Density of CD8+ and CD68+ lymphocyte cell infiltration is associated with clinic outcome in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15612-e15612
Author(s):  
Haibei Xin ◽  
Huan Chen ◽  
Lihong Wu ◽  
Yanhui Chen ◽  
Hongli Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Immune Cell ◽  
Cox Regression ◽  
Association Studies ◽  
Tumor Infiltrating Lymphocytes ◽  
Immune Checkpoint Blockade ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Increasing Risk

e15612 Background: The achievements of immune checkpoint blockade strengthen the concept that tumor outgrowth and development are comprehensively regulated by immune system. The aim of the study was to explorer whether distinct infiltrated immune cell features differentially affect clinical outcome in hepatocellular carcinoma (HCC). Methods: We obtained respectable stage II HCC specimens, along with adjacent para-tumor tissues from 221 patients who underwent surgical resection at Eastern Hepatobiliary Surgery Hospital, (in Shanghai, China) from 2015 through April 2018. CD8+ and CD68+ tumor-infiltrating lymphocytes (TILs) in the cancer area (CA) and stroma area (SA), as well as PD-1/PD-L1 expression, were analyzed by multiple immunohistochemistry. Results: The density of CD8+ TILs were significantly associated with gender, tumor size, and cirrhosis; while the density of CD68+ TILs were associated with copies of HBV DNA and anti-viral treatment. Multivariate Cox regression analysis revealed that high densities of CD8+ TILs and low densities of CD68+/CD8+ ratios independently predicted better outcomes. Of note, a prognostic signature combining clinic features [portal vein tumor thrombus (PVTT) and tumor size (TS)] and CD8+ TILs densities discriminated HCC patients into four subtypes with increasing risk of mortality: PVTT-negative/TS-small/CD8-high (6.8% of cases), PVTT-negative/TS-small/CD8-low (45.7%), PVTT-negative/TS-large (34.4%) and PVTT-positive (13.1%). Further association studies suggested that the four subgroups correlated with gender, tumor envelope, microvascular invasion (MVI), and SA-PD1 expression. Similarly, a prognostic signature combining PVTT, TS and CD68+/CD8+ ratios discriminated HCC patients into four subtypes with increasing risk of recurrence. Conclusions: Combined immune features including CD8+ and CD68+ lymphocyte infiltration and clinic characteristics are useful prognostic biomarkers for HCC patients.

Clinical Significance of Prognostic Alternative Splicing Signature in Tumor Immune Environment and Immune Therapy of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Qianhui Xu ◽  
Hao Xu ◽  
Rongshan Deng ◽  
Nanjun Li ◽  
Ruiqi Mu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Immune Therapy ◽  
Immune Checkpoint Blockade ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

Abstract Background: Hepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. We aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC.Methods: To determine the prognosis-related AS events, univariate Cox regression analysis was performed, followed by the development of prognostic signatures. The prognosis predictive ability of risk signature was validated and a predictive nomogram was constructed. To uncover the context of TIME in HCC, ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed. And the correlation of AS events with immune checkpoint blockade (ICB)-related genes was analyzed.Results: A total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, we then constructed eight AS prognostic signature with robust prognostic predictive accuracy. Furthermore, a quantitative nomogram exhibited robust validity in prognostic prediction of HCC. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs) in HCC.Conclusion: Herein, the AS events may contribute novel and robust indicators for the prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And we revealed the pivotal player of AS events in context of TIME and immunotherapy, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Association of the ratio of CD8+ and CD163+ lymphocytes with clinical outcome in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15613-e15613
Author(s):  
Huan Chen ◽  
Haibei Xin ◽  
Lihong Wu ◽  
Yanhui Chen ◽  
Hongli Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Outcome ◽  
Cancer Progression ◽  
Immune Cell ◽  
Association Studies ◽  
Progression Free Survival ◽  
Tumor Infiltrating Lymphocytes ◽  
Disease Recurrence ◽  
Macrophage Density ◽  
Infiltrating Lymphocytes

