4-gene Signature Research Articles

Convergent and divergent immune aberrations in COVID-19, post-COVID-19-Interstitial Lung Disease and Idiopathic Pulmonary Fibrosis.

We aimed to study transcriptional and phenotypic changes in circulating immune cells associated with increased risk of mortality in COVID-19, resolution of pulmonary fibrosis in post-COVID-19-Interstitial Lung Disease (ILD) and persistence of Idiopathic Pulmonary Fibrosis. Whole blood and Peripheral Blood Mononuclear cells (PBMC) were obtained from 227 subjects with COVID-19, post-COVID-19 Interstitial Lung Disease (ILD), IPF and controls. We measured a 50-gene signature (nCounter, Nanostring) previously found to be predictive of IPF and COVID-19 mortality along with plasma levels of several biomarkers by Luminex. Additionally, we performed single-cell RNA sequencing in PBMC (10X Genomics) to determine the cellular source of the 50-gene signature. We identified the presence of three genomic risk profiles in COVID-19 based on the 50-gene signature associated with low, intermediate, or high-risk of mortality and with significant differences in pro-inflammatory and pro-fibrotic cytokines. COVID-19 patients in the high-risk group had increased expression of seven genes in CD14+HLA-DRlowCD163+Monocytic-Myeloid-Derived Suppressive cells (7Gene-M-MDSCs) and decreased expression of 43 genes in CD4 and CD8 T cell subsets. The loss of 7Gene-M-MDSC and increased expression of these 43 genes in T cells was seen in survivors with post-COVID-19-ILD. On the contrary, IPF patients had low expression of the 43 genes in CD4 and CD8 T cells. Collectively, we showed that a 50-gene, high-risk profile, predictive of IPF and COVID-19 mortality is characterized by a genomic imbalance in monocyte and T-cell subsets. This imbalance reverses in survivors with post-COVID-19-ILD highlighting genomic differences between post-COVID-19-ILD and IPF.

Combined analysis of a serum mRNA/miRNA marker signature and CA 19-9 for timely and accurate diagnosis of recurrence after resection of pancreatic ductal adenocarcinoma: A prospective multicenter cohort study.

Timely and accurate detection of tumor recurrence in pancreatic ductal adenocarcinoma (PDAC) patients is an urgent and unmet medical need. This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples. In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a "No Evidence of Disease" and a "Recurrence" group based on clinical follow-up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). A diagnostic algorithm was developed combining a 7-gene marker signature with CA19-9 data. The best-performing marker combination achieved 90% diagnostic accuracy in predicting the presence of tumor recurrence (98% sensitivity; 84% specificity), clearly outperforming the singular CA 19-9 analysis. Moreover, time series data obtained by analyzing successively collected samples from 5 patients during extended follow-up suggested that molecular diagnosis has the potential to detect recurrence earlier than routine clinical procedures. TaqMan array card measurements were found to be biologically valid and technically reproducible. The BioPac multianalyte marker panel is capable of sensitive and accurate detection of recurrence in patients resected for PDAC using a simple blood test. This could allow a closer follow-up using shorter time intervals than currently used for imaging, thus potentially prompting an earlier work-up with additional modalities to allow for earlier therapeutic intervention. This study provides a promising approach for improved postoperative monitoring of resected PDAC patients, which is an urgent and unmet clinical need.

Validation of a breast cancer assay for radiotherapy omission: an individual participant data meta-analysis.

There are currently no molecular tests to identify individual breast cancers where radiotherapy (RT) offers no benefit. Profile for the Omission of Local Adjuvant Radiotherapy (POLAR) is a 16-gene molecular signature developed to identify low risk cancers where RT will not further reduce recurrence rates. An individual participant data meta-analysis was performed in 623 cases of node-negative ER+/HER2-negative early breast cancer enrolled in three RT randomized trials for whom primary tumor material was available for analysis. A Cox proportional hazards model on time to locoregional recurrence (LRR) was used to test the interaction between POLAR score and RT. 429 (69%) patients' tumors had a high POLAR score and 194 (31%) had a low score. Patients with high POLAR score had, in the absence of RT, a 10-year cumulative incidence of LRR: 20% (15%-26%) vs 5% (2%-11%) for those with a low score. Patients with a high POLAR score had a large benefit from RT (hazard ratio [HR] for RT vs no RT: 0.37 [0.23-0.60], p < .001). In contrast, there was no evidence of benefit from RT for patients with a low POLAR score (HR: 0.92 [0.42-2.02], p = .832). The test for interaction between RT and POLAR was statistically significant (p = .022). POLAR is not only prognostic for locoregional recurrence but also predictive of benefit from radiotherapy in selected patients. Patients ≥ 50 years with ER+/HER2-negative disease and a low POLAR score could consider omitting adjuvant RT. Further validation in contemporary clinical cohorts is required.

