scholarly journals An Immune Cell-Based Signature Associating With EMT Phenotype Predicts Postoperative Overall Survival of ESCC

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongliang Yu ◽  
Dayong Gu ◽  
Chao Yue ◽  
Jianhua Xu ◽  
Feng Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
M2 Macrophage ◽  
M2 Macrophages ◽  
Survival Times ◽  
Training Set ◽  
Mesenchymal Transition ◽  
Validation Set

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest solid malignancies and has a poor survival rate worldwide. In this study, we aimed to establish a tumor-infiltrating immune cell-based prognosis signature (IPS) to predict patients’ survival times and aid in the development of targeted therapies or immunotherapies. The abundances of 22 types of immune cells were determined by the CIBERSORT algorithm from ESCC patient gene expression data in the Gene Expression Omnibus (GEO) training set (n = 179) and The Cancer Genome Atlas (TCGA) validation set (n = 95). Then, the IPS was established by using the least absolute shrinkage and selection operator (LASSO) regression method. Kaplan-Meier analysis showed that patients with high IPS scores had significantly worse overall survival times than patients with low IPS scores in both the training set and the validation set (log-rank p = 0.001, and p = 0.050, respectively). Univariate and multivariate Cox regression analyses proved that the IPS was a robust prognostic factor for ESCC, independent of age, sex, tumor node metastasis (TNM) stage, pathology grade, and tumor location. In the mechanistic study, the epithelial-mesenchymal transition (EMT) process was identified by both gene set enrichment analysis (GSEA) and weighted correlation network analysis (WGCNA) as the underlying mechanism by which the IPS affects the prognosis of ESCC. After systematic correlation analyses, we found that M2 macrophages were the only cell type in the IPS significantly correlated with the EMT process. This relationship between M2 macrophage infiltration and the EMT phenotype was also confirmed by our preliminary immunochemistry (IHC) and multiplexed immunofluorescence study. In conclusion, we constructed an IPS that predicts the postoperative prognosis of ESCC patients and uncovered the critical role of M2 macrophages in the interplay between immune status and the EMT phenotype in ESCC.

Correlates of overall survival (OS) in metastatic uveal melanoma (mUM) and a randomized trial of cabozantinib (cabo) versus chemotherapy (chemo).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 9506-9506
Author(s):  
Daniel Olson ◽  
Riyue Bao ◽  
Jacob B Allred ◽  
Carrie Strand ◽  
Yuanyuan Zha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Epithelial Mesenchymal Transition ◽  
Gene Expression Signature ◽  
Correlated Data ◽  
Rank Test ◽  
M2 Macrophage ◽  
Primary Tumors ◽  
Mesenchymal Transition ◽  
Wilcoxon Rank Sum Test

9506 Background: Survival is poor in mUM and treatment options are limited. MET kinase is over-expressed on UM and the MET inhibitor cabo showed activity in an early trial. Methods: A091201 was a 2:1 randomized phase II study testing progression-free survival (PFS) of cabo (60 mg daily) vs chemo (DTIC/TMZ). We studied baseline metastatic tumor samples (n = 19; 1 lung, 18 liver) by whole exome sequencing (WES) and RNAseq. We correlated data with OS and made comparisons for mUM vs. primary tumors from TCGA (n = 80). Results: 46 patients were accrued with 96% and 63% with liver metastases and elevated LDH, respectively. Toxicities were similar to prior reports of cabo and chemo. The trial stopped at interim analysis due to no difference in PFS (p = 0.964; HR = 0.99) or OS (p = 0.580; HR = 1.21). WES showed tumor mutational burden of 46±4 (mean±SEM) and did not separate OS at 1 year (p = 0.14, two-sided Wilcoxon rank sum test) in A091201. Recurrent known mutations included GNAQ/11, SF3B1, BAP1; novel mutations included GOLGA6L10, PKD1L3, and FAM228B. Gene expression signatures differed significantly between A091201 and TCGA cohorts including MET signaling (p = 7.87e-22), T cell-inflamed (p = 0.004), homologous recombination deficiency (p = 0.004), proliferation (p = 0.009) and hypoxia (p = 5.2e-10) (two-sided Student’s t-test). Tumor immune cell enrichment analysis revealed significant differences with lower M1:M2 macrophage (p = 1.2e-10) and higher Tregs (p = 6.0E-21) in mUM relative to TCGA (two-sided Wilcoxon rank sum test). Epithelial-mesenchymal transition gene expression signature was significantly associated with worse OS in A091201 (p = 0.02) with angiogenesis signature trending toward significance (p = 0.21) (log-rank test). OS separated by differentially expressed genes with OS ≤ 1 year associating with increased expression of the angiogenesis/immune-associated molecule neuropilin 1. Conclusions: These results provide insights between primary and mUM indicating potential novel therapeutic approaches. Support: U10CA180821, U10CA180882, T32GM007019, Exelixis. https://acknowledgments.alliancefound.org ; Clinical trial information: NCT01835145.

