Public Gene Expression Datasets Research Articles

E.PathDash, pathway activation analysis of publicly available pathogen gene expression data.

Chronic respiratory illnesses impose a high disease burden on our communities and people with respiratory diseases are susceptible to robust bacterial infections from pathogens, including Pseudomonas aeruginosa and Staphylococcus aureus, that contribute to morbidity and mortality. Public gene expression datasets generated from these and other pathogens are abundantly available and an important resource for synthesizing existing pathogenic research, leading to interventions that improve patient outcomes. However, it can take many hours or weeks to render publicly available datasets usable; significant time and skills are needed to clean, standardize, and apply reproducible and robust bioinformatic pipelines to the data. Through collaboration with two microbiologists, we have shown that E.PathDash addresses this problem, enabling them to elucidate pathogen responses to a variety of over 400 experimental conditions and generate mechanistic hypotheses for cell-level behavior in response to disease-relevant exposures, all in a fraction of the time.

Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis

BackgroundProstate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.MethodsThis study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.ResultsAn ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.ConclusionsThis study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.

Mitochondrial dynamics as a potential therapeutic target in acute myeloid leukemia.

Acute myeloid leukemia (AML) cells are highly dependent on oxidative phosphorylation and the mitochondrial dynamics regulated by fusion-related genes MFN1, MFN2, and OPA1 and fission-related genes DNM1L and MFF. An analysis of previously published gene expression datasets showed that high expression of MFF was significantly associated with poor prognosis in patients with AML. Based on this finding, we investigated the impact of mitochondrial dynamics in AML. Transduction of shRNA against fission-related genes, DNM1L and MFF, inhibited growth and increased the mitochondrial area in AML cell lines. Extracellular flux analysis showed that deletion of mitochondrial dynamic regulators reduced mitochondrial respiration without significantly affecting glycolysis, except in shDNM1L-transfected cells. Immunodeficient NOG mice transplanted with DNM1L- or MFF-knockdown AML cells survived significantly longer than controls. Treatment of AML cell lines with Mdivi-1, which inhibits the DRP1 encoded by DNM1L, inhibited cell proliferation and oxidative phosphorylation. Our results show that mitochondrial dynamics play an important role in AML, and provide novel biological insights. The inhibition of mitochondrial dynamics induces unique mitochondrial alterations, which may be explored as a potential therapeutic target in AML.

Bayesian clustering with uncertain data.

Clustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points. We show that DPMUnc out-performs existing methods on simulated data. We cluster immune-mediated diseases (IMD) using GWAS summary statistics, which have uncertainty linked with the sample size of the study. DPMUnc separates autoimmune from autoinflammatory diseases and isolates other subgroups such as adult-onset arthritis. We additionally consider how DPMUnc can be used to cluster gene expression datasets that have been summarised using gene signatures. We first introduce a novel procedure for generating a summary of a gene signature on a dataset different to the one where it was discovered, which incorporates a measure of the variability in expression across signature genes within each individual. We summarise three public gene expression datasets containing patients with a range of IMD, using three relevant gene signatures. We find association between disease and the clusters returned by DPMUnc, with clustering structure replicated across the datasets. The significance of this work is two-fold. Firstly, we demonstrate that when data has associated uncertainty, this uncertainty should be used to inform clustering and we present a method which does this, DPMUnc. Secondly, we present a procedure for using gene signatures in datasets other than where they were originally defined. We show the value of this procedure by summarising gene expression data from patients with immune-mediated diseases using relevant gene signatures, and clustering these patients using DPMUnc.

