Construction and Validation of a Novel Pyroptosis-Related Gene Signature to Predict the Prognosis of Uveal Melanoma.
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ABSTRACT:
SUBMITTER: Cao Y
PROVIDER: S-EPMC8662541 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
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Construction and Validation of a Novel Pyroptosis-Related Gene Signature to Predict the Prognosis of Uveal Melanoma.
Cao Yuan Y Xie Jiaheng J Chen Liang L Hu Yiming Y Zhai Leili L Yuan Jin J Suo Long L Shen Yaming Y Ye Rong R Li Jiajun J Gong Zixuan Z Dong Yunfan Y Bao Wei W Li Huan H Wang Ming M
Frontiers in cell and developmental biology 20211126
Uveal melanoma is the most common primary intraocular tumor with a poor prognosis. Currently, treatment for UVM is limited, and the development of drug resistance and tumor recurrence are common. Therefore, it is important to identify new prognostic biomarkers of UVM and explore their role in the tumor microenvironment. Pyroptosis is a way of cell programmed death, and related research is in full throttle. However, the role of pyroptosis in UVM is unclear. In this study, we constructed the progn ...[more]
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Project description:The present study aimed to construct a pyroptosis-related gene signature in uveal melanoma (UM) patients. Patients from The Cancer Genome Atlas (TCGA) served as the training cohort, whereas patients (GSE22138) from Gene Expression Omnibus (GEO) served as the validation cohort. Using the Kaplan-Meier (KM) method, univariate analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression, A five pyroptosis-related gene signature was constructed in the training cohort. Patients were divided into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time. Risk and survival analysis, time-independent receiver operating characteristic (ROC) curve analysis and principal component analysis (PCA) validated that the prognostic signature had greater predictive value in both cohorts. Multivariate analysis proved that the risk score was an independent prognostic factor. Functional analysis showed that the expressed genes in the high-risk group were most abundant in immunological repose-related and tumor-related signaling pathways. Single-sample gene-set enrichment analysis (ssGSEA) revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the predictive signature could help patients be better matched to immunotherapy and targeted treatments. In conclusion, the pyroptosis-related gene signature associated with the tumor microenvironment maybe a reliable tool for predicting the prognosis of UM patients.
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Project description:Uveal melanoma (UM) is the most common intraocular tumor in adults. Ferroptosis is a newly recognized process of cell death, which is different from other forms of cell death in terms of morphology, biochemistry and genetics, and has played a vital role in cancer biology. The present research aimed to construct a gene signature from ferroptosis-related genes that have the prognostic capacity of UM. UM patients from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and GSE22138 from Gene Expression Omnibus (GEO) was treated as the validation cohort. A total of 103 ferroptosis-related genes were retrieved from the GeneCards. We performed Kaplan-Meier and univariate Cox analysis for preliminary screening of ferroptosis-related genes with potential prognostic capacity in the training cohort. These genes were then applied into an overall survival-based LASSO Cox regression model, constructing a gene signature. The discovered gene signature was then evaluated via Kaplan-Meier (KM), Cox, and ROC analyses in both cohorts. The Pearson correlation coefficient examined the correlations between risk score and UM common mutations and autophagy. The analyses of GSEA and immune infiltrating were performed to better study the functional annotation of the gene signature and the character of each kind of immune cell in the tumor microenvironment. A seven-gene signature was found from the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, revealing its independent prognosis value in UM. Moreover, ROC analysis was conducted, confirming the strong predictive ability that this signature had for UM prognosis. A total of 52.24% (256/490) autophagy-related genes were significantly correlated with risk scores. Analyses of GSEA and immune infiltrating detailed exhibited specific pathways associated with the seven-gene signature, also confirming the crucial role that Mast cells resting played in the prognosis of the seven-gene signature. In this study, a novel ferroptosis-related seven-gene signature (ALOX12, CD44, MAP1LC3C, STEAP3, HMOX1, ITGA6, and AIFM2/FSP1) was built. It could accurately predict UM prognosis and was related to Mast cells resting, which provides the potential for personalized outcome prediction and the development of new therapies in the UM population.