Tumor-Infiltrating T Cells Are Not Predictive of Clinical Outcome in Follicular Lymphoma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 824-824 ◽  
Author(s):  
Wei Yun Z. Ai ◽  
Debra Czerwinski ◽  
Sandra J. Horning ◽  
John Allen ◽  
Robert Tibshirani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
Overall Survival ◽  
T Cells ◽  
Clinical Outcome ◽  
Follicular Lymphoma ◽  
Clinical Features ◽  
Training Set ◽  
Hla Dr ◽  
Validation Set

Abstract Background: Follicular lymphoma (FL) has variable clinical outcomes. It has been suspected that tumor-infiltrating immune cells affect the biology and outcome of this disease. Using gene expression profiling and immunoperoxide tissue staining techniques, T cells and macrophages have been related to the survival outcome in some studies, but not others. In the current study, we used flow cytometry to analyze T cells and their subsets in follicular lymphoma biopsy specimens and determined whether these cell populations correlated with clinical features and outcomes. Methods: Two hundred and eighty-nine follicular lymphoma patients (pt) presented from 1997 to 2003 underwent an excisional lymph node biopsy prior to any treatment. The median age of pt at diagnosis was 45.7 yrs, median follow-up was 8.6 yrs for living pts, and median survival was 15.7 yrs. All but 8 patients had stage III/IV disease, 5 had stage I/II, and 3 were unknown. The histological grades were: 162 (56%) grade 1, 112 (39%) grade 2, 13 (4.5%) grade 3 and 2 (0.5%) unknown. Among the 289 patients, 41(17%) had low FLIPI score, 150 (63%) intermediate, 48 (20%) high and 50 unknown. All biopsies were analyzed for CD20, CD3, CD4, CD8 and HLA-DR expression by single-parameter flow cytometry. The 289 pts were divided into a training set of 147 and a validation set of 142, stratified by age and era of diagnosis. We used these two factors to stratify the pts because age at diagnosis is the most important prognostic factor for survival, and, in our data set, the era of diagnosis had an impact on survival and on the time from diagnosis to first treatment. For our analysis, we began with the training set and used the percentages of each immune cell population as a continuous variable in a univariate analysis in relation to clinical features and outcomes. We chose 8 phenotypic variables: CD20, CD3, CD4, CD8, HLA-DR, CD4/CD3 ratio, CD8/CD3 ratio, and activated T cells [defined as (HLA-DR-CD20)/CD3]. Five parameters were used as clinical endpoints: overall survival, FLIPI score at diagnosis, the time from diagnosis to first treatment (defined as the time from the first treatment to second treatment), response to CVP as the first treatment and the duration of the benefit from the first treatment (defined as the time interval between initiation of first treatment and initiation of second treatment). Results: The number of pt evaluable for each of the outcome parameters was as follows: 289 for time to first treatment and for overall survival, 239 for FLIPI scores, 164 for response to CVP and 129 for duration of the benefit from the first treatment., Of the 8 variables tested in the training set, only CD4/CD3 ratio and CD8/CD3 ratio were marginally significant for the survival endpoint, with p 0.034 and 0.088, respectively. None of the variables was significant for any of the other endpoints. A multivariate analysis yielded CD4/CD3 as the only significant predictor for survival. When CD4/CD3 was tested in the validation set, it yielded a p value of 0.48. Conclusion: We find no evidence that the percentage of tumor-infiltrating T cells or their subsets is predictive of clinical outcome in follicular lymphoma. Any gene expression signature involving T cells that does relate to clinical outcome could therefore be a property of the activity of the cells rather than a simple reflection of their numbers.

scholarly journals High Expression of NREP Correlated With Worse Survival and M2 Macrophage Infiltrates in Gastric Cancer

2021 ◽  
Author(s):  
Tailiang Lu ◽  
Chenlong Li ◽  
Wei Peng ◽  
Cailing Xiang ◽  
Yongqiang Gong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
T Cell ◽  
Mrna Expression ◽  
Inflammatory Responses ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
M2 Macrophage ◽  
M2 Macrophages

