Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer.
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ABSTRACT:
SUBMITTER: Zhang X
PROVIDER: S-EPMC9258710 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Hypoxia is a common feature of tumor microenvironment (TME). This study aims to establish the genetic features related to hypoxia in Bladder urothelial carcinoma (BLCA) and investigate the potential correlation with hypoxia in the TME and immune cells. We established a BLCA outcome model using the hypoxia-related genes from The Cancer Genome Atlas using regression analysis and verified the model using the Gene Expression Omnibus GSE32894 cohort. We measured the effect of each gene in the hypoxia-related risk model using the Human Protein Atlas website. The predictive abilities were compared using the area under the receiver operating characteristic curves. Gene Set Enrichment Analysis was utilized for indicating enrichment pathways. We analyzed immune cell infiltration between risk groups using the CIBERSORT method. The indicators related to immune status between the two groups were also analyzed. The findings indicated that the high-risk group had better outcomes than the low-risk group in the training and validation sets. Each gene in the model affected the survival of BLCA patients. Our hypoxia-related risk model had better performance compared to other hypoxia-related markers (HIF-1α and GLUT-1). The high-risk group was enriched in immune-related pathways. The expression of chemokines and immune cell markers differed significantly between risk groups. Immune checkpoints were more highly expressed in the high-risk group. These findings suggest that the hypoxia-related risk model predicts patients' outcomes and immune status in BLCA risk groups. Our findings may contribute to the treatment of BLCA.
| S-EPMC8569353 | biostudies-literature
Project description:ObjectivesThe abnormal expression of immune-related genes (IRGs) plays an important role in the occurrence and progression of ovarian cancer (OC), which is the main cause of mortality among gynecological cancer patients. This study aims to establish a prognostic risk model and comprehensively analyze the relationship between OC risk score and prognosis, immune cell infiltration (ICI) and therapeutic sensitivity in OC.MethodsWe retrospectively evaluated the clinicopathological characteristics of consecutive OC patients in the Cancer Genome Atlas (TCGA) database. First, the prognostic risk model was constructed by bioinformatics methods. And then, we systematically assessed model robustness, and correlations between risk score and prognosis, and immune cell infiltration. The ICGC cohort was used to verify the prognostic risk model. Finally, we evaluated their value in the treatment of OC immunotherapy and chemotherapy.ResultsA total of 10 IRGs were identified to construct the prognostic risk model. Survival analysis revealed that patients in the low-risk group had a better prognosis (P < .01), and the risk score might be considered an independent predictor for predicting the prognosis. In addition, risk scores and patient clinical information were used to construct clinical nomograms, improving the prediction's precision. We also explored the relationship between the risk score and ICI, immunotherapy and drug sensitivity.ConclusionsCollectively, we identified a novel ten IRGs signature that may be applied as a prognostic predictor of OC, thereby benefiting clinical decision-making and personalized treatment of patients.
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Project description:Background: The pathogenesis and diagnosis of Ankylosing Spondylitis (AS) has remained uncertain due to several reasons, including the lack of studies on the local and systemic immune response in AS. To construct a clinical diagnostic model, this study identified the micro RNA-messenger RNA (miRNA-mRNA) interaction network and immune cell infiltration-related hub genes associated with AS. Materials and Methods: Total RNA was extracted and purified from the interspinous ligament tissue samples of three patients with AS and three patients without AS; miRNA and mRNA microarrays were constructed using the extracted RNA. Bioinformatic tools were used to construct an miRNA-mRNA network, identify hub genes, and analyze immune infiltration associated with AS. Next, we collected the blood samples and clinical characteristics of 359 patients (197 with AS and 162 without AS). On the basis of the clinical characteristics and results of the routine blood tests, we selected immune-related cells whose numbers were significantly different in patients with AS and patients without AS. Univariate and multivariate logistic regression analysis was performed to construct a nomogram. Immunohistochemistry staining analysis was utilized to verify the differentially expression of LYN in AS and controls. Results: A total of 225 differentially expressed miRNAs (DE miRNAs) and 406 differentially expressed mRNAs (DE mRNAs) were identified from the microarray. We selected 15 DE miRNAs and 38 DE mRNAs to construct a miRNA-mRNA network. The expression of LYN, an immune-related gene, correlated with the counts of monocytes, neutrophils, and dendritic cells. Based on the independent predictive factors of sex, age, and counts of monocytes, neutrophils, and white blood cells, a nomogram was established. Receiver operating characteristic (ROC) analysis was performed to evaluate the nomogram, with a C-index of 0.835 and AUC of 0.855. Conclusion: LYN, an immune-related hub gene, correlated with immune cell infiltration in patients with AS. In addition, the counts of monocytes and neutrophils were the independent diagnostic factors for AS. If verified in future studies, a diagnostic model based on these findings may be used to predict AS effectively.
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Project description:BackgroundIncreasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.MethodsThe ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.ResultsThree immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients' risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.ConclusionsOur study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.
| S-EPMC9307664 | biostudies-literature
Project description:Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.