e15613 Background: Within the tumor microenvironment (TME), infiltrating lymphocytes and myeloid cells including tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) are key players involved in liver cancer progression. The purpose of the study was to explorer whether distinct infiltrated immune cell features differentially affect clinical outcome in hepatocellular carcinoma (HCC). Methods: We obtained respectable stage II HCC specimens, along with adjacent para-tumor tissues from 221 patients who underwent surgical resection at Eastern Hepatobiliary Surgery Hospital, (in Shanghai, China) from 2015 through April 2018. CD8+, CD163+ and CD66B+ tumor-infiltrating lymphocytes (TILs) in the cancer area (CA) and stroma area (SA), as well paratumor stroma area, were analyzed by multiple immunohistochemistry. Results: Hierarchical clustering analysis of immune cell densities revealed that all HCC samples can be classified into three distinct groups. The three immune oncology types (IO-types) were characterized by a strong CD8 T cell density in CA and SA region (IO-1), an intermediate state of CD8 and CD163+ (IO-2), and a strong CD163+ macrophage density in IO-3. Remarkably increasing risks of mortality and recurrence, as well as elevated AST, ALP, GGT and AFP levels, were identified in IO-3 group, when compared with IO-1 group. We then identified that percentages of CA-CD8+ TILs in the tumor sample and SA-CD163+ macrophages in the para-tumor region showed opposite distribution pattern among the three IO types, suggesting a predictive role for CD8/TAM ratio in HCC cohort. Therefore, we next classified all HCC samples into two subgroups, according to the levels of tumor-CA-CD8/paratumor-SA-CD163 ratio. Expectedly, higher rate of CD8/CD163 represented significantly improved overall survival (OS) and progression free survival (PFS), verses lower rate of CD8/CD163. Further association studies suggested that the two subgroups correlated with HBV DNA, tumor size, and microvascular invasion (MVI). Of note, a prognostic signature combining portal vein tumor thrombus (PVTT) and CD8/CD163 ratio discriminated HCC patients into four subtypes with increasing risk of mortality and recurrence. Conclusions: The current results indicated that the CD8/CD163 is a novel, independent prognostic factor for a lower rate of disease recurrence and favorable OS in patients with resectable HCC.

scholarly journals An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Gang Xiao ◽  
Xuan Gao ◽  
Lifeng Li ◽  
Chao Liu ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Outcomes ◽  
Immune Cell ◽  
Cox Regression ◽  
Immune Escape ◽  
Gene Signature ◽  
Individual Therapy ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Background. IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated. Methods. A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms. Results. Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients. Conclusion. The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals Human Nuclear Receptors (NRs) Genes have Prognostic Significance in Hepatocellular Carcinoma Patients

2020 ◽  
Author(s):  
Guangtao Sun ◽  
Kejian Sun ◽  
Chao Shen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Nuclear Receptors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Prognostic Signature ◽  
Term Survival ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Human nuclear receptors (NRs) have been identified to closely related to various cancer. However, the prognostic significance of NRs on HCC patients has not been studied in detail.Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from TCGA database and analyzed the expression of 48 NRs. The consensus clustering analysis with the mRNA levels of 48 NRs was performed by the "ConsensusClusterPlus". The Univariate cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs which were selected. Then Multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors including risk score and TNM Stage was used to predict the long-term survival of HCC patients.Results: NRs could effectively separate HCC samples with different prognosis. The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and TNM Stage and could better predict the long-term survival for 3- and 5-year of HCC patients.Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

2020 ◽  
Author(s):  
Xinxin Xia ◽  
Hui Liu ◽  
Yuejun Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Biological Mechanisms ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

scholarly journals Integrated analysis of 1804 samples of six centers to construct and validate a robust immune-related prognostic signature associated with stromal cell abundance in tumor microenvironment for gastric cancer

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Junyu Huo ◽  
Ge Guan ◽  
Jinzhen Cai ◽  
Liqun Wu
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Stromal Cell ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Integrated Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Stromal cells in tumor microenvironment could promote immune escape through a variety of mechanisms, but there are lacking research in the field of gastric cancer (GC). Methods We identified differential expressed immune-related genes (DEIRGs) between the high- and low-stromal cell abundance GC samples in The Cancer Genome Atlas and GSE84437 datasets. A risk score was constructed basing on univariate cox regression analysis, LASSO regression analysis, and multivariate cox regression analysis in the training cohort (n=772). The median value of the risk score was used to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE26253, n=432; GSE62254, n=300; GSE15459, n=191; GSE26901, n=109) from the Gene Expression Omnibus (GEO) database. The immune cell infiltration was quantified by the CIBERSORT method. Results The risk score contained 6 genes (AKT3, APOD, FAM19A5, LTBP3, NOV, and NOX4) showed good performance in predicting 5-year overall survival (OS) rate and 5-year recurrence-free survival (RFS) rate of GC patients. The risk death and recurrence of GC patients growing with the increasing risk score. The patients were clustered into three subtypes according to the infiltration of 22 kinds of immune cells quantified by the CIBERSORT method. The proportion of cluster A with the worst prognosis in the high-risk group was significantly higher than that in the low-risk group; the risk score of cluster C subtype with the best prognosis was significantly lower than that of the other two subtypes. Conclusion This study established and validated a robust prognostic model for gastric cancer by integrated analysis 1804 samples of six centers, and its mechanism was explored in combination with immune cell infiltration characterization.

scholarly journals Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Zhihao Wang ◽  
Kidane Siele Embaye ◽  
Qing Yang ◽  
Lingzhi Qin ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Value ◽  
Cox Regression ◽  
Cancer Genome ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Tcga Dataset

Abstract Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

scholarly journals Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

2020 ◽  
Author(s):  
Ze-bing Song ◽  
Guo-pei Zhang ◽  
shaoqiang li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

Sign in / Sign up

Export Citation Format

Share Document