Abstract Background: Neuronal Regeneration Related Protein (NREP) is a highly conserved protein and is a newly discovered protein that may be closely related to tumor cell migration. We aim at investigating the prognostic role of NREP in gastric cancer (GC). Methods: Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analysis NREP mRNA expression in GC. Correlations between NREP mRNA expression and clinicopathological characteristic were analyzed. TCGA and GEO data were analyzed by The Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan–Meier plotter databases respectively to assess prognostic value of NREP in GC. Gene set enrichment analysis (GSEA) was performed to identify the keg pathways related to NREP expression. TIMER and CIBERSORT analysis were used to found tumor-infiltrating immune cells associated with NREP mRNA expression. Results: NREP was over expressed in GC and significantly associated with T stage (P<0.001), Histologic grade (P = 0.022) and OS events (P = 0.007) of GC patients. High mRNA expression of NREP correlated with worse survival. The significantly kegg pathways enriched in samples with NREP high expression involved in cell adhesion, tumorigenesis, and immune and inflammatory responses. NREP mRNA expression was positively associated with CD4+T cell(r = 0.294, P = 1.04e−08), CD8+T cell(r = 0.125, P = 0.0167), Neutrophil(r = 0.169, P = 0.00116), Dendritic(r = 0.314, P = 1.03e−09), and was strongly associated with Macrophage (r = 0.547, P = 5.44e−30). The CIBERSORT database revealed that NREP mRNA expression was correlated with the activated memory CD4+ T cells(p<0.01), Monocytes(p<0.001) and M2 Macrophages(p<0.001). NREP is strongly correlated with the markers genes of M2 macrophages and tumor-associated macrophages (TAMs). Conclusion: NREP might be served as a novel prognostic biomarker of GC and associated with M2 macrophage infiltrates.

scholarly journals A transcription factor signature predicts the survival of patients with adrenocortical carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12433
Author(s):  
Jianyu Zhao ◽  
Bo Liu ◽  
Xiaoping Li
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Training Set ◽  
Abdominal Masses ◽  
Validation Set

Background Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels and is associated with poor clinical outcomes. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for the prediction of survival of ACC patients. Methods The gene expression profile and clinical information for ACC patients were downloaded from The Cancer Genome Atlas (TCGA, training set) and Gene Expression Omnibus (GEO, validation set) datasets after obtained 1,639 human TFs from a previously published study. The univariate Cox regression analysis was applied to identify the survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature based on these survival-associated TFs candidates. Then, multivariate analysis was used to reveal the independent prognostic factors. Furthermore, Gene Set Enrichment Analysis (GSEA) was performed to analyze the significance of the TFs constituting the prognostic signature. Results LASSO Cox regression and multivariate Cox regression identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6. The risk score based on the TF signature could classify patients into low- and high-risk groups. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival (OS) compared to the low-risk patients. Receiver operating characteristic (ROC) curves showed that the prognostic signature predicted the OS of ACC patients with good sensitivity and specificity both in the training set (AUC > 0.9) and the validation set (AUC > 0.7). Furthermore, the TF-risk score was an independent prognostic factor. Conclusions Taken together, we identified a 13-TF prognostic marker to predict OS in ACC patients.

scholarly journals ANTXR1 Is a Prognostic Biomarker and Correlates With Stromal and Immune Cell Infiltration in Gastric Cancer

2020 ◽  
Vol 7 ◽  
Author(s):  
Xiaodong Huang ◽  
Jie Zhang ◽  
Yongbin Zheng
Keyword(s):  
Gastric Cancer ◽  
Clinical Outcomes ◽  
Immune Cell ◽  
Prognostic Biomarker ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
M2 Macrophage ◽  
Immune Cell Infiltration ◽  
Mesenchymal Transition ◽  
Response To Chemotherapy

