Prediction of Radiation-induced Late Intestinal Injury
Ontology highlight
ABSTRACT:
DISEASE(S): Radiation-induced Intestinal Injury,Wounds And Injuries
PROVIDER: 9987 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Our team has constructed a prediction model based on the expression level of immune factors (PD-L1, PD-1, CTLA4, Siglec15) to predict the chronicization of radiation-induced acute intestinal injury (RAII) and verified the predictive efficacy of the system in retrospective studies. This clinical study intends to further prospectively verify the accuracy of this prediction model in rectal cancer patients. In this study, we plan to enroll 200 patients diagnosed with locally advanced rectal cancer by pathology and MRI, who undergo neoadjuvant chemoradiotherapy (NCRT) and total mesorectal excision (TME) and develop RAII during NCRT or within 1 month. We will follow up the occurrence and progression of radiation-induced intestinal injury within 1 year after TME. Expression levels of immune factors will be detected in pathological tissue after TME and applied to the prediction model to predict the chronicization of RAII. Based on the clinical diagnosis of chronic radiation-induced intestinal injury, the area under curve (AUC), accuracy, precision, specificity, and sensitivity of this prediction model in predicting the chronicization of RAII will be evaluated. The main outcome hypothesis is that the AUC of chronicization of RAII predicted by the prediction model based on the expression level of immune factors is more than 0.8.
| 9985 | ecrin-mdr-crc
Project description:Our team has constructed a prediction model based on the expression level of lncRNA (lncRNA-UCID、NEAT1、ciRS-7) to predict the chronicization of radiation-induced acute intestinal injury (RAII) and verified the predictive efficacy of the system in retrospective studies. This clinical study intends to further prospectively verify the accuracy of this prediction model in rectal cancer patients. In this study, we plan to enroll 200 patients diagnosed with locally advanced rectal cancer by pathology and MRI, who undergo neoadjuvant chemoradiotherapy (NCRT) and total mesorectal excision (TME) and develop RAII during NCRT or within 1 month. We will follow up the occurrence and progression of radiation-induced intestinal injury within 1 year after TME. Expression levels of lncRNA will be detected in pathological tissue after TME and applied to the prediction model to predict the chronicization of RAII. Based on the clinical diagnosis of chronic radiation-induced intestinal injury, the area under curve (AUC), accuracy, precision, specificity, and sensitivity of this prediction model in predicting the chronicization of RAII will be evaluated. The main outcome hypothesis is that the AUC of chronicization of RAII predicted by the prediction model based on the expression level of lncRNA is more than 0.8.
| 9986 | ecrin-mdr-crc
Project description:Gene signatures have been shown to predict the response/resistance to immunotherapies but with only modest accuracy. Reduction in this precision might be due to the lack of spatial information which prevents the ability from distinguish tumor from tumor-microenvironment (TME) genes. Here we collected gene expression data spatially from three compartments (CD68+macrophages, CD45+leukocytes and S100+tumor cells) of 59-immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. We achieved AUC≥0.90 for CD45, AUC≥0.94 for CD68, and AUC≥0.86 for S100B signatures, whereas AUC≥0.70 for pseudo-bulk () signature. Cross-testing in different compartments (e.g., CD45 signature in CD68 and S100B compartments) showed poor performance indicating compartment-specificity. Our novel spatial S100B signature showed the best performance with AUC≥0.80 in the validation cohort (N=46). Testing our signatures in computationally deconvolved pseudo-compartments revealed lower AUCs. We conclude that the spatially defined compartment signatures utilize tumor and TME-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical
2023-05-24 | GSE233305 | GEO
Project description:Background: Low-caloric diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression. Yet the relationship with long-term clinical outcomes remains unclear. Objective: We evaluated transcriptome alterations in AT during LCD and association with weight and glycemic outcomes both at LCD termination and 6-month after the LCD. Results: Upon LCD, we identified 1’173 genes differentially expressed; with 350 and 33 genes associated respectively with changes in BMI and Matsuda. Twenty-nine genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Models were constructed to predict weight maintainers. A model based on clinical baseline parameters could not achieve any prediction (validation’s AUC= 0.50 [0.36, 0.64]), while a model based on clinical changes during LCD yielded to good performance (AUC=0.73 [0.60, 0.87]). Incorporating baseline expression from the 18 genes outperformed the best clinical model (AUC=0.87 [0.77, 0.96], Delong’s p=0.012). Similar analyses were made to predict subjects with good glycemic improvements. Both baseline- and LCD-based clinical models yielded to similar performance (with best AUC=0.73 [0.60, 0.85]). Addition of expression changes during LCD improved substantially the performance (AUC=0.80 [0.69, 0.92], p=0.058). Conclusions: This study investigated AT transcriptome alterations following LCD in a large cohort of obese, non-diabetic patients. The identified genes enabled to significantly improve clinical models and predict long-term clinical outcomes. These biomarkers may help clinicians understanding the large inter-subject variability and better predict the success of dietary interventions.
2018-03-01 | GSE95640 | GEO
Project description:We used machine learning models to systematically integrate large-scale multi-omics data from genomics, DNA methylation in blood, and nasal and environmental factors. We firstly evaluated the prediction power of each omics layer and identified that the prediction power was largely attributed to nasal methylation. We further built a prediction model based on only three nasal CpG sites that can predict childhood allergic disease.
