Establishment and experimental validation of an immune miRNA signature for assessing prognosis and immune landscape of patients with colorectal cancer.
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ABSTRACT:
SUBMITTER: Liu Z
PROVIDER: S-EPMC8278100 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MicroRNA (miRNA) expression profiling of colorectal cancer (CRC) are often inconsistent among different studies. To determine candidate miRNA biomarkers for CRC, we performed an integrative analysis of miRNA expression profiling compared CRC tissues and paired neighboring noncancerous colorectal tissues. Using robust rank aggregation method, we identified a miRNA set of 10 integrated-signature miRNAs. In addition, the qRT-PCR validation demonstrated that 9 miRNAs were consistent dysregulated with the integrative analysis in CRC tissues, 4 miRNAs (miR-21-5p, miR-183-5p, miR-17-5p and miR-20a-5p) were up-regulated expression, and 5 miRNAs (miR-145-5p, miR-195-5p, miR-139-5p, miR-378a-5p and miR-143-3p) were down-regulated expression (all p < 0.05). Consistent with the initial analysis, 7 miRNAs were found to be significantly dysregulated in CRC tissues in TCGA data base, 4 miRNAs (miR-21-5p, miR-183-5p, miR-17-5p and miR-20a-5p) were significantly up-regulated expression, and 3 miRNAs (miR-145-5p, miR-139-5p and miR-378a-5p) were significantly down-regulated expression in CRC tissues (all p < 0.001). Furthermore, miR-17-5p (p = 0.011) and miR-20a-5p (p = 0.003) were up-regulated expression in the III/IV tumor stage, miR-145-5p (p = 0.028) and miR-195-5p (p = 0.001) were significantly increased expression with microscopic vascular invasion in CRC tissues, miR-17-5p (p = 0.037) and miR-145-5p (p = 0.023) were significantly increased expression with lymphovascular invasion. Moreover, Cox regression analysis of CRC patients in TCGA data base showed miR-20a-5p was correlated with survival (hazard ratio: 1.875, 95%CI: 1.088-3.232, p = 0.024). Hence, the finding of current study provides a basic implication of these miRNAs for further clinical application in CRC.
| S-EPMC4741947 | biostudies-literature
Project description:BackgroundOsteosarcoma (OSA), the most common primary mesenchymal bone tumor, is a health threat to children and adolescents with a dismal prognosis. While cuproptosis and mitochondria dysfunction have been demonstrated to exert a crucial role in tumor progression and development, the mechanisms by which they are regulated in OSA still await clarification.MethodsTwo independent OSA cohorts containing transcriptome data and clinical information were collected from public databases. The heterogeneity of OSA were evaluated by single cell RNA (scRNA) analysis. To identify a newly molecular subtype, unsupervised consensus clustering was conducted. Cox relevant regression methods were utilized to establish a prognostic gene signature. Wet lab experiments were performed to confirm the effect of model gene in OSA cells.ResultsWe determined 30 distinct cell clusters and assessed OSA heterogeneity and stemness scRNA analysis. Then, univariate Cox analysis identified 24 candidate genes which were greatly associated with the prognosis of OSA. Based on these prognostic genes, we obtained two molecular subgroups. After conducting step Cox regression, three model genes were selected to construct a signature showing a favorable performance to forecast clinical outcome. Our proposed signature could also evaluate the response to chemotherapy and immunotherapy of OSA cases.ConclusionWe generated a novel risk model based on cuproptosis and mitochondria-related genes in OSA with powerful predictive ability in prognosis and immune landscape.
| S-EPMC10796547 | biostudies-literature
Project description:BackgroundNeuroblastoma (NBL) is the most common extracranial solid tumor in childhood, and patients with high-risk neuroblastoma had a relatively poor prognosis despite multimodal treatment. To improve immunotherapy efficacy in neuroblastoma, systematic profiling of the immune landscape in neuroblastoma is an urgent need.MethodsRNA-seq and according clinical information of neuroblastoma were downloaded from the TARGET database and GEO database (GSE62564). With an immune-related-gene set obtained from the ImmPort database, Immune-related Prognostic Gene Pairs for Neuroblastoma (IPGPN) for overall survival (OS) were established with the TARGET-NBL cohort and then verified with the GEO-NBL cohort. Immune cell infiltration analysis was subsequently performed. The integrated model was established with IPGPN and clinicopathological parameters. Immune cell infiltration was analyzed with the XCELL algorithm. Functional enrichment analysis was performed with clusterProfiler package in R.ResultsImmune-related Prognostic Gene Pairs for Neuroblastoma was successfully established with seven immune-related gene pairs (IGPs) involving 13 unique genes in the training cohort. In the training cohort, IPGPN successfully stratified neuroblastoma patients into a high and low immune-risk groups with different OS (HR=3.92, P = 2 × 10-8) and event-free survival (HR=3.66, P=2 × 10-8). ROC curve analysis confirmed its predictive power. Consistently, high IPGPN also predicted worse OS (HR=1.84, P = .002) and EFS in validation cohort (HR=1.38, P = .06) Moreover, higher activated dendritic cells, M1 macrophage, Th1 CD4+, and Th2 CD4+ T cell enrichment were evident in low immune-risk group. Further integrating IPGPN with age and stage demonstrated improved predictive performance than IPGPN alone.ConclusionHerein, we presented an immune landscape with IPGPN for prognosis prediction in neuroblastoma, which complements the present understanding of the immune signature in neuroblastoma.