Associations of prostate tumor immune landscape with vigorous physical activity and prostate cancer progression.

Vigorous physical activity has been associated with lower risk of fatal prostate cancer. However, mechanisms contributing to this relationship are not understood. We studied 117 men with prostate cancer in the University of North Carolina Cancer Survivorship Cohort (UNC CSC) who underwent radical prostatectomy, and 101 radiation-treated prostate cancer patients in FASTMAN. Structured questionnaires administered in UNC CSC assessed physical activity. In both studies, digital image analysis of H&E-stained tissues was applied to quantify Tumor Infiltrating Lymphocytes (TILs) in segmented regions. Nanostring gene expression profiling in UNC CSC and microarray in FASTMAN were performed on tumor tissue and a 50-gene signature utilized to predict immune cell types. Vigorous recreational activity, reported by 34 (29.1%) UNC men, was inversely associated with TILs abundance. Tumors of men reporting any vigorous activity versus none showed lower gene expression-predicted abundance of Th, exhausted CD4 T cells and macrophages. T cell subsets, including Treg, Th, Tfh, exhausted CD4 T cells, and macrophages were associated with increased risk of biochemical recurrence, only among men with ERG-positive tumors. Vigorous activity was associated with lower prostate tumor inflammation and immune microenvironment differences. Macrophages and T cell subsets, including those with immunosuppressive roles and those with lower abundance in men reporting vigorous exercise, were associated with worse outcomes in ERG-positive prostate cancer. Our novel findings contribute to our understanding of the role of the tumor immune microenvironment in prostate cancer progression, and may provide insight into how vigorous exercise could affect prostate tumor biology.

Establishment of the Prognostic Signature with Genes Related to G2/M Checkpoint in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality in the world. Prognostic indicators such as clinicopathological characteristics and single molecular signature are far from satisfactory in clinical practice. More and more researches have suggested that polygenic prognostic features could predict the prognosis of cancer more precisely than single genes nowadays. In this study, we performed gene set enrichment analysis (GSEA) to identify the sets of TCGA hallmark genes. Univariate Cox regression analysis was used to select preliminary genes, and then multivariate Cox regression analysis was used to identify genes associated with overall survival (OS). We also used Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis to validate the prognostic gene signature. Lastly, qRT-PCR was used to evaluate the expression of these genes in clinical samples, and immunohistochemical staining was performed to confirm the signature. A 12-gene signature was finally built and the risk score was significantly associated with the survival of the patients. Subsequent validation by qRT-PCR and immunohistochemical staining in clinical specimens confirmed the value of the risk score in predicting the prognosis. We developed a 12-gene signature that could predict the prognosis of HCC patients. This signature is of high precision and would help identifying subgroups of HCC patients with high or low risk of unfavorable survival.

Identification of a 5-gene signature panel for the prediction of prostate cancer progression.

Despite nearly 100% 5-year survival for localised prostate cancer, the survival rate for metastatic prostate cancer significantly declines to 32%. Thus, it is crucial to identify molecular indicators that reflect the progression from localised disease to metastatic prostate cancer. To search for molecular indicators associated with prostate cancer metastasis, we performed proteomic analysis of rapid autopsy tissue samples from metastatic prostate cancer (N = 8) and localised prostate cancer (N = 2). Then, we utilised multiple independent, publicly available prostate cancer patient datasets to select candidates that also correlate with worse prostate cancer clinical prognosis. We identified 154 proteins with increased expressions in metastases relative to localised prostate cancer through proteomic analysis. From the subset of these candidates that correlate with prostate cancer recurrence (N = 28) and shorter disease-free survival (N = 37), we identified a 5-gene signature panel with improved performance in predicting worse clinical prognosis relative to individual candidates. Our study presents a new 5-gene signature panel that is associated with worse clinical prognosis and is elevated in prostate cancer metastasis on both protein and mRNA levels. Our 5-gene signature panel represents a potential modality for the prediction of prostate cancer progression towards the onset of metastasis.

