scholarly journals The core genome M5C plays an important role in methylation modification and immune infiltration of acute myelocytic leukemia samples

2021 ◽  
Author(s):  
Qiang Wen ◽  
ShouJun Wang ◽  
Lili Hong ◽  
Xianfu Sheng ◽  
Xiaofen Zhuang ◽  
...  
Keyword(s):  
Core Genome ◽  
Gene Signature ◽  
Low Risk ◽  
Acute Myelocytic Leukemia ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
The Core ◽  
Myelocytic Leukemia

Abstract Currently, the pathogenesis of acute myelocytic leukemia(AML) is still unclear. We found the core genome M5C plays a vital role in methylation modification and immune infiltration of AML. At the same time, we created a new M5C score model to define the high and low-risk groups of AML.Our research showed the expression levels of the three molecular subtypes of M5C (C1, C2 and C3);as well as different clinical features ,the results showed significant differences in age\RUNX1-RUNX1T1 fusion, but not in RUNX1 mutation group.We constructed a prognostic risk model based on m5C phenotype from 417 samples in the GSE37642 data set and found 5 differential genes using lasso regression method. And the prognostic KM curve of the 5-gene signature was obtained, from which it can be seen that: all the five genes could significantly reduce the high and low risk of GSE37642 training set samples (P < 0.05).Finally, the robustness of M5C related 5-gene signature for AML prediction was verified by internal and external data using single factor and multifactor COX regression analysis.5-gene signature has strong robustness and can play a stable prediction performance in external validation data sets (GSE12417, TCGA-LAML).

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

scholarly journals Establishment of a 8 Immune-Related LncRNA Signature for Predicting the Prognosis of Soft Tissue Sarcoma

2021 ◽  
Author(s):  
Yuhang Liu ◽  
Changjiang Liu ◽  
Aixi Yu
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Cox Regression ◽  
Hot Spot ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Models ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Abstract Background: Soft tissue sarcoma is relatively rare and highly heterogeneous, which brings great difficulties to treatment. Long non-coding RNA acts a vital role in the occurrence and progression of soft tissue sarcoma, especially in the tumor-related immune process, which has become a hot spot of current research. Therefore, we are committed to developing lncRNA markers related to immunity to promote the treatment and prognosis of patients with soft tissue sarcoma.Methods：Based on the TCGA-SARC and GTEx data set, we screened out 8 prognostic-related immune lncRNAs and constructed a nomogram, which was verified in the test set. Furthermore, immune infiltration analysis was carried out on patients of high and low risk.Results: Based on the results of Pearson's correlation coefficient, we obtained 859 immune-related lncRNAs. After difference analysis, we finally determined 54 different lncRNAs. Univariate and multivariate cox regression analysis finally determined 8 immune-related lncRNAs to construct prognostic models and nomograms to predict the prognosis of STS patients. The above results have been verified in external data sets, indicating that this model has good predictive ability. Gene Set Enrichment Analysis and ESTIMATE analysis showed obviously differences exist in the immune infiltration status and immune cell subtypes of high- and low-risk patients.Conclusion: We constructed an immune-related lncRNA pattern to predict the survival status of soft tissue sarcoma patients.

Identification of the Pyroptosis-related Signature for Predicting Prognosis in Cervical Cancer

2021 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Yuhua Zheng ◽  
Xiaochun Liu
Keyword(s):  
Cervical Cancer ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Experimental Studies ◽  
Risk Groups ◽  
Core Gene ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Abstract Background: Cervical cancer (CC) is one of the most common malignancies in gynecology. There is still a lack of specific biomarkers for the diagnosis and prognosis of CC. Pyroptosis is one of the methods of programmed cell death, and its various components are related to the occurrence, invasion, and metastasis of tumors. However, the role of pyroptosis in CC has not yet been elucidated.Methods: This study focuses on the development of a prognostic signature associated with pyroptosis for CC patients using integrated bioinformatics to elucidate the relationship between the signature and the tumor microenvironment and immune response.Results: We identified a prognostic signature based on eight pyroptosis-related genes (PRGs), with better prognostic survival in the low-risk group (P<0.05) and AUC values greater than 0.7. The results of the multi-factor Cox regression analysis indicated that the signature could be used as an independent prognostic factor, and both the DCA and the Nomogram suggested that the prognostic signature had good predictive power. Interestingly, this prognostic signature can also be applied to multiple tumors. In addition, the tumor microenvironment and immune infiltration status were significantly different between high and low-risk groups (P<0. 05). The core gene GZMB was screened and the CC-associated GZMB/ miR-378a/TRIM52-AS1 regulatory axis was constructed.Conclusion: The study successfully established the prognostic signature based on eight PRGs and reflected their tumor microenvironment and immune infiltration. The GZMB/ miR-378a/TRIM52-AS1 regulatory axis may play an important regulatory role in the development of CC, and further experimental studies are needed to validate these results subsequently.

