The Prognostic Value and Potential Immune Mechanisms of lncRNAs Related to Immunogenic Cell Death in Papillary Thyroid Carcinoma
Ontology highlight
ABSTRACT:
SUBMITTER: Wang Y
PROVIDER: S-EPMC10986630 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundBoth immunogenic cell death (ICD) and long noncoding RNAs (lncRNAs) are strongly associated with tumor development, but the mechanism of action of ICD-associated lncRNAs in hepatocellular carcinoma (HCC) remains unclear.MethodsWe collected data from 365 HCC patients from The Cancer Genome Atlas (TCGA) database. We formulated a prognostic signature of ICD-associated lncRNAs and a nomogram to predict prognosis. To explore the potential mechanisms and provide clinical guidance, survival analysis, enrichment analysis, tumor microenvironment analysis, tumor mutation burden (TMB), and drug sensitivity prediction were conducted based on the subgroups obtained from the risk score.ResultsA prognostic signature of seven ICD-associated lncRNAs was constructed. Kaplan-Meier (K-M) survival curves showed a more unfavorable outcome in high-risk patients. The nomogram had a higher predictive value than the nomogram constructed without the risk model. Enrichment analysis confirmed that risk lncRNAs were closely associated with cell proliferation and mitosis. Most of the immune checkpoints currently used in therapy (e.g., PDCD1 and CTLA4) appeared to be elevated in high-risk patients. Tumor microenvironment analysis showed differential expression of lymphocytes (including natural killer cells, regulatory T cells, etc.) in the high-risk group. TMB had a higher incidence of mutations in the high-risk group (P=0.004). Chemotherapy drug sensitivity prediction provides effective guidelines for individual therapy. RT-qPCR of human HCC tissues verified the accuracy of the model.ConclusionWe constructed an effective prognostic signature for patients with HCC using seven ICD-lncRNAs, which provides guidance for the prognostic assessment and personalized treatment of patients.
| S-EPMC10500227 | biostudies-literature
Project description:Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.
| S-EPMC10628222 | biostudies-literature
Project description:IntroductionThyroid cancer is a very common malignant tumor in the endocrine system, while the incidence of papillary thyroid carcinoma (PTC) throughout the world also shows a trend of increase year by year. In this study, we constructed two models: ICIscore and Riskscore. Combined with these two models, we can make more accurate and reasonable inferences about the prognosis of PTC patients.MethodsWe selected 481 PTC samples from TCGA and 147 PTC samples from GEO (49 samples in GSE33630, 65 samples in GSE35570 and 33 samples in GSE60542). We performed consistent clustering for them and divided them into three subgroups and screened differentially expressed genes from these three subgroups. Then we divided the differential genes into three subtypes. We also distinguished the up-regulated and down-regulated genes and calculated ICIscore for each PTC sample. ICIscore consists of two parts: (1) the PCAu was calculated from up-regulated genes. (2) the PCAd was calculated from down-regulated genes. The PCAu and PCAd of each sample were the first principal component of the relevant gene. What's more, we divided the patients into two groups and constructed mRNA prognostic signatures. Additionally we also verified the independent prognostic value of the signature.ResultsThough ICIscore, we were able to observe the relationship between immune infiltration and prognosis. The result suggests that the activation of the immune system may have both positive and negative consequences. Though Riskscore, we could make more accurate predictions about the prognosis of patients with PTC. Meanwhile, we also generated and validated the ICIscore group and Riskscore group respectively.ConclusionAll the research results show that by combining the two models constructed, ICIscore and Riskscore, we can make a more accurate and reasonable inference about the prognosis of patients with clinical PTC patients. This suggests that we can provide more effective and reasonable treatment plan for clinical PTC patients.
| S-EPMC8142931 | biostudies-literature
Project description:Background Accumulating evidence shows that immunogenic cell death (ICD) enhances immunotherapy effectiveness. In this study, we aimed to develop a prognostic model combining ICD, immunity, and long non-coding RNA biomarkers for predicting hepatocellular carcinoma (HCC) outcomes. Methods Immune- and immunogenic cell death-related lncRNAs (IICDLs) were identified from The Cancer Genome Atlas and Ensembl databases. IICDLs were extracted based on the results of differential expression and univariate Cox analyses and used to generate molecular subtypes using ConsensusClusterPlus. We created a prognostic signature based on IICDLs and a nomogram based on risk scores. Clinical characteristics, immune landscapes, immune checkpoint blocking (ICB) responses, stemness, and chemotherapy responses were also analyzed for different molecular subtypes and risk groups. Result A total of 81 IICDLs were identified, 20 of which were significantly associated with overall survival (OS) in patients with HCC. Cluster analysis divided patients with HCC into two distinct molecular subtypes (C1 and C2), with patients in C1 having a shorter survival time than those in C2. Four IICDLs (TMEM220-AS1, LINC02362, LINC01554, and LINC02499) were selected to develop a prognostic model that was an independent prognostic factor of HCC outcomes. C1 and the high-risk group had worse OS (hazard ratio > 1.5, p < 0.01), higher T stage (p < 0.05), higher clinical stage (p < 0.05), higher pathological grade (p < 0.05), low immune cell infiltration (CD4+ T cells, B cells, macrophages, neutrophils, and myeloid dendritic cells), low immune checkpoint gene expression, poor response to ICB therapy, and high stemness. Different molecular subtypes and risk groups showed significantly different responses to several chemotherapy drugs, such as doxorubicin (p < 0.001), 5-fluorouracil (p < 0.001), gemcitabine (p < 0.001), and sorafenib (p < 0.01). Conclusion Our study identified molecular subtypes and a prognostic signature based on IICDLs that could help predict the clinical prognosis and treatment response in patients with HCC.