Blood transcriptomics reveal systemic eosinophilic and neutrophilic inflammation patterns in patients with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic sinonasal disease characterized by heterogeneous inflammation. However, the presence of systemic inflammation heterogeneity in CRSwNP patients remains unknown. This study aims to profile transcriptomic alterations in the blood of CRSwNP patients and characterize the CRSwNP heterogeneity based on blood transcriptomic biomarkers. Patients with CRSwNP were prospectively recruited from three hospitals and chronologically divided into exploratory (n=123) and independent validation (n=46) cohorts. Transcriptomic profiles were generated by whole blood mRNA sequencing and subjected to patient clustering, differential expression, and pathway analysis. Differences in immune pattern and clinicopathologic features between clusters were assessed. A transcriptomic signature was defined and applied to an independent cohort to validate the findings. CRSwNP patients showed diverse blood transcriptomic profiles versus healthy controls, or when stratified by tissue and blood eosinophils and asthma comorbidity. Transcriptome-wide correlation analysis revealed a transcriptional signature associated with blood eosinophil levels, consisting of nine T2-related genes (CLC, SIGLEC8, ALOX15, IL5RA, PTGDR2, CCL23, CCR3, EPX and IL1RL1). Three distinct clusters with differing systemic eosinophilic and neutrophilic inflammation patterns and asthma comorbidity were identified based on transcriptomic profiling of T2 and T1/3-related blood biomarkers. A 36-gene signature was developed by machine learning and accurately predicted the three CRSwNP subtypes. Validation on an independent cohort confirmed the prediction robustness. There is heterogeneous systemic inflammation associated with eosinophilic and neutrophilic patterns in patients with CRSwNP. Endotyping based on blood transcriptomic biomarkers might lead to more personalized treatment strategies for CRSwNP in the future.

Deciphering the prognostic and therapeutic significance of BAG1 and BAG2 for predicting distinct survival outcome and effects on liposarcoma

Liposarcoma (LPS) is the second most common kind of soft tissue sarcoma, and a heterogeneous malignant tumor derived from adipose tissue. Up to now, the prognostic value of BAG1 or BAG2 in LPS has not been defined yet. Expression profiling data of LPS patients were collected from TCGA and GEO database. Survival curves were plotted to verify the outcome differences of patients based on BAG1 or BAG2 expression. Univariate and multivariate Cox regression models were used to analyze the prognostic ability of BAG1 or BAG2. Chaperone’s regulators BAG1 and BAG2 were identified as prognostic biomarkers for LPS patients, which exhibited distinct expression patterns and survival outcome prediction performances. Patients with high BAG2 expression and/or low BAG1 expression had worse prognosis. Enrichment analysis showed that BAG1 was involved in negative regulation of TGF-β signaling. Low expression of BAG1 was associated with high abundance of regulatory T cells (Tregs). The 2-gene signature model further confirmed the improved risk assessment performance of BAG1 and BAG2: high risk patients displayed poor prognosis. BAG1 and BAG2 are supposed to be potential prognostic biomarkers for LPS and have impacts on liposarcomagenesis and immune infiltration in distinctive manners, which may function as potential therapy targets (BAG1 agonists/BAG2 inhibitors) for LPS.

Predictors of response to venetoclax and therapeutic potential of CDK7 inhibition in multiple myeloma

AbstractVenetoclax, a selective BCL2 inhibitor, has emerged as a promising therapeutic agent for multiple myeloma (MM), particularly in patients harboring the t(11;14) translocation. In this study, we set out to identify markers of sensitivity and resistance to venetoclax in a real-world patient population, aiming to facilitate the development of personalized therapeutic strategies. Through the analysis of RNA-seq data from relapsed/refractory patients treated with venetoclax, either as a single agent or in combination with other drugs, we unveiled a novel 6-gene signature that significantly stratified patients into risk groups for relapse and further validated its clinical relevance in 2 independent clinical and ex vivo data sets. Our analysis also highlighted the negative impact of chromosome 1q gain, which harbors the MCL1 gene, on progression-free survival, even in t(11;14)-positive patients. Encouraged by the well-documented role of MCL1 in resistance to venetoclax in various malignancies and the prognostic importance of the BCL2/MCL1 ratio in our cohort, we explored Cyclin-Dependent Kinase 7 (CDK7) inhibition as a potential strategy to overcome venetoclax resistance. In vitro experiments demonstrated that CRISPR-Cas9–mediated CDK7 depletion led to decreased MCL1 levels, enhancing the sensitivity of MM cells to venetoclax. Moreover, the combination of the CDK7 inhibitor THZ1 with venetoclax markedly induced cell death in venetoclax-resistant MM cells harboring 1q gain, thus offering a rational therapeutic approach, particularly for patients with this aberration. Overall, these findings provide important insights for optimizing venetoclax-based therapeutic strategies in MM.

Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer

Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer

Integrated analysis reveals prognostic correlation and immune characteristics of a tumor-associated macrophage-based risk signature in triple-negative breast cancer.

Tumor-associated macrophages play a critical role in the progression and immune response of triple-negative breast cancer (TNBC). Our study aimed to explore the characteristics of tumor-associated macrophages (TAMs) in TNBC, construct a risk signature associated with TAM clusters, and verify its relationship with prognosis and immune-related characteristics. Firstly, we identified four TAM clusters and determined prognosis-related clusters in TNBC based on the single-cell RNA sequencing (scRNA-seq) data. Subsequently, the TAM-related prognostic genes were obtained by the univariate Cox regression analysis and an 8-gene risk signature was then constructed by least absolute shrinkage and selection operator (LASSO) regression based on these TAM-related prognostic genes. Analyses of immune characteristics showed a significant association between the signature with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that the risk signature was an independent prognostic factor for TNBC, and its value in predicting immunotherapeutic outcomes was also confirmed. A novel nomogram integrating the stage and TAM-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of TNBC. Finally, the increasing expression of GPR34 which is one of the eight hub genes was explored in TNBC by experiments including reverse-transcriptase polymerase chain reaction, western blot, and immunohistochemistry. Our study may provide unique insights into obtaining independent prognostic factors, improving immunotherapeutic strategies, and identifying effective therapeutic targets for TNBC.

TNBC-DX genomic test in early-stage triple-negative breast cancer treated with neoadjuvant taxane-based therapy

BackgroundIdentification of biomarkers to optimize treatment strategies for early-stage triple-negative breast cancer (TNBC) is crucial. This study presents the development and validation of TNBC-DX, a novel test aimed at predicting both short- and long-term outcomes in early-stage TNBC. MethodsInformation from 1,259 patients with early-stage TNBC (SCAN-B, CALGB-40603, and BrighTNess) were used to establish the TNBC-DX scores. Independent validation of TNBC-DX was carried out in 3 studies: i) WSG-ADAPT-TN, ii) MMJ-CAR-2014-01, and iii) NeoPACT, including 527 patients with stage I-III TNBC undergoing neoadjuvant chemotherapy. In WSG-ADAPT-TN, patients were randomized to receive nab-paclitaxel plus gemcitabine or carboplatin. In MMJ-CAR-2014-01, patients received carboplatin plus docetaxel. In NeoPACT, patients received carboplatin plus docetaxel and pembrolizumab. The objective of this study was to evaluate the association between TNBC-DX and efficacy outcomes (pCR, distant disease-free survival [DDFS] or event-free survival [EFS], and overall survival [OS]) in the validation cohorts. ResultsTNBC-DX test was created incorporating 10-gene core immune gene module, 4-gene tumor cell proliferation signature, tumor size, and nodal staging. In the 2 independent validation cohorts without pembrolizumab, the TNBC-DX pCR score was significantly associated with pCR after adjustment for clinicopathological variables and treatment regimen (odds ratio per 10-units increment=1.34, 95% CI 1.20-1.52, p<0.001). pCR rates for the TNBC-DX pCR-high, -medium, and -low categories were 56.3%, 53.6%, and 22.5% respectively (odds ratio for pCR-high vs pCR-low=3.48 [95% CI 1.72-7.15], p<0.001). Additionally, the TNBC-DX risk score was significantly associated with DDFS (hazard ratio [HR] high-risk vs low-risk=0.24, 95% CI 0.15-0.41, p<0.001) and OS (HR=0.19, 95% CI 0.11-0.35, p<0.001). In the validation cohort with pembrolizumab, the TNBC-DX scores were significantly associated with pCR, EFS, and OS. ConclusionsTNBC-DX predicts pCR to neoadjuvant taxane-carboplatin in stage I-III TNBC and helps to forecast the patient´s long-term survival in the absence of neoadjuvant anthracycline/cyclophosphamide, and independent of pembrolizumab use.