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Project description:Background: Colorectal cancer (CRC) ranks as the third most common malignancy worldwide but a reliable prognostic biomarker of CRC is still lack. Thus, the purpose of our study was to explore whether ferroptosis - related lncRNAs could predict the prognosis of CRC. Methods: The mRNA expression profiling of colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) patients in The Cancer Genome Atlas (TCGA) database were downloaded. Univariate Cox and multivariate Cox regression analyses was used to obtain prognostic differently expressed ferroptosis-related lncRNAs (DE-FLs) and a risk signature was developed. Quantitative polymerase chain reaction (q-PCR) was used to validated the different expressions of DE-FLs. The calibration curves, C-index and the receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of nomogram. Gene set enrichment analyses (GSEA) were carried out to explore the biological mechanism between high- and low-risk group and the potential regulated pathway of prognostic DE-FLs in CRC. Results: Forty-nine DE-FLs were identified between CRC and normal tissue. Then, a 4-DE-FLs (AC016027.1, AC099850.3, ELFN1-AS1, and VPS9D1-AS1) prognostic signature model was generated. AC016027.1 was downregulated in CRC tissue; VPS9D1-AS1 and ELFN1-AS1 were upregulated by q-PCR. The model had a better accuracy presenting by 1-, 3-, and 5-years ROC curve (AUC ≥0.6), and identified survival probability (p < 0.05) well. Moreover, the risk signature could play as an independent factor of CRC (p < 0.05). Further, a nomogram including age, pathologic stage, T stage, and risk score with good prognostic capability (C-index = 0.789) was constructed. In addition, we found biological pathways mainly related to metabolism and apoptosis were down-regulated in high-risk group who with poor outcome. Finally, the functional enrichment showed prognostic DE-FLs may significantly impact bile secretion in CRC. Conclusion: A risk model and nomogram based on ferroptosis-related lncRNAs were created to predict 1-, 3-, and 5-years survival probability of CRC patients. Our data suggested that the prognostic lncRNAs could serve as valuable prognostic marker.
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Project description:BackgroundFerroptosis is a newly iron-dependent mode of programmed cell death that is involved in a variety of malignancies. But no research has shown a link between ferroptosis-related long non-coding RNAs (FRLs) and uveal melanoma (UM). We aimed to develop a predictive model for UM and explore its potential function in relation to immune cell infiltration.MethodsIdentification of FRLs was performed using the Cancer Genome Atlas (TCGA) and FerrDb databases. To develop a prognostic FRLs signature, univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in training cohort. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were used to assess the reliability of the risk model. The immunological functions of FRLs signature were determined using gene set enrichment analysis (GSEA). Immunological cell infiltration and immune treatment were studied using the ESTIMATE, CIBERSORT, and ssGSEA algorithms. Finally, in vitro assays were carried out to confirm the biological roles of FRLs with known primer sequences (LINC00963, PPP1R14B.AS1, and ZNF667.AS1).ResultsA five-genes novel FRLs signature was identified. The mean risk score generated by this signature was used to create two risk groups. The high-risk score UM patients had a lower overall survival rate. The area under the curve (AUC) of ROC and K-M analysis further validated the strong prediction capacity of the prognostic signature. Immune cells such as memory CD8 T cells, M1 macrophages, monocytes, and B cells showed a substantial difference between the two groups. GSEA enrichment results showed that the FRLs signature was linked to certain immune pathways. Moreover, UM patients with high-risk scores were highly susceptible to several chemotherapy drugs, such as cisplatin, imatinib, bortezomib, and pazopanib. Finally, the experimental validation confirmed that knockdown of three identified lncRNA (LINC00963, PPP1R14B.AS1, and ZNF667.AS1) suppressed the invasive ability of tumor cells in vitro.ConclusionThe five-FRLs (AC104129.1, AC136475.3, LINC00963, PPP1R14B.AS1, and ZNF667.AS1) signature has effects on clinical survival prediction and selection of immunotherapies for UM patients.
| S-EPMC9238413 | biostudies-literature
Project description:BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults. Epithelial-mesenchymal transition (EMT) is an essential regulator of the UVM's immune microenvironment. However, the precise role of EMT in UVM remains to be explored and the development of a related treatment strategy is urgently needed.MethodsMultiomics data and clinical information for TCGA-UVM were used to identify the EMT subtypes and analyze their regulatory role in the immune microenvironment in UVM. A machine-learning method based on the identified subtypes was utilized to construct the EMT feature-based prognostic model. External validation cohorts GSE84976 and GSE22138 were employed to validate the model's robustness. Immunotherapy cohort IMvigor210 was used to explore the model's potential to predict immunotherapy responsiveness.ResultsTwo EMT subtypes were identified in UVM. The role of EMT in shaping the immune microenvironment and regulating cancer-immunity circle of UVM was analyzed. A robust prognostic model was presented and validated to predict patient prognosis. The model also predicted patient's immune features and immunotherapy responsiveness.ConclusionThe EMT-mediated immune features in UVM were illustrated, providing a reliable model to facilitate precise UVM treatment. This research may assist in decision-making during clinical UVM therapy.
