scholarly journals Effect of MHC Linked 7-Gene Signature on Delayed Hepatocellular Carcinoma Recurrence

2021 ◽  
Vol 11 (11) ◽  
pp. 1129
Author(s):  
Fomaz Tariq ◽  
Walizeb Khan ◽  
Washaakh Ahmad ◽  
Syeda Kiran Riaz ◽  
Mahvish Khan ◽  
...  
Keyword(s):  
Cox Regression ◽  
Human Leukocyte ◽  
Gene Interaction ◽  
Gene Signature ◽  
Leukocyte Antigens ◽  
Kaplan Meier ◽  
Hepatocellular Carcinoma Recurrence ◽  
Patient Prognosis ◽  
Transcriptomic Level ◽  
Immune Related Genes

Dysregulated immune response significantly affects hepatocellular carcinoma’s (HCC) prognosis. Human Leukocyte Antigens are key in devising immune responses against HCC. Here, we investigated how HLAs modulate HCC development at the transcriptomic level. RNA-seq data of 576 patients from two independent cohorts was retrieved. The clinicopathological relevance of all HLA genes was investigated using Fisher-Exact, correlation, and Kaplan–Meier and cox regression survival tests. Clustering of ~800 immune-related genes against HLAs was completed using a ward-agglomerative method. Networks were generated using 40 HLA associated unique genes and hub genes were investigated. HLAs including HLA-DMA, HLA-DMB, HLA-DOA and HLA-DRB6 were associated with delayed recurrence in both discovery (204 HCC cases) and validation (372 HCC cases) cohorts. Clustering analyses revealed 40 genes associated with these four HLAs in both cohorts. A set of seven genes (NCF4, TYROBP, LCP2, ZAP70, PTPRC, FYN and WAS) was found co-expressed at gene–gene interaction level in both cohorts. Furthermore, survival analysis revealed seven HLA-linked genes as predictors of delayed recurrence. Multivariate analysis also predicted that mean expression of 7-gene is an independent predictor of delayed recurrence in both cohorts. We conclude that the expression of 7-gene signature may lead to improved patient prognosis. Further studies are required for consideration in clinical practice.

Down-regulation of miR-219-5p increase the risk of cancer-related mortality in patients with prostate cancer

2021 ◽  
pp. postgradmedj-2021-139981
Author(s):  
Shimin Tang ◽  
Hao Jiang ◽  
Zhijun Cao ◽  
Qiang Zhou
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Cancer Patients ◽  
Cox Regression ◽  
Cox Model ◽  
Risk Of Death ◽  
Kaplan Meier ◽  
Risk Of Progression ◽  
Patient Prognosis ◽  
Risk Of Cancer

IntroductionProstate cancer is a common malignancy in men that is difficult to treat and carries a high risk of death. miR-219-5p is expressed in reduced amounts in many malignancies. However, the prognostic value of miR-219-5p for patients with prostate cancer remains unclear.MethodsWe retrospectively analysed data from 213 prostate cancer patients from 10 June 2012 to 9 May 2015. Overall survival was assessed by Kaplan-Meier analysis and Cox regression models. Besides, a prediction model was constructed, and calibration curves evaluated the model’s accuracy.ResultsOf the 213 patients, a total of 72 (33.8%) died and the median survival time was 60.0 months. We found by multifactorial analysis that miR-219-5p deficiency increased the risk of death by nearly fourfold (HR: 3.86, 95% CI): 2.01 to 7.44, p<0.001) and the risk of progression by twofold (HR: 2.79, 95% CI: 1.68 to 4.64, p<0.001). To quantify each covariate’s weight on prognosis, we screened variables by cox model to construct a predictive model. The Nomogram showed excellent accuracy in estimating death’s risk, with a corrected C-index of 0.778.ConclusionsmiR-219-5p can be used as a biomarker to predict death risk in prostate cancer patients. The mortality risk prediction model constructed based on miR-219-5p has good consistency and validity in assessing patient prognosis.