Establishment of RNA modification regulators index predicting clinical outcomes and immune relevance of kidney cancer patients

Increasing evidence indicates that RNA modifications are misregulated in human cancers, which might be optimal targets of cancer therapy. However, important RNA regulators in kidney cancer still need further exploration. In this study, we collected regulators representing different types of RNA modification and identified the prognosis-related RNA regulators in kidney cancer patients. We further constructed a 4-gene RNA regulators signature and index called prognosis-related RNA regulators index (PRRI) by the Lasso-Cox regression algorithm. We found that PRRI could precisely predict prognosis of patients in the KIRC training (AUC at 3-/5-/7-years = 0.7132/0.7220/0.7283) and testing cohorts (AUC at 3-/5-/7-years = 0.7141/0.7403/0.7305) and two independent RCC cohorts - E-MTAB-1980 (AUC at 3-/5-/7-years = 0.7036/0.7385/0.7143) and KIRP (AUC at 3-/5-/7-years = 0.6203/0.6365/0.6941). Moreover, the high PRRI group showed a worse clinical outcome than the low PRRI group. PRRI demonstrated strong robustness and was related to histological grade and pathologic stage, which was also found to be an independent prognosis factor when other clinical variables adjusted it. We further found several immune-related pathways differentially enriched in the high or low PRRI group. The regulation of T cell migration, which has been proven to be an immunosuppressive cell, shows a high enrichment in the high PRRI group. Further analysis reveals that PRRI also shows a highly positive correlation with the activity of Tregs. TIDE analysis and two independent immune therapy cohorts revealed that the high PRRI group might resist immune therapy, while the low PRRI group might benefit from the treatment, indicating that PRRI could be a marker for predicting immune therapeutic response. All in all, we determined 4 potentially essential RNA regulators and illustrated their mechanisms concretely. Furthermore, we constructed a 4-gene index called PRRI to predict patients’ outcomes and immunotherapy response.

Construction of store-operated calcium entry-related gene signature for predicting prognosis and indicates immune microenvironment infiltration in stomach adenocarcinomas

Gastric adenocarcinoma (STAD) is the most prevalent malignancy of the human digestive system and the fourth leading cause of cancer-related death. Calcium pools, especially Ca2+ entry (SOCE) for storage operations, play a crucial role in maintaining intracellular and extracellular calcium balance, influencing cell activity, and facilitating tumor progression. Nevertheless, the prognostic and immunological value of SOCE in STAD has not been systematically studied. The objective of this study was to develop a risk model for SOCE signature and to explore its correlation with clinical characteristics, prognosis, tumor microenvironment (TME), as well as response to immunotherapy, chemotherapy, and targeted drugs. We used the TCGA, GEO (GSE84437 and GSE159929), cBioPortal and TIMER databases to acquire mRNA expression data for STAD, along with patient clinical indicators, single-cell sequencing data, genomic variation information, and correlations of immune cell infiltration. An analysis of SOCE genes based on tumor vs. normal tissue comparisons, pan-cancer dimension, single-cell sequencing, DNA mutation, and copy number variation revealed that SOCE genes significantly impact the survival of STAD patients and are differentially involved in the immune response. SOCE co-expressed genes were identified by Pearson analysis, and subsequently protein-protein interaction (PPI) and gene function enrichment analysis indicated that coexpressed genes were associated with multicellular pathways. Based on TCGA and GSE84437 datasets, a multifactor Cox proportional hazard regression analysis was conducted to identify SOCE co-expressed genes associated with overall survival in STAD patients. Several mRNA prognostic genes, including PTPRJ, GPR146, LTBP3, FBLN1, EFEMP2, ADAMTS7 and LBH, were identified, which could be used as effective prognostic predictors for STAD patients. In both training and test datasets, receiver operating characteristic (ROC) curves were utilized to evaluate and illustrate the predictive capability of this characteristic in forecasting overall survival of STAD patients. The qPCR and human protein atlas (HPA) were employed to assess mRNA expression and protein levels. Furthermore, the ESTIMATE, TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, xCell and EPIC algorithms were utilized to perform immune score and analyze immune cell infiltration. It effectively revealed the difference in prognosis and immune cell infiltration in TME between high-risk and low-risk groups based on the risk signature associated with SOCE. Notably, significant differences in tumor immune dysfunction and rejection (TIDE) scores between the two groups, suggesting that patients in the low-risk group may exhibit a more favorable response to ICIS treatment. The GDSC database and R packages for predictive analysis were utilized to analyze responses to chemotherapy and targeted drugs in high-risk and low-risk groups. In summary, the 7-gene signature associated with SOCE serves as a significant biomarker for evaluating the TME and predicting the prognosis of STAD patients. In addition, it may provide valuable insights for developing effective immunotherapy and chemotherapy regiments for patients with STAD.

