Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE141479 | GEO | 2020/04/07
REPOSITORIES: GEO
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Project description:Pulmonary embolism (PE) is a life-threatening disease. Our aim was to search for potential biomarkers using modern high-throughput metabolomics methods to improve diagnostic efficacy. The discovery cohort included 60 participants, including 30 PE patients and 30 healthy individuals. The validation cohort had 40 participants, including 20 PE patients and 20 healthy individuals. Gas chromatography-mass spectrometry (GC-MS) was combined with multivariate data analysis to determine serum metabolic profiles in patients with PE and healthy controls. The distribution of metabolic profiles in the two cohorts was assessed by unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA). Sixteen metabolites were selected from the ranked variable of predictive importance (VIP) scores and applied to the correlation analysis of PE-related clinical indicators. Four metabolites that were correlated with D-dimer levels were selected, including L-tryptophan, N-α-acetyl-L-lysine, dopamine, and N2-acetylornithine. Finally, the AUC values were calculated to be 0.958 (95% CI: 0.9122-1) for the combined biomarker panel including the 4 specific metabolites in the discovery cohort and 0.963 (95% CI: 0.9122-1) in the validation cohort. The results suggest that these four specific metabolites can be used as diagnostic biomarkers to improve diagnostic efficacy in pulmonary embolism.
2024-10-24 | PXD057168 |
Project description:Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels (Affymetrix) and 1.3 million genome-wide SNP markers (Illumina) were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation. EBV-transformed LCLs from 96 African-American (AA), 96 Caucasian-American (CA), and 96 Han Chinese-American (HCA) unrelated healthy subjects (sample sets HD100AA, HD100CAU, HD100CHI) were purchased from the Coriell Cell Repository (Camden, NJ).
2010-09-24 | E-GEOD-24245 | biostudies-arrayexpress
Project description:Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels (Affymetrix) and 1.3 million genome-wide SNP markers (Illumina) were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation. EBV-transformed LCLs from 96 Caucasian-American (CA), and 96 Han Chinese-American (HCA) unrelated healthy subjects (sample sets HD100CAU, HD100CHI) were purchased from the Coriell Cell Repository (Camden, NJ).
2010-09-24 | E-GEOD-24260 | biostudies-arrayexpress
Project description:Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels (Affymetrix) and 1.3 million genome-wide SNP markers (Illumina) were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation. EBV-transformed LCLs from 95 African-American (AA) unrelated healthy subjects (sample sets HD100AA) were purchased from the Coriell Cell Repository (Camden, NJ).
2010-09-25 | E-GEOD-24274 | biostudies-arrayexpress
Project description:We used microarrays to identify the variation of basal gene expression level among 287 lymphoblastoid cell lines. Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels and 1.3 million genome-wide SNP markers from both Affymetrix and Illumina platforms were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation. EBV-transformed LCLs from 95 African-American (AA), 96 Caucasian-American (CA), and 96 Han Chinese-American (HCA) unrelated healthy subjects (sample sets HD100AA, HD100CAU, HD100CHI) were purchased from the Coriell Cell Repository (Camden, NJ).
2010-07-24 | E-GEOD-23120 | biostudies-arrayexpress
Project description:Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora
2008-12-31 | GSE14245 | GEO
Project description:Background Testosterone (T) is the main androgen playing a crucial role in regulating the function of both reproductive and non-reproductive organs. Current methods for assessing T levels in humans do not reflect the biological androgenic activity (BAA), which may be important for example in the diagnosis of hypogonadism and clarifying pathogenetic mechanisms linking androgen deficiency to various comorbidities. In this study, we aimed to identify new protein markers of BAA in humans and test their predictive value in relation to some androgen deficiency linked pathologies. Methods and Findings Plasma samples (n=90) from 30 healthy chemically castrated young volunteer males (19-32 years) were collected at three different time points: baseline – prior to treatment with GnRH antagonist), three weeks after GnRH antagonist treatment, and at the end of the study, two weeks thereafter. Following the second blood sampling, all men were supplemented with 1000 mg T undecanoate. The samples were analysed by mass spectrometry-based proteomics. A total of 676 proteins were identified among which expression of 46 was statistically significantly associated with T concentration (p<0.05, ANOVA paired). Based on ROC-analysis, 3 candidate proteins were selected, 4-hydroxyphenylpyruvate dioxygenase (4HPPD), insulin-like growth factor-binding protein 6 (IGFBP6) and fructose-bisphosphate aldolase (ALDOB). Together with a mathematically calculated variable (MMA: Multi-Marker Algorithm), based on the combination of 4HPPD and IGFBP6 values, their predictive value in relation to low T levels and cardio-metabolic risk factors was tested in 75 males suffering from infertility, 32-43 years of age. Of these males, four were diagnosed with type 2-diabetes and 14 with the metabolic syndrome. Clinical chemistry data regarding cardio-metabolic parameters, bone mineral density and androgen receptor CAG repeat lengths were assessed. The three proteins significantly distinguished between low (< 8 nmol/L) and normal (> 12 nmol/L) morning fasting T (AUC: 0.70-0.77). The MMA had an even higher accuracy (AUC=0.80) in identifying subjects with low T. 4HPPD, ALDOB and MMA showed higher accuracy than T (AUC=0.85-0.92 vs. AUC=0.55; p<.001) in identifying infertile patients with type 2 diabetes mellitus and metabolic syndrome (AUC=0.74-0.78 vs. AUC=0.56; p<0.05). These markers also showed higher AUC than T in predicting adverse cardiovascular risk lipid profile and insulin resistance patients but the differences were not statistically significant. 4HPPD and MMA distinguished patients with low bone density (z-score in DEXA scan < -1), but no difference was observed when comparing with AUC for T,. The androgenic dependence of 4HPPD and ALDOB were confirmed by finding statistically significant lower levels of these markers in men with androgen CAG repeat lengths < 21 (p=0.002) and >22 (p=0.038) as compared to those with CAG of 21-22. No statistical significances were found for IGFBP6. Conclusions 4HPPD and ALDOB were identified as potential biomarkers for diagnosis of male hypogonadism and in identifying subjects at high risk of some of its clinical sequelae. More extensive testing should be carried out in order to elucidate their usefulness in clinical assessment of androgenic activity, not only in adult males but also in women and in pre-pubertal boys.
2022-04-07 | PXD024448 | Pride
Project description:Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels (Affymetrix) and 1.3 million genome-wide SNP markers (Illumina) were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.
2010-09-25 | GSE24274 | GEO
Project description:Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels (Affymetrix) and 1.3 million genome-wide SNP markers (Illumina) were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.
2010-09-25 | GSE24260 | GEO
Project description:Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels (Affymetrix) and 1.3 million genome-wide SNP markers (Illumina) were assayed for all 277 human LCLs. MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. 27 loci, each containing at least 2 SNPs within 50kb with p-values <10-4, were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC with p<10-3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes that were also associated with radiation AUC (p<10-3). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52 and DEPDC1B each significantly altered radiation sensitivity in at least 2 cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.
2010-09-25 | GSE24245 | GEO