scholarly journals Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

2021 ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

scholarly journals A Cholesterol Homeostasis-Related Gene Signature Predicts Prognosis of Endometrial Cancer and Correlates With Immune Infiltration

2021 ◽  
Vol 12 ◽  
Author(s):  
Yili Chen ◽  
Kaping Lee ◽  
Yanchun Liang ◽  
Shuhang Qin ◽  
Yuan Zhu ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Roc Curve ◽  
Cholesterol Metabolism ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Cholesterol Homeostasis ◽  
Related Gene ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Background: Endometrial cancer (EC) is one of the most common gynecological malignancies in women. Cholesterol metabolism has been confirmed to be closely related to tumor proliferation, invasion and metastasis. However, the correlation between cholesterol homeostasis-related genes and prognosis of EC remains unclear.Methods: EC patients from the Cancer Genome Atlas (TCGA) database were randomly divided into training cohort and test cohort. Transcriptome analysis, univariate survival analysis and LASSO Cox regression analysis were adopted to construct a cholesterol homeostasis-related gene signature from the training cohort. Subsequently, Kaplan-Meier (KM) plot, receiver operating characteristic (ROC) curve and principal component analysis (PCA) were utilized to verify the predictive performance of the gene signature in two cohorts. Additionally, enrichment analysis and immune infiltration analysis were performed on differentially expressed genes (DEGs) between two risk groups.Results: Seven cholesterol homeostasis-related genes were selected to establish a gene signature. KM plot, ROC curve and PCA in two cohorts demonstrated that the gene signature was an efficient independent prognostic indicator. The enrichment analysis and immune infiltration analysis indicated that the high-risk group generally had lower immune infiltrating cells and immune function.Conclusion: We constructed and validated a cholesterol homeostasis-related gene signature to predict the prognosis of EC, which correlated to immune infiltration and expected to help the diagnosis and precision treatment of EC.

scholarly journals Development of An Inflammatory Response-Related Gene Signature To Predict Prognosis and Immunotherapy Response For Patients With Glioma

2021 ◽  
Author(s):  
Zhen Zhao ◽  
Jianglin Zheng ◽  
Yi Zhang ◽  
Xiaobing Jiang ◽  
Chuansheng Nie ◽  
...  
Keyword(s):  
Inflammatory Response ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Normal Brain ◽  
Related Gene ◽  
Who Grade ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Inflammatory response plays a crucial role in the development and progression of gliomas. However, the prognostic value of inflammatory response-related genes has never been comprehensively investigated for glioma. In this study, we identified 39 differentially expressed genes (DEGs) between glioma and normal brain tissue samples, of which 31 inflammatory response-related genes are related to the prognosis of glioma., The 8 optimal inflammatory response-related genes were selected to construct prognostic inflammatory response-related gene signature (IRGS) through the least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis. The effectiveness of the IRGS was verified in the training (TCGA) and validation (CGGA-693 CGGA-325 and Rembrandt) cohorts. The Kaplan-Meier curve revealed a significant difference in the OS between the high- and low-risk groups. The receiver operating characteristic curve (ROC) shows the powerful predictive ability of IRGS. Meanwhile, a nomogram with better accuracy was established to predict overall survival (OS) based on the independent prognostic factors (IRGS, age, WHO grade, and 1p19q codeletion). In addition, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, higher expression of immune checkpoints, higher expression of TIDE and Exclusion, and lower expression of MSI Expe Sig. Thus, the patients in the low-risk group had significantly higher respond rate of immune checkpoint inhibitors (ICIs). A novel prognostic signature incorporated 8 inflammatory response-related genes was associated with the prognosis, immune landscape and the immunotherapy response in patients with gliomas. Thus, the signature can be suitable for future clinical application to predict the prognosis of patients with glioma.

scholarly journals Development and validation of an immune-related prognostic signature in cervical cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

Accurate Prediction and Validation of Response to Endocrine Therapy in Breast Cancer

2015 ◽  
Vol 33 (20) ◽  
pp. 2270-2278 ◽  
Author(s):  
Arran K. Turnbull ◽  
Laura M. Arthur ◽  
Lorna Renshaw ◽  
Alexey A. Larionov ◽  
Charlene Kay ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Response ◽  
Estrogen Receptor Alpha ◽  
Quantitative Polymerase Chain Reaction ◽  
Three Dimensional ◽  
Gene Signature ◽  
Molecular Response ◽  
Cancer Specific Survival ◽  
Validation Data ◽  
Data Set