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Project description:Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. In this paper, we develop a novel multi-layer encoder for individual sample-specific SL prediction (MLEC-iSL). Unlike existing SL prediction models, MLEC-iSL is built to predict SL connectivity first. Because SL connectivity is scalable from existing genes in the training data to new genes in validation data, we hypothesize MLEC-iSL has better SL prediction performance. MLEC-iSL has three encoders, namely gene encoder, graph encoder, and transformer encoder. MLEC-iSL has high performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells while no existing methods exceed 0.62 AUPR and AUC in either cell. MLEC-iSL guided CDKO experiment in 22Rv1 cells yielded a 46.8% SL ratio amongst its selected gene pairs. Six of top ten SL connectivity hub genes are validated in 22Rv1 cells. It reveals SL gene pairs and dependency between apoptosis and mitosis cell death pathways.
2024-09-13 | GSE262953 | GEO
Project description:Vestibular schwannomas (VS) are benign tumors that lead to significant neurologic and otologic morbidity. How VS heterogeneity and the tumor microenvironment (TME) contribute to the pathogenesis of these tumors remains poorly understood. We performed scRNA-seq on 15 VS samples, with paired scATAC-seq (n=6) and exome sequencing (n=12). We identified diverse Schwann cell (SC), stromal, and immune populations in the VS TME and found that repair-like and MHC-II antigen presenting subtype SCs are associated with increased myeloid cell infiltrate, implicating a nerve injury-like process. Deconvolution of RNA-expression data from 195 tumors revealed Injury-like tumors are associated with larger tumor size, and scATAC-seq identified transcription factors associated with nerve repair among SCs from Injury-like tumors. Ligand-receptor analysis and in vitro experiments suggested that SCs recruit monocytes. Our study indicates that Injury-like SCs may cause tumor growth via myeloid cell recruitment and identifies molecular pathways that may be targeted to prevent tumor progression. This SubSeries is part of a SuperSeries (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE216784).
2023-09-15 | GSE236735 | GEO
Project description:Vestibular schwannomas (VS) are benign tumors that lead to significant neurologic and otologic morbidity. How VS heterogeneity and the tumor microenvironment (TME) contribute to the pathogenesis of these tumors remains poorly understood. We performed scRNA-seq on 15 VS samples, with paired scATAC-seq (n=6) and exome sequencing (n=12). We identified diverse Schwann cell (SC), stromal, and immune populations in the VS TME and found that repair-like and MHC-II antigen presenting subtype SCs are associated with increased myeloid cell infiltrate, implicating a nerve injury-like process. Deconvolution of RNA-expression data from 195 tumors revealed Injury-like tumors are associated with larger tumor size, and scATAC-seq identified transcription factors associated with nerve repair among SCs from Injury-like tumors. Ligand-receptor analysis and in vitro experiments suggested that SCs recruit monocytes. Our study indicates that Injury-like SCs may cause tumor growth via myeloid cell recruitment and identifies molecular pathways that may be targeted to prevent tumor progression. This SubSeries is part of a SuperSeries (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE216784).
2023-09-15 | GSE216781 | GEO
Project description:Chronic injury in kidney transplants remains a major cause of graft loss. The aim of this study was to identify a predictive gene set capable of classifying renal grafts at risk for progressive injury due to fibrosis.The Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicenter study. Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by microarray. Genes were sought which correlated with subsequent 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters. We identified a set of 13 genes that was independently predictive for the development of fi brosis at 1 year (ie, CADI-12 >=2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fi brosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Biopsies obtained prospectively 3 months after transplantation from renal allograft recipients (n=159) with stable renal function were analyzed for gene expression by Affymetrix exon arrays. Genes that were specifically correlated with 12-month Chronic Allograft Damage Index (CADI) but neither CADI in the 3 month biopsy nor other histological or clinical parameters were identified by correlation analysis. The minimal geneset was further identified to predict the progressors/non progressors and validated by qPCR in an independent cohort and two public datasets. This dataset is part of the TransQST collection.
2016-07-20 | E-GEOD-57387 | biostudies-arrayexpress
Project description:Microarray analysis of human kidneys with acute kidney injury (AKI) has been limited because such kidneys are seldom biopsied. However, all kidney transplants experience AKI, and early kidney transplants without rejection are an excellent model for human AKI: they are screened to exclude chronic kidney disease, frequently biopsied, and have extensive follow-up. We used histopathology and microarrays to compare indication biopsies from 28 transplants with AKI to 11 pristine protocol biopsies of stable transplants. Kidneys with AKI showed increased expression of 394 injury-repair response associated transcripts, including many known epithelial injury molecules (e.g. ITGB6, LCN2), tissue remodeling molecules (e.g. VCAN), and inflammation molecules (S100A8, ITGB3). Many other genes also predict the phenotype, depending on statistical filtering rules, including AKI biomarkers as HAVCR1 and IL18. Most mouse orthologs of the top injury-repair transcripts were increased in published mouse AKI models. Pathway analysis of the injury-repair transcripts revealed similarities to cancer, development, and cell movement. The injury-repair transcript score AKI kidneys correlated with reduced function, future recovery, brain death, and need for dialysis, but not future graft loss. In contrast, histologic features of "acute tubular injury" did not correlate with function or with the molecular changes. Thus the injury-repair associated transcripts represent a massive coordinate injury-repair response of kidney parenchyma to AKI, similar to mouse AKI models, and provide an objective measure for assessing the severity of AKI in kidney biopsies and validation for the use of many AKI biomarkers. AKI biopsies sample names and CEL files are from GSE21374. All consenting renal transplant patients undergoing biopsies for cause as standard of care between 09/2004 and 10/2007 at the university of Alberta or between 11/2006 and 02/2007 at the University of Illinois were included in the analysis. In addition to the cores required for standard histopathology, we collected one core for gene expression studies. the relationship between gene expression in the biopsy and subsequent graft loss was analyzed. This dataset is part of the TransQST collection.
2012-02-21 | E-GEOD-30718 | biostudies-arrayexpress