| S-EPMC9720162 | biostudies-literature
Project description:BackgroundThe primary goal of papillary thyroid cancer (PTC) management was to stratify patients at pre- and post-surgical level to identify the small proportion of cases with potentially aggressive disease.PurposeThe aim of our study is to evaluate the possible role of programmed cell death 4 (PDCD4) and BRAF status as prognostic markers in PTC.Patients and methodsWe investigate programmed cell death 4 (PDCD4) immunohistochemical expression in 125 consecutive PTCs with median follow-up of 75.3 months (range, 15-98 months) to verify the possible correlation between BRAF status and correlate the classical clinicopathological prognostic factors and PTC outcome with PDCD4 expression. To further support the data, miR-21 expression was tested (by quantitative real-time PCR and in situ hybridization) in a different series of 30 cases (15 PTCs BRAFwt and 15 PTCs BRAFV600E). Moreover, we validated our results using TGCA thyroid carcinoma dataset.ResultsWe found that 59.8% of the patients showed low-grade PDCD4 nuclear expression and low-grade expression correlated with BRAF V600E. Compared with BRAF 15 wild-type tissue samples, a significant miR-21 up-regulation was associated with BRAF V600E mutations. Low-grade PDCD4 resulted, and was associated with aggressive histological variants, higher cancer size, extra-thyroidal extension, multifocality, lymph-node metastasis and lymph nodal ratio at the diagnosis. Concerning the outcome, the low-grade PDCD4 expression correlated at univariate and multivariate analysis, with lower levels of recurrence-free survival rate (RFS) and with poor outcome. Moreover, there was significant association between BRAF V600E patients with PDCD4 nuclear loss and lower RFS, whilet here was significant association between BRAF wild-type patients with PDCD4 nuclear expression and better outcome.ConclusionThese results showed that PDCD4 could predict PTC outcome and that the sum of PDCD4 and BRAF alterations increases the prognostic power of BRAF mutation alone.
| S-EPMC6708393 | biostudies-literature
Project description:BackgroundAHNAK2 has been recently reported as a biomarker in many cancers. However, a systematic investigation of AHNAK2 in papillary thyroid carcinoma (PTC) has not been conducted.ResultsAHNAK2 is overexpressed in PTC tissues and could be an independent prognostic factor. AHNAK2 expression was significantly high in patients with advanced stage, advanced T classification, lymph node metastasis, increased BRAF mutations and decreased RAS mutations. Cell adhesion-, cell junction-, and immune-related pathways were the most frequently noted in gene set enrichment analysis. AHNAK2 expression in PTC was positively correlated with immune infiltration and negatively correlated with AHNAK2 methylation. AHNAK2 expression was significantly positively correlated with tumor progression and poor overall survival (OS) in pan-cancer patients.ConclusionsAHNAK2 is a good biomarker for the diagnosis and prognosis of PTC. AHNAK2 may promote thyroid cancer progression through cell adhesion-, cell junction-, and immune-related pathways. Methylation may act as an upstream regulator to inhibit the expression and biological function of AHNAK2. Additionally, AHNAK2 has broad prognostic value in pan-cancer.MethodsBased on The Cancer Genome Atlas (TCGA) data, we screened AHNAK2-related genes through weighted gene coexpression network analysis and explored the clinical value and the potential mechanism of AHNAK2 in PTC by multiomics analysis.
| S-EPMC7585101 | biostudies-literature
Project description:The incidence of papillary thyroid carcinoma (PTC) has increased significantly in recent years, and for patients with metastatic and recurrent PTC, the options for treatment currently available are insufficient. To date, the exact molecular mechanism underlying PTC is still not fully understood. 5-Methylcytosine (m5C) RNA methylation is associated with the prognosis of a variety of tumors. However, the molecular mechanisms and biomarkers associated with m5C in the diagnosis, treatment, and prognosis of this disease have not been fully elucidated. Ten m5C regulators with significantly different expression levels were included in this study. Immune infiltration analysis revealed significant negative correlations between most of these regulators and regulatory T cells. TRDMT1, NSUN5, and NSUN6 had high weights and strong correlations in the protein-protein interaction network. Using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, 1489 differentially expressed genes were screened from The Cancer Genome Atlas messenger RNA matrix, indicating that these differentially expressed genes were significantly enriched in various pathways and functions related to cancers. Four m5C regulators, NSUN2, NSUN4, NSUN6, and DNMT3B, were screened as prognostic markers by least absolute shrinkage and selection operator regression analysis, and NSUN2 and NSUN6 were identified as risk factors for poor prognosis. We found that the prognostic prediction model constructed using the m5C regulators NSUN2, NSUN4, NSUN6, and DNMT3B showed good prognostic prediction ability and diagnostic ability. This model was applied to predict the survival probability of patients with PTC, the prediction ability of 5-year survival was the best. The multi-factor prognostic prediction model combined with the tumor node metastasis stage and risk score grouping showed better prognostic predictive power.