Optimised multiple data partitions for cluster-wise imputation of missing values in gene expression data

It is commonly agreed that the quality of data analysis may be degraded by the presence of missing data. In various domains such as bioinformatics, an effective tool is required for the discovery of knowledge from gene expression datasets. One may simply ignore defected samples, while others attempt to either make an algorithm robust to the problem or develop an imputation technique to fill in missing values. This research focuses on the latter and introduces a new hybridisation of cluster- and neighbour-based references to generate an accurate estimate. It also proposes a novel exploitation of multiple clusterings as the source of cluster-wise information, instead of a single data partition that has been studied by existing methods. These data partitions are selected from a pool of base clusterings with respect to both quality and diversity criteria. Another hybridisation is thus established between swarm intelligence and this search problem. In particular, the algorithm of artificial bee colony (ABC) is explored, with two new operators being invented to allow an evolution of solutions, or food sources for bees. Also, two different imputation strategies are provided to generate entries of missing entries in a data matrix, called cluster-only and cluster-neighbour. Based on published gene expression datasets and different experimental settings, the resulting models usually outperform their baselines and recent approaches, which make use of cluster analysis or devise an intelligent determination of nearest neighbours. Furthermore, they have proven competitive to a benchmark technique belonging to the global approach, especially with high missing ratios. Further extensions to iterative refinement and supervised imputation are discussed in addition to parameter analysis.

Phantasus, a web application for visual and interactive gene expression analysis.

Transcriptomic profiling became a standard approach to quantify a cell state, which led to the accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here, we present Phantasus: a user-friendly web application for interactive gene expression analysis which provides a streamlined access to more than 96,000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing, and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed online at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under an MIT license.

Particulate matter-induced gene expression patterns in human-derived cells based on 11 public gene expression datasets.

Exposure to particulate matter (PM) and house dust mite (HDM) can change the expression patterns of inflammation-, oxidative stress-, and cell death-related genes. We investigated the changes in gene expression patterns owing to PM exposure. This study examined the changes in gene expression patterns following PM exposure. We searched for differentially expressed genes (DEGs) following PM exposure using five cell line-based RNA-seq or microarray datasets and six human-derived datasets. The enrichment terms of the DEGs were assessed. DEG analysis yielded two gene sets. Thus, enrichment analysis was performed for each gene set, and the enrichment terms related to respiratory diseases were presented. The intersection of six human-derived datasets and two gene sets was obtained, and the expression patterns following PM exposure were observed. Two gene sets were obtained for cells treated with PM and their expression patterns were presented following verification in human-derived cells. Our findings suggest that exposure to PM2.5 and HDM may reveal changes in genes that are associated with diseases, such as allergies, highlighting the importance of mitigating PM2.5 and HDM exposure for disease prevention.

Thyroiditis and Thyroid Cancer: Bioinformatics Analysis of Gene Expression Data.

Hashimoto thyroiditis (HT) association with thyroid lymphoma is well established; however, the association with papillary thyroid cancer (PTC) is still unclear. Thyroid cancer incidence has shown an increasing trend in recent years. It is characterized by slow growth, making it generally amenable to successful treatment. We aimed to identify genes considered as promising biomarkers of the progression from thyroiditis to thyroid cancer in public gene expression datasets. We identified 70 differentially expressed genes (DEGs) and used them to prioritize biological risk genes for thyroiditis and thyroid cancer. Statistics and a scoring system based on six functional annotations of significant biological impact identified four genes of interest: CXCR4, IL6ST, PPARG and TP53. Kaplan-Meier plots were used to assess the expression levels related to overall survival. Furthermore, a manual bibliographic search was carried out for each gene, and a protein-protein interaction (PPI) network was built to verify their known associations. The results showed that all four genes (CXCR4, IL6ST, PPARG, TP53) were highly relevant to thyroiditis and thyroid cancer, thus making them worthy of further investigation to understand their relationship with these two diseases.

Effect of SDF-1 and CXCR4 gene variants on the development of diabetic kidney disease.