| S-EPMC8477712 | biostudies-literature
Project description:BackgroundBreast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated.Materials and methodsThe expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real-time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes.ResultsAn 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR.ConclusionsIn summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.
| S-EPMC9798726 | biostudies-literature
Project description:Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response-related risk predictive signature for forecasting the prognosis of patients with LUAD. Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response-related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting. Results: We constructed a novel inflammatory response-related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells' sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression. Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.
| S-EPMC8964604 | biostudies-literature
Project description:BackgroundColorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data.MethodsGene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues.ResultsA gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival.ConclusionsA novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.
| S-EPMC9742469 | biostudies-literature
Project description:Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.
| S-EPMC7873765 | biostudies-literature
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:BackgroundLow-grade gliomas (LGGs), which are the second most common intracranial tumor, are diagnosed in seven out of one million people, tending to develop in younger people. Tumor stem cells and immune cells are important in the development of tumorigenesis. However, research on prognostic factors linked to the immune microenvironment and stem cells in LGG patients is limited. We critically need accurate related tools for assessing the risk of LGG patients.MethodsIn this study, we aimed to identify immune-related genes (IRGs) in LGG based on the mRNAsi score. We employed differentially expressed gene (DEG) methods and weighted correlation network analysis (WGCNA). The risk signature was then further established using a lasso Cox regression analysis and a multivariate Cox analysis. Next, we used immunohistochemical sections (HPA) and a survival analysis to identify the hub genes. A nomogram was built to assess the prognosis of patients based on their clinical information and risk scores and was validated using a DCA curve, among other methods.ResultsFour hub genes were obtained: C3AR1 (HR = 0.98, p < 0.001), MSR1 (HR = 1.02, p < 0.001), SLC11A1 (HR = 1.01, p < 0.01), and IL-10 (HR = 1.01, p < 0.001). For LGG patients, we created an immune-related prognostic signature (IPS) based on mRNAsi for estimating risk scores; different risk groups showed significantly different survival rates (p = 3.3 × 10-16). Then, via an evaluation of the IRG-related signature, we created a nomogram for predicting LGG survival probability.ConclusionThe outcome suggests that, when predicting the prognosis of LGG patients, our nomogram was more effective than the IPS. In this study, four immune-related predictive biomarkers for LGG were identified and proven to be IRGs. Therefore, the development of efficient immunotherapy techniques can be facilitated by the creation of the IPS.
| S-EPMC10296549 | biostudies-literature
Project description:Necroptosis is a new form of cell death. Since the discovery that long non-coding RNAs can affect the proliferation of lung adenocarcinoma, much has been learned about it, yet those of necroptosis-related long non-coding RNAs (NRlncRNAs) in lung adenocarcinoma (LUAD) remain enigmatic. This study aims to explore novel biomarkers and therapeutic targets for LUAD. The LUAD data was downloaded from The Cancer Genome Atlas, and necroptosis-related genes were retrieved from published literature. Co-expression analysis, univariate Cox analysis, least absolute shrinkage and selection operator regression analysis were used to identify necroptosis-related prognostic long non-coding RNAs. A comprehensive evaluation of tumor immunity for necrosis-related features was performed, and we identified a 9-NRlncRNA signature. Kaplan-Meier and Cox regression analyses confirmed that the signature was an independent predictor of LUAD outcome in the test and train sets (all P < 0.05). The areas of 1-, 2-, and 3-year overall survival under the time-dependent receiver operating characteristics (ROC) curve (AUC) were 0.754, 0.746, and 0.720, respectively. The GSEA results showed that 9 NRlncRNAs were associated with multiple malignancy-associated and immunoregulatory pathways. Based on this model, we found that the immune status and level of response to chemotherapy and targeted therapy were significantly different in the low-risk group compared with the high-risk group. qRT-PCR assay revealed that 9 NRlncRNAs were involved in the regulation of tumor cell proliferation and may affect the expression of programmed cell death 1 (PD1) and CD28 at human immune checkpoints. Our results indicated that the novel signature involving 9 NRlncRNAs (AL031600.2, LINC01281, AP001178.1, AL157823.2, LINC01290, MED4-AS1, AC026355.2, AL606489.1, FAM83A-AS1) can predict the prognosis of LUAD and are associated with the immune response. This will provide new insights into the pathogenesis and development of therapies for LUAD.
| S-EPMC10560937 | biostudies-literature