| S-EPMC11191899 | biostudies-literature
Project description:Background: Recent research showed that abnormal lipid metabolism was associated with cancers. As one of the genes that can regulate the level of lipid metabolism, abnormal APOE expression was associated with carcinogenesis. However, the clinical value of APOE in papillary thyroid carcinoma (PTC) remains to be determined. Methods: ONCOMINE, GEPIA, UALCAN, STRING, GeneMANIA, LinkedOmics, GSCALite, TISIDB, EPIC and TIMER were utilized to achieve comprehensively bioinformatics analysis of APOE in this study. And the immunohistochemical staining of APOE was used to verify the predicted results. Results: The mRNA level and protein level of APOE of PTC tissues were significantly elevated in TCGA cohort and Shanghai cohort. PTC patients with low mRNA level of APOE were associated with a bad prognosis. The functions of APOE co-expressed genes were mainly enriched in adaptive immune response, protein-lipid complex subunit organization, actin cytoskeleton reorganization, cell chemotaxis, protein activation cascade and transcriptional misregulation in cancer. APOE level was significantly correlated with tumor-infiltrating cells (B cells, CD8+ T cells, neutrophils, and dendritic) and immune biomarkers in PTC. Conclusions: APOE is a potential independent biomarker for PTC and APOE expression is positively correlated with immune cell infiltration in PTC.
| S-EPMC8965124 | biostudies-literature
Project description:The Krüppel-like factor 5 (KLF5), a zinc-finger transcriptional factor, is highly expressed in several solid tumors, but its role in PTC remains unclear. We investigated the expression of KLF5 protein in a large cohort of PTC patient samples and explored its functional role and mechanism in PTC cell lines in vitro and in vivo. KLF5 overexpression was observed in 65.1% of all PTC cases and it was significantly associated with aggressive clinico-pathological parameters and poor outcome. Given the significant association between KLF5 and HIF-1α overexpression in PTC patients, we investigated the functional correlation between KLF5 and HIF-1α in PTC cells. Indeed, the analysis revealed the co-immunoprecipitation of KLF5 with HIF-1α in PTC cells. We also identified KLF5-binding sites in the HIF-1α promoter that specifically bound to KLF5 protein. Mechanistically, KLF5 promoted PTC cell growth, invasion, migration, and angiogenesis, while KLF5 downregulation via specific inhibitor or siRNA reverses its action in vitro. Importantly, the silencing of KLF5 decreases the self-renewal ability of spheroids generated from PTC cells. In addition, the depletion of KLF5 reduces PTC xenograft growth in vivo. These findings suggest KLF5 can be a possible new molecular therapeutic target for a subset of PTC.
| S-EPMC7825749 | biostudies-literature
Project description:5-methylcytosine (m5C) modification is involved in tumor progression. However, the lncRNAs associated with m5C in lung squamous cell carcinoma (LUSC) have not been elucidated. The Cancer Genome Atlas database was used to get the open-accessed transcriptional profiling and clinical information of LUSC patients. All the statistical analyses were performed based on R software v 4.0.0 and SPSS13.0. First, there were 614 m5C-related lncRNAs identified under the criterion of |R|>0.4 and p < 0.001 with m5C genes. Next, a prognosis model based on ERICD, AL021068.1, LINC01341, AC254562.3, and AP002360.1 was established, which showed good prediction efficiency in both the training and validation cohorts. Next, a nomogram plot was established by combining the risk score and clinical features for a better application in clinical settings. Pathway enrichment analysis showed that the pathways of angiogenesis, TGF-β signaling, IL6-JAK-STAT3 signaling, protein secretion, androgen response, interferon-α response, and unfolded protein response were significantly enriched in the high-risk patients. Immune infiltration analysis showed that the risk score was positively correlated with neutrophils, resting CD4+ memory T cells, and M2 macrophages, yet negatively correlated with follicular helper T cells, CD8+ T cells, and activated NK cells. Moreover, we found that high-risk patients might be more sensitive to immunotherapy, imatinib, yet resistant to erlotinib, gefitinib, and vinorelbine. In summary, our prognosis model is an effective tool that could robustly predict LUSC patient prognosis, which had the potential for clinical guidance.
| S-EPMC9355133 | biostudies-literature