Diabetic kidney disease (DKD) is the gradual loss of renal function occurring in patients with diabetes. Stromal cell-derived factor-1 (SDF-1, encoded by SDF-1 gene) is a chemokine that binds to its receptor, CXCR4, to mediate many aspects of renal biology. To test the potential impact of SDF-1/CXCR4 gene variations on the risk for DKD, single-nucleotide polymorphisms (SNPs) of SDF-1/CXCR4 genes were genotyped in 388 DKD patients and 335 DKD-free diabetic controls. Among 6 SNPs examined, we demonstrated that rs1801157 of SDF-1 gene was associated with an increased risk for DKD (GA vs GG, AOR=2.252, p=0.035; GA+AA vs GG, AOR=2.156, p=0.036). Further stratification revealed that the correlation of rs1801157 with DKD was particularly detected in diabetic patients with early CKD but not in those with severe renal impairment. Instead, another SNP of SDF-1 gene, rs266085, was found in association with the advanced form of DKD (TC vs TT, AOR=2.106, p=0.027; TC+CC vs TT, AOR=2.130, p=0.019), indicating differential impacts of SDF-1 gene polymorphisms on the progressive loss of renal function in diabetic patients. Moreover, preliminary survey of public gene expression datasets showed that rs1801157 and rs266085 modulated SDF-1 expression in many human tissues, and SDF-1/CXCR4 levels were elevated in renal tissues of DKD patients. These data suggest that allele-specific expression of SDF-1 gene may influence DKD progression.

Glyoxalase 1: Emerging biomarker and therapeutic target in cervical cancer progression.

Cervical cancer presents a significant global health challenge, disproportionately impacting underserved populations with limited access to healthcare. Early detection and effective management are vital in addressing this public health concern. This study focuses on Glyoxalase-1 (GLO1), an enzyme crucial for methylglyoxal detoxification, in the context of cervical cancer. We assessed GLO1 expression in cervical cancer patient samples using immunohistochemistry. In vitro experiments using HeLa cells were conducted to evaluate the impact of GLO1 inhibition on cell viability and migration. Single-cell RNA sequencing (scRNA-seq) and gene set variation analysis were utilized to investigate the role of GLO1 in the metabolism of cervical cancer. Additionally, public microarray data were analyzed to determine GLO1 expression across various stages of cervical cancer. Our analysis included 58 cervical cancer patients, and showed that GLO1 is significantly upregulated in cervical cancer tissues compared to normal cervical tissues, independent of pathological findings and disease stage. In vitro experiments indicated that GLO1 inhibition by S-p-bromobenzylglutathione cyclopentyl diester decreased cell viability and migration in cervical cancer cell lines. Analyses of scRNA-seq data and public gene expression datasets corroborated the overexpression of GLO1 and its involvement in cancer metabolism, particularly glycolysis. An examination of expression data from precancerous lesions revealed a progressive increase in GLO1 expression from normal tissue to invasive cervical cancer. This study highlights the critical role of GLO1 in the progression of cervical cancer, presenting it as a potential biomarker and therapeutic target. These findings contribute valuable insights towards personalized treatment approaches and augment the ongoing efforts to combat cervical cancer. Further research is necessary to comprehensively explore GLO1's potential in clinical applications.

Establishment of the Myeloid TBX-Code Reveals Aberrant Expression of T-Box Gene TBX1 in Chronic Myeloid Leukemia.

T-box genes encode transcription factors, which control developmental processes and promote cancer if deregulated. Recently, we described the lymphoid TBX-code, which collates T-box gene activities in normal lymphopoiesis, enabling identification of members deregulated in lymphoid malignancies. Here, we have extended this analysis to cover myelopoiesis, compiling the myeloid TBX-code and, thus, highlighting which of these genes might be deregulated in myeloid tumor types. We analyzed public T-box gene expression datasets bioinformatically for normal and malignant cells. Candidate T-box-gene-expressing model cell lines were identified and examined by RQ-PCR, Western Blotting, genomic profiling, and siRNA-mediated knockdown combined with RNA-seq analysis and live-cell imaging. The established myeloid TBX-code comprised 10 T-box genes, including progenitor-cell-restricted TBX1. Accordingly, we detected aberrant expression of TBX1 in 10% of stem/progenitor-cell-derived chronic myeloid leukemia (CML) patients. The classic CML cell line K-562 expressed TBX1 at high levels and served as a model to identify TBX1 activators, including transcription factor GATA1 and genomic amplification of the TBX1 locus at 22q11; inhibitors, including BCR::ABL1 fusion and downregulated GNAI2, as well as BMP, FGF2, and WNT signaling; and the target genes CDKN1A, MIR17HG, NAV1, and TMEM38A. The establishment of the myeloid TBX-code permitted identification of aberrant TBX1 expression in subsets of CML patients and cell lines. TBX1 forms an integral part of an oncogenic regulatory network impacting proliferation, survival, and differentiation. Thus, the data spotlight novel diagnostic markers and potential therapeutic targets for this malignancy.

Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn's Disease.

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn's disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA). Cellular heterogeneity was inferred using CiberSortx, with bulk RNA-seq data. The PCA results displayed two clusters of CD-inflamed samples: one close to (Inflamed_1) and the other far away (Inflamed_2) from the non-inflamed samples. Inflamed_1 was rich in anti-TNF durable responders (DRs), and Inflamed_2 was enriched in non-durable responders (NDRs). The CiberSortx results showed that the cell fraction of activated fibroblasts was six times higher in Inflamed_2 than in Inflamed_1. Validation with public gene expression datasets (GSE16879) revealed that the activated fibroblasts were enriched in NDRs over Next, we used DRs by 1.9 times pre-treatment and 7.5 times after treatment. Fibroblast activation protein (FAP) was overexpressed in the Inflamed_2 and was also overexpressed in the NDRs in both the RISK and GSE16879 datasets. The activation of fibroblasts may play a role in resistance to anti-TNF therapy. Characterizing fibroblasts in inflamed tissues at diagnosis may help to identify patients who are likely to respond to anti-TNF therapy.

Immunologic constant of rejection as a predictive biomarker of immune checkpoint inhibitors efficacy in non-small cell lung cancer

BackgroundAnti-PD1/PDL1 immune checkpoint inhibitors (ICI) transformed the prognosis of patients with advanced non-small cell lung cancer (NSCLC). However, the response rate remains disappointing and toxicity may be life-threatening, making urgent identification of biomarkers predictive for efficacy. Immunologic Constant of Rejection signature (ICR) is a 20-gene expression signature of cytotoxic immune response with prognostic value in some solid cancers. Our objective was to assess its predictive value for benefit from anti-PD1/PDL1 in patients with advanced NSCLC.MethodsWe retrospectively profiled 44 primary tumors derived from NSCLC patients treated with ICI as single-agent in at least the second-line metastatic setting. Transcriptomic analysis was performed using the nCounter® analysis system and the PanCancer Immune Profiling Panel. We then pooled our data with clinico-biological data from four public gene expression data sets, leading to a total of 162 NSCLC patients treated with single-agent anti-PD1/PDL1. ICR was applied to all samples and correlation was searched between ICR classes and the Durable Clinical Benefit (DCB), defined as stable disease or objective response according to RECIST 1.1 for a minimum of 6 months after the start of ICI.ResultsThe DCB rate was 29%; 22% of samples were classified as ICR1, 30% ICR2, 22% ICR3, and 26% ICR4. These classes were not associated with the clinico-pathological variables, but showed enrichment from ICR1 to ICR4 in quantitative/qualitative markers of immune response. ICR2-4 class was associated with a 5.65-fold DCB rate when compared with ICR1 class. In multivariate analysis, ICR classification remained associated with DCB, independently from PDL1 expression and other predictive immune signatures. By contrast, it was not associated with disease-free survival in 556 NSCLC TCGA patients untreated with ICI.ConclusionThe 20-gene ICR signature was independently associated with benefit from anti-PD1/PDL1 ICI in patients with advanced NSCLC. Validation in larger retrospective and prospective series is warranted.

Summarising multiple clustering-centric estimates with OWA operators for improved KNN imputation on microarray data

As part of celebrating the success of OWA operators and their contributions over the past decades, this work presents an original investigation of exploiting OWA in dealing with missing value imputation witnessed in microarray experimental data. This task is significant in life science and its realisation to humanity. Both argument-independent and -dependent variants of such operators are applied to summarise a collection of estimates, determined through the concept of clustering-centric KNN imputation. This provides an innovative alternative to the state-of-the-art model that makes use of a single clustering to identify neighbours of a particular instance of interest. Instead of manually specify data partition, the proposed approach works by selecting a subset of diverse clusterings or committees from a candidate pool, which has been prepared using k-means and different (and popular) generation strategies invented for ensemble clustering. It is automated through a greedy forward-search looking for a desired number of committee members. Based on published gene expression datasets and different experimental settings, the resulting model generally outperforms its baselines, being competitive to related methods found in the literature. Further extensions to iterative refinement and supervised imputation are also discussed in addition to the analysis of algorithmic parameters.