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven 7-Gene Stemness Signature that Predicts Progression.

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting 7-gene signature (KMT5C, MEN1, TYMS, IRF5, DNMT3B, CDC25B and DPP4) was validated across independent cohorts and patient-derived xenograft (PDX) models. The signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the 7-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

Pan-cancer analysis of ADAR1 with its prognostic relevance in low-grade glioma

ADAR1, known as the primary enzyme for adenosine-to-inosine RNA editing, has recently been implicated in cancer development through both RNA editing-dependent and −independent pathways. These discoveries suggest that ADAR1′s functions may extend beyond our current understanding. A pan-cancer analysis offers a unique opportunity to identify both common and distinct mechanisms across various cancers, thereby advancing personalized medicine. Low-grade glioma (LGG), characterized by a diverse group of tumor cells, presents a challenge in risk stratification, leading to significant variations in treatment approaches. Recently discovered molecular alterations in LGG have helped to refine the stratification of of these tumors and offered novel targets for predicting likely outcomes. This study aims to provide a detailed analysis of ADAR mRNA across multiple cancers, emphasizing its prognostic significance in LGG. We observed inconsistent mRNA and consistent protein expression patterns of ADAR1/ADAR in pan-cancer analyses that across tumor types. ADAR mRNA expression did not always correlate with ADAR1 protein expression. Nevertheless, the transcript levels correlated significantly with genetic alterations, tumor mutation burden, microsatellite instability, overall survival, recurrence-free survival, immune marker presence, immune infiltration, and the survival of patients undergoing immunotherapy in select cancers. Furthermore, ADAR and its top 50 associated genes were primarily involved in mRNA-related events, as identified through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Utilizing the Cox proportional hazards model, we developed a 3-gene signature (ADAR, HNRNPK, and SMG7), which effectively stratified patients into high- and low-risk groups, with high-risk patients exhibiting poorer overall survival, higher tumor grades, and a greater number of non-codeletions. Overall, this signature was inversely related to immune infiltration across cancers. Transcription factor SPI1 and miR-206, potential upstream regulators of the signature genes, were closely linked to patient survival in LGG. The promoter regions of these genes were hypermethylated, further associating them with patient outcomes. Additionally, these genes displayed consistent drug susceptibility patterns. In conclusion, our findings reveal multiple aspects of ADAR1′s role in cancer and underscore its prognostic value in LGG, offering insights into potential therapeutic targets and strategies.

Transcriptomic signatures during normothermic liver machine perfusion correspond with graft quality and predict the early graft function

A better understanding of the molecular events during liver normothermic machine perfusion (NMP) is warranted to develop a data-based approach for the identification of biomarkers representative of graft quality and posttransplant outcome. We analysed the dynamic transcriptional changes during NMP and linked them to clinical and biochemical parameters. 50 livers subjected to NMP for up to 24h were enrolled. Bulk RNA sequencing was performed in serial biopsies collected pre and during NMP, and after reperfusion. Perfusate was sampled to monitor liver function. qPCR and immunohistochemistry were performed to validate findings. Molecular profiles were compared between transplanted and non-transplanted livers, and livers with and without early allograft dysfunction. Pathways related to immune and cell stress responses, cell trafficking and cell regulation were activated during NMP, while cellular metabolism was downregulated over time. Anti-inflammatory responses and genes involved in tissue remodelling were induced at later time-points, suggesting a counter-response to the immediate damage. NMP strongly induced a gene signature associated with ischemia-reperfusion injury. A 7-gene signature corresponds with the benchmarking criteria for transplantation or discard at 6h NMP (area under curve 0.99). CD274 gene expression (encoding programmed cell-death ligand-1) showed the highest predictive value. LEAP2 gene expression at 6h NMP correlated with impaired graft function. Assessment of gene expression markers could serve as a reliable tool to evaluate liver quality during NMP and predicts early graft function after transplantation. The research was supported by "In Memoriam Dr. Gabriel Salzner Stiftung", Tiroler Wissenschaftsfond, Jubiläumsfonds-Österreichische Nationalbank and MUI Start grant.