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Project description:BackgroundColonic adenocarcinoma (COAD) is a common gastrointestinal tract tumor, and its occurrence and progression are typically associated with genomic instability, tumor-suppressor gene and oncogene mutations, and tumor mutational load. N6-methyladenosine (m6A) modification of RNAs and long non-coding RNA (lncRNA) expression are important in tumorigenesis and progression. However, the regulatory roles of m6A-associated lncRNAs in the tumor microenvironment, stratification of prognosis, and immunotherapy are unclear.MethodsWe screened 43 prognostic lncRNAs linked to m6A and performed consistent molecular typing of COAD using consensus clustering. The single-sample Gene Set Enrichment Analysis and ESTIMATE algorithms were used to assess the immune characteristics of different subgroups. Covariation between methylation-related prognostic lncRNAs was eliminated by least absolute shrinkage and selection operator Cox regression. A nomogram was created and evaluated by combining the methylation-related prognostic lncRNA model with other clinical factors. The relationship between the prognostic model grouping and microsatellite instability, immunophenotype score, and tumor mutation burden was validated using R scripts. Finally, we used a linkage map to filter sensitive medicines to suppress the expression of high-risk genes. Three m6A-associated lncRNA modes were identified in 446 COAD specimens with different clinical endpoints and biological statuses. Risk scores were constructed based on the m6A-associated lncRNA signature genes. Patients with lower risk scores showed superior immunotherapy responses and clinical benefits compared to those with higher risk scores. Lower risk scores were also correlated with higher immunophenotype scores, tumor mutation burden, and mutation rates in significantly mutated genes (e.g., FAT4 and MUC16). Piperidolate, quinostatin, and mecamylamin were screened for their abilities to suppress the expression of high-risk genes in the model.ConclusionsQuantitative assessment of m6A-associated lncRNAs in single tumors can enhance the understanding of tumor microenvironment profiles. The prognostic model constructed using m6A-associated lncRNAs may facilitate prognosis and immunotherapy stratification of patients with COAD; finally, three drugs with potential therapeutic value were screened based on the model.
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Project description:Due to the rarity and heterogeneity, it is challenging to explore and develop new therapeutic targets for patients with sarcoma. Recently, immune cell infiltration in the tumor microenvironment (TME) was widely studied, which provided a novel potential approach for cancer treatment. The competing endogenous RNA (ceRNA) regulatory network has been reported as a critical molecular mechanism of tumor development. However, the role of the ceRNA regulatory network in the TME of sarcoma remains unclear. In this study, gene expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) sarcoma datasets, and an immune infiltration-related ceRNA network was constructed, which comprised 14 lncRNAs, 13 miRNAs, and 23 mRNAs. Afterward, we constructed an immune infiltration-related risk score model based on the expression of IRF1, MFNG, hsa-miR-940, and hsa-miR-378a-5p, presenting a promising performance in predicting the prognosis of patients with sarcoma.
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Project description:PurposeWe focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients' prognosis and to establish a risk assessment model in colorectal cancer.SummaryThis article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients' survival was firmly established.
| S-EPMC7970128 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most important subtypes of lung cancer. Compared with male LUAD patients, female patients have a higher incidence, but better long-term survival rate, with unknown reasons. In this study, we aimed to explore the effect of sex differences on immune cell infiltration in lung tumor microenvironment (TME), and tried to clarify the reasons for the different clinical characteristics of male and female LUAD patients, by conducting a comparative analysis of the TME.MethodsUsing ESTIMATE algorithm, we calculated immune and stromal scores of tumor samples downloaded from TCGA database according to immune or stromal components in TME. GO and KEGG enrichment analysis were conducted to reveal biological processes of these intersecting genes of high- and low-score groups. Cox regression analysis and protein-protein interaction (PPI) network analysis were performed to screen immune-related prognostic genes in female (CCR2, LCP2, and PTPRC) and male (BTK and CCR2) patients. Kaplan-Meier survival analysis was used to evaluate prognostic value of these identified genes. Mann-Whitney test was used to compare various indicators of male patients and female patients. The main results were subsequently validated in 420 cases from GSE72094.Results304 and 368 intersecting genes were identified in female and male patients, respectively. The immune score ranged from -943.17 to 3229.35 among female patients and from -541.75 to 3441.78 among male patients. The stromal score ranged from -1790.23 to 2097.27 among female patients and from -1786.94 to 1722.70 among male patients. The immune and stromal scores of women were higher than those of men (p < 0.05). CCR2, LCP2 and PTPRC were identified as the most important immune-related prognostic genes in female LUAD patients. BTK and CCR2 were identified as the most important immune-related prognostic genes in male LUAD patients. Female patients had a higher proportion of memory B cells than that of male patients, while the percentage of T cells CD4 naïve and resting NK cells was lower in female patients (p < 0.05).ConclusionsThis study comprehensively compared the differences in tumor immune microenvironment between male and female LUAD patients, and identified prognosis-related genes for patients of different sexes.
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Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10-11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.
| S-EPMC8220217 | biostudies-literature