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Project description:BackgroundIGFBP3 plays a pivotal role in carcinogenesis by being anomalously expressed in some malignancies. However, the clinical value of IGFBP3 and the role of IGFBP3-related signature in HCC remain unclear.MethodsMultiple bioinformatics methods were used to determine the expression and diagnostic values of IGFBP3. The expression level of IGFBP3 was validated by RT-qPCR and IHC. A IGFBP3-related risk score (IGRS) was built via correlation analysis and LASSO Cox regression analysis. Further analyses, including functional enrichment, immune status of risk groups were analyzed, and the role of IGRS in guiding clinical treatment was also evaluated.ResultsIGFBP3 expression was significantly downregulated in HCC. IGFBP3 expression correlated with multiple clinicopathological characteristics and demonstrated a powerful diagnostic capability for HCC. In addition, a novel IGRS signature was developed in TCGA, which exhibited good performance for prognosis prediction and its role was further validated in GSE14520. In TCGA and GSE14520, Cox analysis also confirmed that the IGRS could serve as an independent prognostic factor for HCC. Moreover, a nomogram with good accuracy for predicting the survival of HCC was further formulated. Additionally, enrichment analysis showed that the high-IGRS group was enriched in cancer-related pathways and immune-related pathways. Additionally, patients with high IGRS exhibited an immunosuppressive phenotype. Therefore, patients with low IGRS scores may benefit from immunotherapy.ConclusionsIGFBP3 can act as a new diagnostic factor for HCC. IGRS signature represents a valuable predictive tool in the prognosis prediction and therapeutic decision making for Hepatocellular Carcinoma.
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Project description:BackgroundAmong the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments.Materials and methodsIn the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments.ResultsIn the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions.ConclusionBased on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.
| S-EPMC10986118 | biostudies-literature
Project description:IntroductionUveal melanoma (UM) is the most common primary intraocular malignant tumor in adults, and the main treatment for UM is currently surgery and plaque brachytherapy. UM is highly susceptible to metastasis, which eventually occurs in nearly half of all patients; once metastasis occurs, patients have a poor prognosis and the condition is difficult to treat. Therefore, the identification of new and effective UM biomarkers is vital for the application of therapeutic strategies. Immunogenic cell death (ICD) is a type of regulatory cell death that activates adaptive immune responses and generates long-term immunological memory. ICD can promote antitumor immunity, which may be a potential immunotherapeutic strategy for UM.MethodsThe data of UM from the Cancer Genome Atlas (TCGA) was used as a training set and the data from Gene Expression Omnibus (GEO) was used as a validation set. To determine the expression pattern of ICD-related genes in UM, survival analysis and difference analysis was conducted. The ICD-related risk signature was constructed by employing the least absolute shrinkage and selection operator (LASSO) Cox regression. Subsequently, immune profile and somatic mutation analysis were performed. In addition, cell experiments were performed to verify the role of immunogenic cell death-related genes in UM.ResultsIn this study, we analyzed the relationship between ICD-related gene expression and UM patient prognosis, somatic mutations, and the tumor immune microenvironment. Importantly, we constructed a 5-gene ICD-related risk signature and confirmed it as a novel prognostic biomarker in UM patients. We found that the high-risk group had more immune cell infiltration and a worse prognosis than the low-risk group. In cellular experiments, we confirmed the high expression of FOXP3 inMUM2B andOCM-1A cell lines and that knockdown of FOXP3 markedly inhibited the proliferation of UM tumor cells.DiscussionICD-related genes play a critical role in the tumor immune microenvironment. Our results may contribute to the development of effective immunotherapies.
| S-EPMC9709208 | biostudies-literature
Project description:ObjectivesUveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model.ResultsHigher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p < 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy.ConclusionWe developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.
| S-EPMC8450517 | biostudies-literature
Project description:BackgroundUveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated.MethodsTwo UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn.ResultsThe MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan-Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857-1.003)). Finally, a nomogram was established to serve in clinical practice.SignificanceUM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy.
| S-EPMC10800307 | biostudies-literature