Purpose Aromatase inhibitors (AIs) have an established role in the treatment of breast cancer. Response rates are only 50% to 70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Patients and Methods Pretreatment and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 postmenopausal women who had estrogen receptor–alpha positive breast cancer and were receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed with use of three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterized and a four-gene classifier of clinical response was established (accuracy of 96%) on the basis of the level of two genes before treatment (one gene [IL6ST] was associated with immune signaling, and the other [NGFRAP1] was associated with apoptosis) and the level of two proliferation genes (ASPM, MCM4) after 2 weeks of therapy. The four-gene signature was found to be 91% accurate in a blinded, completely independent validation data set of patients treated with anastrozole. Matched 2-week on-treatment biopsies were associated with improved predictive power as compared with pretreatment biopsies alone. This signature also significantly predicted recurrence-free survival (P = .029) and breast cancer –specific survival (P = .009). We demonstrate that the test can also be performed with use of quantitative polymerase chain reaction or immunohistochemistry. Conclusion A four-gene predictive model of clinical response to AIs by 2 weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and cell proliferation that is not reduced 2 weeks after initiation of treatment are functional characteristics of breast tumors that do not respond to AIs.

scholarly journals Expression signature based on TP53 target genes doesn't predict response to TP53-MDM2 inhibitor in wild type TP53 tumors

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Dmitriy Sonkin
Keyword(s):  
Cell Lines ◽  
Target Genes ◽  
Gene Signature ◽  
Wild Type ◽  
Validation Data ◽  
Data Set ◽  
Expression Signature ◽  
Selection Of Patients ◽  
Mdm2 Inhibitor ◽  
Selection Of

A number of TP53-MDM2 inhibitors are currently under investigation as therapeutic agents in a variety of clinical trials in patients with TP53 wild type tumors. Not all wild type TP53 tumors are sensitive to such inhibitors. In an attempt to improve selection of patients with TP53 wild type tumors, an mRNA expression signature based on 13 TP53 transcriptional target genes was recently developed (Jeay et al. 2015). Careful reanalysis of TP53 status in the study validation data set of cancer cell lines considered to be TP53 wild type detected TP53 inactivating alterations in 23% of cell lines. The subsequent reanalysis of the remaining TP53 wild type cell lines clearly demonstrated that unfortunately the 13-gene signature cannot predict response to TP53-MDM2 inhibitor in TP53 wild type tumors.

scholarly journals Evolutionary Dynamics Based on Comparative Genomics of Pathogenic Escherichia coli Lineages Harboring Polyketide Synthase (pks) Island

mBio ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Arya Suresh ◽  
Sabiha Shaik ◽  
Ramani Baddam ◽  
Amit Ranjan ◽  
Shamsul Qumar ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Maximum Likelihood ◽  
High Throughput ◽  
Core Genome ◽  
Polyketide Synthase ◽  
Clinical Implications ◽  
Data Set ◽  
Content Type ◽  
E Coli ◽  
The Core

ABSTRACT The genotoxin colibactin is a secondary metabolite produced by the polyketide synthase (pks) island harbored by extraintestinal pathogenic E. coli (ExPEC) and other members of the Enterobacteriaceae that has been increasingly reported to have critical implications in human health. The present study entails a high-throughput whole-genome comparison and phylogenetic analysis of such pathogenic E. coli isolates to gain insights into the patterns of distribution, horizontal transmission, and evolution of the island. For the current study, 23 pks-positive ExPEC genomes were newly sequenced, and their virulome and resistome profiles indicated a preponderance of virulence encoding genes and a reduced number of genes for antimicrobial resistance. In addition, 4,090 E. coli genomes from the public domain were also analyzed for large-scale screening for pks-positive genomes, out of which a total of 530 pks-positive genomes were studied to understand the subtype-based distribution pattern(s). The pks island showed a significant association with the B2 phylogroup (82.2%) and a high prevalence in sequence type 73 (ST73; n = 179) and ST95 (n = 110) and the O6:H1 (n = 110) serotype. Maximum-likelihood (ML) phylogeny of the core genome and intergenic regions (IGRs) of the ST95 model data set, which was selected because it had both pks-positive and pks-negative genomes, displayed clustering in relation to their carriage of the pks island. Prevalence patterns of genes encoding RM systems in the pks-positive and pks-negative genomes were also analyzed to determine their potential role in pks island acquisition and the maintenance capability of the genomes. Further, the maximum-likelihood phylogeny based on the core genome and pks island sequences from 247 genomes with an intact pks island demonstrated horizontal gene transfer of the island across sequence types and serotypes, with few exceptions. This study vitally contributes to understanding of the lineages and subtypes that have a higher propensity to harbor the pks island-encoded genotoxin with possible clinical implications. IMPORTANCE Extraintestinal pathologies caused by highly virulent strains of E. coli amount to clinical implications with high morbidity and mortality rates. Pathogenic E. coli strains are evolving with the horizontal acquisition of mobile genetic elements, including pathogenicity islands such as the pks island, which produces the genotoxin colibactin, resulting in severe clinical outcomes, including colorectal cancer progression. The current study encompasses high-throughput comparative genomics and phylogenetic analyses to address the questions pertaining to the acquisition and evolution pattern of the genomic island in different E. coli subtypes. It is crucial to gain insights into the distribution, transfer, and maintenance of pathogenic islands, as they harbor multiple virulence genes involved in pathogenesis and clinical implications of the infection.

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