scholarly journals Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

2021 ◽  
Author(s):  
Teng-di Fan ◽  
Di-kai Bei ◽  
Song-wei Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Bone Metastasis ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Training Set ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

scholarly journals Favorable 90-Day Mortality in Obese Caucasian Patients with Septic Shock According to the Sepsis-3 Definition

2019 ◽  
Vol 9 (1) ◽  
pp. 46 ◽  
Author(s):  
Caspar Mewes ◽  
Carolin Böhnke ◽  
Tessa Alexander ◽  
Benedikt Büttner ◽  
José Hinz ◽  
...  
Keyword(s):  
Septic Shock ◽  
Protective Effect ◽  
Cox Regression ◽  
Obese Patients ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Threatening Condition ◽  
Life Threatening ◽  
Caucasian Patients ◽  
Patient Prognosis

Septic shock is a frequent life-threatening condition and a leading cause of mortality in intensive care units (ICUs). Previous investigations have reported a potentially protective effect of obesity in septic shock patients. However, prior results have been inconsistent, focused on short-term in-hospital mortality and inadequately adjusted for confounders, and they have rarely applied the currently valid Sepsis-3 definition criteria for septic shock. This investigation examined the effect of obesity on 90-day mortality in patients with septic shock selected from a prospectively enrolled cohort of septic patients. A total of 352 patients who met the Sepsis-3 criteria for septic shock were enrolled in this study. Body-mass index (BMI) was used to divide the cohort into 24% obese (BMI ≥ 30 kg/m2) and 76% non-obese (BMI < 30 kg/m2) patients. Kaplan-Meier survival analysis revealed a significantly lower 90-day mortality (31% vs. 43%; p = 0.0436) in obese patients compared to non-obese patients. Additional analyses of baseline characteristics, disease severity, and microbiological findings outlined further statistically significant differences among the groups. Multivariate Cox regression analysis estimated a significant protective effect of obesity on 90-day mortality after adjustment for confounders. An understanding of the underlying physiologic mechanisms may improve therapeutic strategies and patient prognosis.

scholarly journals Up-Regulation of Long Noncoding RNA CCAL Predicts Poor Patient Prognosis and Promotes Tumor Metastasis in Osteosarcoma

2017 ◽  
Vol 32 (1) ◽  
pp. 108-112 ◽  
Author(s):  
Da-Kai Zhou ◽  
Xi-Wang Yang ◽  
Huining Li ◽  
Yongbo Yang ◽  
Zhen-Jun Zhu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Proliferation ◽  
Noncoding Rna ◽  
Tumor Metastasis ◽  
Cox Regression ◽  
Migration And Invasion ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Invasion Assays

Background Long noncoding RNAs (IncRNAs) play essential roles in tumor progression. Aberrant colorectal cancer-associated IncRNA (CCAL) has been found in colorectal cancer. However, the function of IncRNA CCAL in osteosarcoma (OS) remains unclear. Methods Quantitative real-time PCR (qRT-PCR) was performed to measure CCAL expression in OS tissues and adjacent nontumor tissues. The correlation betweent CCAL expression and clinicopathological features and prognosis was also analyzed. In addition, the function of CCAL was further evaluated by cell proliferation, migration and invasion assays. Results We showed that CCAL was significantly up-regulated in OS tissues compared with adjacent nontumor tissues. Increased expression of CCAL was correlated with advanced TNM stage and metastasis. Kaplan-Meier analysis demonstrated that patients with high CCAL expression had lower overall survival than those with low CCAL expression. Multivariate Cox regression analysis indicated that CCAL expression might be an independent prognostic factor for OS patients. In addition, functional assays showed that decreased CCAL expression could inhibit OS cell proliferation, migration and invasion ability. Conclusions Our findings suggested that CCAL plays critical roles in OS progression and could act as a therapeutic target in the treatment of OS.

scholarly journals LINC01268 and CTB-31O20.2 as Prognostic and Functional Protective Immune Related lncRNAs in Glioblastoma