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

Characterizing the Cell-Free Transcriptome in a Humanized Diffuse Large B-Cell Lymphoma Patient-Derived Tumor Xenograft Model for RNA-Based Liquid Biopsy in a Preclinical Setting.

The potential of RNA-based liquid biopsy is increasingly being recognized in diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin's lymphoma. This study explores the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft (PDTX) model. Blood plasma samples (n = 171) derived from a DLBCL PDTX model, including 27 humanized (HIS) PDTX, 8 HIS non-PDTX, and 21 non-HIS PDTX non-obese diabetic (NOD)-scid IL2Rgnull (NSG) mice were collected during humanization, xenografting, treatment, and sacrifice. The mice were treated with either rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), CD20-targeted human IFNα2-based AcTaferon combined with CHOP (huCD20-Fc-AFN-CHOP), or phosphate-buffered saline (PBS). RNA was extracted using the miRNeasy serum/plasma kit and sequenced on the NovaSeq 6000 platform. RNA sequencing data of the formalin-fixed paraffin-embedded (FFPE) tissue and blood plasma samples of the original patient were included. Flow cytometry was performed on immune cells isolated from whole blood, spleen, and bone marrow. Bulk deconvolution was performed using the Tabula Sapiens v1 basis matrix. Both R-CHOP and huCD20-Fc-AFN-CHOP were able to control tumor growth in most mice. Xenograft tumor volume was strongly associated with circulating tumor RNA (ctRNA) concentration (p < 0.001, R = 0.89), as well as with the number of detected human genes (p < 0.001, R = 0.79). Abundance analysis identified tumor-specific biomarkers that were dynamically tracked during tumor growth or treatment. An 8-gene signature demonstrated high accuracy for assessing therapy response (AUC 0.92). The tumoral gene detectability in the ctRNA of the PDTX-derived plasma was associated with RNA abundance levels in the patient's tumor tissue and blood plasma (p < 0.001), confirming that tumoral gene abundance contributes to the cell-free RNA (cfRNA) profile. Decomposing the transcriptome, however, revealed high inter- and intra-mouse variability, which was lower in the HIS PDTX mice, indicating an impact of human engraftment on the stability and profile of cfRNA. Immunochemotherapy resulted in B cell depletion, and tumor clearance was reflected by a decrease in the fraction of human CD45+ cells. Lastly, bulk deconvolution provided complementary biological insights into the composition of the tumor and circulating immune system. In conclusion, the blood plasma-derived transcriptome serves as a biomarker source in a preclinical PDTX model, enables the assessment of biological pathways, and enhances the understanding of cfRNA dynamics.

A Bruton tyrosine kinase inhibitor-resistance gene signature predicts prognosis and identifies TRIP13 as a potential therapeutic target in diffuse large B-cell lymphoma

Bruton tyrosine kinase inhibitor (BTKi) combined with rituximab-based chemotherapy benefits diffuse large B-cell lymphoma (DLBCL) patients. However, drug resistance is the major cause of relapse and death of DLBCL. In this study, we conducted a comprehensive analysis BTKi-resistance related genes (BRRGs) and established a 10-gene (CARD16, TRIP13, PSRC1, CASP1, PLBD1, CARD6, CAPG, CACNA1A, CDH15, and NDUFA4) signature for early identifying high-risk DLBCL patients. The resistance scores based on the BRRGs signature were associated with prognosis. Furthermore, we developed a nomogram incorporating the BRRGs signature, which demonstrated excellent performance in predicting the prognosis of DLBCL patients. Notably, tumor immune microenvironment, biological pathways, and chemotherapy sensitivity were different between high- and low-resistance score groups. Additionally, we identified TRIP13 as a key gene in our model. TRIP13 was found to be overexpressed in DLBCL and BTKi-resistant DLBCL cell lines, knocking down TRIP13 suppresses cell proliferation, promotes cell apoptosis, and enhances the apoptosis effect of BTKi on DLBCL cells by regulating the Wnt/β-catenin pathway. In conclusion, our study presents a novel BRRGs signature that could serve as a promising prognostic marker in DLBCL, and TRIP13 might be a potential therapeutic target for resistant DLBCL.