Gastric cancer (GC) is a complex and heterogeneous disease, making it difficult to ascertain the optimal therapeutic approach for individual GC patients. Stromal and immune cell infiltration in GC has a strong correlation with clinical outcomes; however, the underlying mechanisms that drive immunosuppression remain vastly undiscovered. Recent studies validated that anthrax toxin receptor 1 (ANTXR1) is aberrantly expressed in several cancers and holds promise as a new therapeutic target for cancer. However, its immunological roles in GC are still unclear. Here, we show that we identify the distinct stromal and immune cell infiltration in GC between the high and low ANTXR1 expression group by analyzing genomic data. Clinically, ANTXR1 is highly expressed in GC and correlates with adverse clinicopathological characteristics. Additionally, high ANTXR1 expression is linked to markedly poor clinical outcomes and resistance to chemotherapy, whereas the low ANTXR1 expression group is correlated with better outcomes and response to chemotherapy in GC patients. We further revealed the differential landscape of somatic tumor mutation burden (TMB) between the two groups and observed that patients with high ANTXR1 expression suffered from a lower TMB, potentially leading to less sensitivity to checkpoint therapy. Molecularly, results displayed that ANTXR1 is an immunosuppressive element, which may perform its function via promoting the secretion of immunosuppressive factors that play a significant role in modulating tumor-associated fibroblast transformation, M2 macrophage polarization, and T cell exhaustion. Gene set enrichment analysis revealed that cancer-related pathways including epithelial-to-mesenchymal transition, focal adhesion, and transforming growth factor-β (TGF-β) signaling pathways were enriched in high ANTXR1 expression tumors. Our work suggests that ANTXR1 could not only serve as a valuable prognostic biomarker in GC but also be deemed as a potential immunotherapeutic target and useful biomarker of sensitivity to chemotherapy.

scholarly journals POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 684.1-684
Author(s):  
J. Q. Zhang ◽  
S. X. Zhang ◽  
R. Zhao ◽  
J. Qiao ◽  
M. T. Qiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Expression Profiles ◽  
Inflammatory Myopathy ◽  
Shanxi Province ◽  
Survival Prediction ◽  
Training Set ◽  
Redox Biology ◽  
Score Model ◽  
Validation Set

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

scholarly journals Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Colon Cancer Patients and Assist Immunotherapy

2021 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Guanyu Yan ◽  
Ningning Wang ◽  
Moye Chen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Microsatellite Instability ◽  
Cancer Patients ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Macrophage Infiltration ◽  
M2 Macrophage ◽  
High Morbidity ◽  
M2 Macrophages ◽  
Hub Genes

Abstract Background: Colon cancer is a kind of malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divide it into different subtypes in order to achieve individualized treatment. With the rise of immunotherapy, its value in the field of tumor has initially emerged. Based on the above background, from the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored it.Methods: Cibersort was used to analyze the level of immune cell infiltration in colon cancer patients in the TCGA database. WGCNA, Consensus Clustering analysis, Lasso analysis, and univariate KM analysis were used to screen and verify the hub genes associated with M2 macrophages. PCA was used to establish the M2 macrophage-related score—M2I Score. The correlation between M2I Score and somatic cell variation and microsatellite instability were analysed. Furthermore the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored. Results: M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were selected as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in COAD was constructed based on four hub genes. M2I Score was positively correlated with tumor mutation load (TMB). The M2I Score of MSI-H group was higher than that of MSI-L group and MSS group. Combine with the TCIA database, we concluded that patients with a high M2I Score were more sensitive to PD-1 inhibitors and PD-1 inhibitors combined with CTLA-4 inhibitors. The low rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.Conclusion: Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score and divided the patients into two subgroups: high M2I Score group and low M2I Score group. TMB, microsatellite instability and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

Abstract 545: Differential mRNA Expression is Influenced by Apolipoprotein A-I in Order to Promote Foam Cell Regression

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elisa C Maruko ◽  
Hao Xu ◽  
Sushma Kaul ◽  
Brian J Capaldo ◽  
Nathalie Pamir ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Immune Cell ◽  
Foam Cell ◽  
Differential Expression Analysis ◽  
Ldl Receptor ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Apolipoprotein A

Atherosclerosis is a disease of both lipids and inflammatory immune cells. More specifically, elevated plasma levels of low-density lipoproteins (LDL) leads to migration of circulating monocytes into the artery wall. Lipid loaded monocyte cells subsequently proliferate in the arterial walls becoming macrophage foam cells; a hallmark of atherosclerotic lesions. A proposed mechanism of the protective effects of high-density lipoprotein (HDL) is apolipoprotein A-I (apo A-I) acting as a mediator of cholesterol efflux and subsequent foam cell regression. To better understand the biological changes stimulated by apo A-I treatment, differential expression analysis of microarray data was performed on spleen cells from apo A-I treated mice. LDL receptor null (LDLr -/- ) and LDL receptor and apo A-I null (LDLr -/- , apoA-I -/- ) mice were fed a western diet consisting of 0.2% cholesterol and 42% of calories as fat for 12 weeks. After 6 weeks of diet, a subset of mice for each genotype was subcutaneously injected with 200 micrograms of apo A-I 3 times a week for the remaining 6 weeks. The control group mice were subcutaneously injected with 200 micrograms of saline or BSA. Spleen cell RNA was isolated, purified, and analyzed for differential expression analysis using Illumina BeadArray Microarray Technology Analysis. Individual gene expression analysis for LDLr -/- , apoA-I -/- apo A-I treated mice showed 281 significantly differentially expressed genes compared to BSA treated mice. LDLr -/- A-I treated mice had 1502. Of the significant genes, 189 intersected across both genotypes. LDLr -/- , apoA-I -/- A-I mice showed 73 up-regulated and 116 down-regulated genes. Similarly, LDLr -/- A-I mice had 71 up-regulated and 118 down-regulated. One-directional Gene Set Enrichment Analysis (GSEA) of LDLr -/- , apoA-I -/- A-I mice revealed 49 significant pathways while a total of 63 were found for LDLr -/- . Of these pathways, 21 were up-regulated and 13 were down-regulated in both genotypes. Eight of the top 10 most significant up-regulated pathways in both genotypes were immune cell related. Their functions involve receptor, adhesion, and chemokine signaling. Overall, preliminary analysis suggests A-I treatment induces similar gene expression changes across different genotypes.