2021 ◽  
Author(s):  
Dong-Hui Liu ◽  
Xiu Yang ◽  
Han Meng ◽  
Gui Yun Zhang ◽  
Shanghang Shen
Keyword(s):  
Cox Regression ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Cox Regression Analysis ◽  
Invasion And Migration ◽  
Kaplan Meier ◽  
Competitive Endogenous Rnas ◽  
The Central Nervous System ◽  
And Migration ◽  
Immune Related Genes

Abstract Glioblastoma (GBM) is the most common and deadly tumor in the central nervous system. Recent studies illuminated that long noncoding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) and play an important role in GBM by regulating immune responses. Here, GBM datasets from TCGA database were analyzed to obtain 356 significantly differentially expressed lncRNAs (DE-lncRNAs), 4951 DE-mRNAs, and 34 DE-miRNAs in GBM, respectively. For mRNAs, 369 DE-mRNAs were identified as immune-related genes in Immport database. For DE-lncRNAs, univariate analysis identified 39 DE-lncRNAs with prognostic significance, and 9 DE-lncRNAs are included in ImmLnc database. Then, combined analysis was conducted, by integrating 9 immune related DE-lncRNAs, 369 immune related DE-mRNAs and 34 DE-miRNAs, and generated a ceRNA network composed of 2 upregulated lncRNAs (LINC01268 and CTB-31O20.2), 3 downregulated miRNAs and 5 upregulated mRNAs. Then we focused on LINC01268 and CTB-31O20.2, and Kaplan-Meier survival, univariate and multivariate Cox regression analysis showed that LINC01268 and CTB-31O20.2 serve as independent protective prognostic markers in GBM. Finally, LINC01268 and CTB-31O20.2 overexpression was conducted in GBM cell U251. CCK8, transwell and scratch healing assay indicated that LINC01268 and CTB-31O20.2 inhibits GBM cell line U251 proliferation, invasion and migration. To sum up, LINC01268 and CTB-31O20.2 are independent prognostic immune related markers, and reduces cancer cell proliferation and metastasis in GBM.

scholarly journals Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Jincai Wu ◽  
Yuliang Zhang ◽  
Zhehao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
Clinical Samples ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Cancer Tissues

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes.Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples.Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages.Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

scholarly journals Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Jimin He ◽  
Chun Zeng ◽  
Yong Long
Keyword(s):  
Risk Stratification ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Genome Atlas

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

scholarly journals A novel tumor doubling time-related immune gene signature for prognosis prediction in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Genhao Zhang ◽  
Lisa Su ◽  
Xianping Lv ◽  
Qiankun Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Doubling Time ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Gene Signature ◽  
Tumor Doubling Time ◽  
Prognosis Prediction ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) has become a global health issue of wide concern due to its high prevalence and poor therapeutic efficacy. Both tumor doubling time (TDT) and immune status are closely related to the prognosis of HCC patients. However, the association between TDT-related genes (TDTRGs) and immune-related genes (IRGs) and the value of their combination in predicting the prognosis of HCC patients remains unclear. The current study aimed to discover reliable biomarkers for anticipating the future prognosis of HCC patients based on the relationship between TDTRGs and IRGs. Methods Tumor doubling time-related genes (TDTRGs) were acquired from GSE54236 by using Pearson correlation test and immune-related genes (IRGs) were available from ImmPort. Prognostic TDTRGs and IRGs in TCGA-LIHC dataset were determined to create a prognostic model by the LASSO-Cox regression and stepwise Cox regression analysis. International Cancer Genome Consortium (ICGC) and another cohort of individual clinical samples acted as external validations. Additionally, significant impacts of the signature on HCC immune microenvironment and reaction to immune checkpoint inhibitors were observed. Results Among the 68 overlapping genes identified as TDTRG and IRG, a total of 29 genes had significant prognostic relevance and were further selected by performing a LASSO-Cox regression model based on the minimum value of λ. Subsequently, a prognostic three-gene signature including HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1), C-type lectin domain family 1 member B (CLEC1B), and Collectin sub-family member 12 (COLEC12) was finally identified by stepwise Cox proportional modeling. The signature exhibited superior accuracy in forecasting the survival outcomes of HCC patients in TCGA, ICGC and the independent clinical cohorts. Patients in high-risk subgroup had significantly increased levels of immune checkpoint molecules including PD-L1, CD276, CTLA4, CXCR4, IL1A, PD-L2, TGFB1, OX40 and CD137, and are therefore more sensitive to immune checkpoint inhibitors (ICIs) treatment. Finally, we first found that overexpression of CLEC1B inhibited the proliferation and migration ability of HuH7 cells. Conclusions In summary, the prognostic signature based on TDTRGs and IRGs could effectively help clinicians classify HCC patients for prognosis prediction and individualized immunotherapies.

