increasing amount of studies indicate that the tumor microenvironment plays a pivotal role in tumor malignancy and treatment responses. However, studies focusing on the tumor microenvironment (TME) of LrGG are still limited. In this study, taking advantage of the currently popular computational methods for estimating the infiltration of tumor-associated normal cells in tumor samples and using weighted gene co-expression network analysis, we screened the co-expressed gene modules associated with the TME and further identified the prognostic hub genes in these modules. Furthermore, eight prognostic hub genes (ARHGDIB, CLIC1, OAS3, PDIA4, PARP9, STAT1, TAP2, and TAGLN2) were selected to construct a prognostic risk score model using the least absolute shrinkage and selection operator method. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor for LrGG. Moreover, time-dependent ROC curves indicated that our model had favorable efficiency in predicting both short- and long-term prognosis in LrGG patients, and the stratified survival analysis demonstrated that our model had prognostic value for different subgroups of LrGG patients. Additionally, our model had potential value for predicting the sensitivity of LrGG patients to radio- and chemotherapy. Besides, differential expression analysis showed that the eight genes were aberrantly expressed in LrGG compared to normal brain tissue. Correlation analysis revealed that the expression of the eight genes was significantly associated with the infiltration levels of six types of immune cells in LrGG. In summary, the TME-related eight-gene signature was significantly associated with the prognosis of LrGG patients. They might act as potential prognostic biomarkers for LrGG patients, offer better stratification for future clinical trials, and be candidate targets for immunotherapy, thus deserving further investigation.
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Project description:BackgroundWe aimed to build a novel model with golgi apparatus related genes (GaGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).MethodsWe performed a bioinformatic analysis of integrated PTC datasets with the GaGs to identify differentially expressed GaGs (DE-GaGs). Then we generated PFI-related DE-GaGs and established a novel GaGs based signature. After that, we validated the signature on multiple external datasets and PTC cell lines. Further, we conducted uni- and multivariate analyses to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC.ResultsWe identified 260 DE-GaGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic glycoprotein biosynthetic process. Consequently, we established and optimized a novel 11 gene signature that could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.78, and the relevant nomogram had a C-index of 0.79. Also, it was closely related to the pivotal clinical characters of and anaplastic potential in datasets and PTC cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC's PFI.ConclusionThe GaGs signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.
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