Combining Proteomics and Gene Expression Profiling Identifies Proteins/Genes Associated with Short Overall Survival in Multiple Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 197-197
Author(s):  
Ricky D Edmondson ◽  
Shweta S. Chavan ◽  
Christoph Heuck ◽  
Bart Barlogie
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Multivariate Analyses ◽  
Rank Test ◽  
P Value ◽  
Newly Diagnosed ◽  
Training Set ◽  
Test Set ◽  
Log Rank Test ◽  
Test Sets

Abstract Abstract 197 We and others have used gene expression profiling to classify multiple myeloma into high and low risk groups; here, we report the first combined GEP and proteomics study of a large number of baseline samples (n=85) of highly enriched tumor cells from patients with newly diagnosed myeloma. Peptide expression levels from MS data on CD138-selected plasma cells from a discovery set of 85 patients with newly diagnosed myeloma were used to identify proteins that were linked to short survival (OS < 3 years vs OS ≥ 3 years). The proteomics dataset consisted of intensity values for 11,006 peptides (representing 2,155 proteins), where intensity is the quantitative measure of peptide abundance; Peptide intensities were normalized by Z score transformation and significance analysis of microarray (SAM) was applied resulting in the identification 24 peptides as differentially expressed between the two groups (OS < 3 years vs OS ≥ 3 years), with fold change ≥1.5 and FDR <5%. The 24 peptides mapped to 19 unique proteins, and all were present at higher levels in the group with shorter overall survival than in the group with longer overall survival. An independent SAM analysis with parameters identical to the proteomics analysis (fold change ≥1.5; FDR <5%) was performed with the Affymetrix U133Plus2 microarray chip based expression data. This analysis identified 151 probe sets that were differentially expressed between the two groups; 144 probe sets were present at higher levels and seven at lower levels in the group with shorter overall survival. Comparing the SAM analyses of proteomics and GEP data, we identified nine probe sets, corresponding to seven genes, with increased levels of both protein and mRNA in the short lived group. In order to validate these findings from the discovery experiment we used GEP data from a randomized subset of the TT3 patient population as a training set for determining the optimal cut-points for each of the nine probe sets. Thus, TT3 population was randomized into two sub-populations for the training set (two-thirds of the population; n=294) and test set (one-third of the population; n=147); the Total Therapy 2 (TT2) patient population was used as an additional test set (n=441). A running log rank test was performed on the training set for each of the nine probe sets to determine its optimal gene expression cut-point. The cut-points derived from the training set were then applied to TT3 and TT2 test sets to investigate survival differences for the groups separated by the optimal cutpoint for each probe. The overall survival of the groups was visualized using the method of Kaplan and Meier, and a P-value was calculated (based on log-rank test) to determine whether there was a statistically significant difference in survival between the two groups (P ≤0.05). We performed univariate regression analysis using Cox proportional hazard model with the nine probe sets as variables on the TT3 test set. To identify which of the genes corresponding to these nine probes had an independent prognostic value, we performed a multivariate stepwise Cox regression analysis. wherein CACYBP, FABP5, and IQGAP2 retained significance after competing with the remaining probe sets in the analysis. CACYBP had the highest hazard ratio (HR 2.70, P-value 0.01). We then performed the univariate and multivariate analyses on the TT2 test set where CACYBP, CORO1A, ENO1, and STMN1 were selected by the multivariate analysis, and CACYBP had the highest hazard ratio (HR 1.93, P-value 0.004). CACYBP was the only gene selected by multivariate analyses of both test sets. Disclosures: No relevant conflicts of interest to declare.

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