scholarly journals Enthesitis Related Arthritis in a Longitudinal Southeast Asian Registry: High Prevalence of HLA-B27, Different Sacroiliitis Risk Factors and Less Common Drug-Free Remission

2021 ◽  
Vol 10 (4) ◽  
pp. 568
Author(s):  
Thaschawee Arkachaisri ◽  
Kai Liang Teh ◽  
Yun Xin Book ◽  
Sook Fun Hoh ◽  
Xiaocong Gao ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Cox Regression ◽  
Age At Onset ◽  
Human Leukocyte ◽  
Common Disease ◽  
Hla B27 ◽  
Significance Level ◽  
Enthesitis Related Arthritis ◽  
Joint Deformity ◽  
Kaplan Meier

Objective. To describe the clinical characteristics, predictors and treatment of children with Enthesitis Related Arthritis (ERA) in a Singapore longitudinal cohort over 11 years. Methods. ERA patients were recruited from our registry (2009–2019). Nonparametric descriptive statistics including median (interquartile range, IQR) were used to describe data. Kaplan–Meier survival and logistic/Cox regression analyses were used to estimate the probabilities and determine predictors of clinical variables, respectively. The significance level was set at <0.05. Results. One hundred and forty-six ERA patients (87% male, 82% Chinese) were included. Median onset age was 11.9 years (IQR 9.4–14.0) and median disease duration was 4.9 years (IQR 2.6–8.3). Family history of Human Leukocyte Antigen (HLA)-B27 associated diseases was positive in 7.5%. Acute uveitis occurred in 3.4%. Oligoarthritis was present in 89.7%. Hip, knee and ankle joints were among the most common joints involved. One-fourth had enthesitis at diagnosis (Achilles tendon entheses, 82.9%). Sacroiliitis occurred in 61%. Probabilities of sacroiliitis development were 0.364, 0.448 and 0.578 at 1, 2 and 5 years after onset, respectively. Negative HLA-B27, female, older age at onset and hip arthritis at diagnosis were associated with shorter time for sacroiliitis development (p = 0.001–0.049). Methotrexate (MTX) remained the most common disease modifying anti-rheumatic drug (DMARD) used (77.4%). However, 77.9% required anti-TNF (aTNF) therapy secondary to MTX failure. Among MTX-treated sacroiliitis patients, 85.3% failed, requiring aTNF, as compared to 63.2%patients without axial disease. Longer duration to diagnosis (p = 0.038) and MTX use (p = 0.007) predicted aTNF therapy. None had joint deformity. Conclusions. This study underscores differences in ERA clinical characteristics, predictors and treatment responses. Our ERA population had many unique findings but good functional outcomes.

scholarly journals An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weifeng Zheng ◽  
Chaoying Chen ◽  
Jianghao Yu ◽  
Chengfeng Jin ◽  
Tiemei Han
Keyword(s):  
Energy Metabolism ◽  
Cox Regression ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Prognostic Gene ◽  
Kaplan Meier ◽  
Esophagus Carcinoma

Abstract Background The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes. Materials and methods The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature. Results A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures. Conclusions Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

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