The roles of different forms of IL-15 in human melanoma progression.

Melanoma is a lethal skin cancer, and the risk of developing it is increased by exposure to ultraviolet (UV) radiation. The production of cytokines such as interleukin-15 (IL-15), induced by the exposure of skin cells to UV rays, could also promote melanoma development. The aim of this study is to investigate the possible role of Interleukin-15/Interleukin-15 Receptor α (IL-15/IL-15Rα) complexes in melanoma development. The expression of IL-15/IL-15Rα complexes by melanoma cells was evaluated both ex vivo and in vitro by tissue microarray, PCR, and flow cytometry. The presence of the soluble complex (sIL-15/IL-15Rα) in the plasma of metastatic melanoma patients was detected using an ELISA assay. Subsequently, we investigated the impact of natural killer (NK) cell activation after rIL-2 starvation followed by exposure to the sIL-15/IL-15Rα complex. Finally, by analyzing public datasets, we studied the correlation between IL-15 and IL-15Rα expressions and melanoma stage, NK and T-cell markers, and overall survival (OS). Analysis of a melanoma tissue microarray shows a significant increase in the number of IL-15+ tumor cells from the benign nevi to metastatic melanoma stages. Metastatic melanoma cell lines express a phorbol-12-myristate-13-acetate (PMA)-cleavable membrane-bound IL-15 (mbIL-15), whereas cultures from primary melanomas express a PMA-resistant isoform. Further analysis revealed that 26% of metastatic patients present with consistently high plasmatic levels of sIL-15/IL-15Rα. When the recombinant soluble human IL-15/IL-15Rα complex is added to briefly starved rIL-2-expanded NK cells, these cells exhibit strongly reduced proliferation and levels of cytotoxic activity against K-562 and NALM-18 target cells. The analysis of public gene expression datasets revealed that high IL-15 and IL-15Rα intra-tumoral production correlates with the high levels of expression of CD5+ and NKp46+ (T and NK markers) and significantly correlates with a better OS in stages II and III, but not in stage IV. Membrane-bound and secreted IL-15/IL-15Rα complexes are continuously present during progression in melanoma. It is notable that, although IL-15/IL-15Rα initially promoted the production of cytotoxic T and NK cells, at stage IV promotion of the development of anergic and dysfunctional cytotoxic NK cells was observed. In a subgroup of melanoma metastatic patients, the continuous secretion of high amounts of the soluble complex could represent a novel NK cell immune escape mechanism.

Genome-wide detection of m6A-associated SNPs in atrial fibrillation pathogenesis.

N6-Methyladenosine (m6A) modification is of great importance in both the pathological conditions and physiological process. The m6A single nucleotide polymorphisms (SNPs) are associated with cardiovascular diseases including coronary artery disease, heart failure. However, it is unclear whether m6A-SNPs are involved in atrial fibrillation (AF). Here, we aimed to explore the relationship between m6A-SNPs and AF. The relationship between m6A-SNPs and AF was evaluated by analyzing the AF genome-wide association study (GWAS) and m6A-SNPs annotated by the m6AVar database. Further, eQTL and gene differential expression analysis were performed to confirm the association between these identified m6A-SNPs and their target genes in the development of AF. Moreover, we did the GO enrichment analysis to figure out the potential functions of these m6A-SNPs affected genes. Totally, 105 m6A-SNPs were identified to be significantly associated with AF (FDR < 0.05), among which 7 showed significant eQTL signals on local genes in the atrial appendage. By using four public AF gene expression datasets, we identified genes SYNE2, USP36, and THAP9 containing SNPs rs35648226, rs900349, and rs1047564 were differentially expressed in AF population. Further, SNPs rs35648226 and rs1047564 are potentially associated with AF by affecting m6A modification and both of them might have an interaction with RNA-binding protein, PABPC1. In summary, we identified m6A-SNPs associated with AF. Our study provided new insights into AF development as well as AF therapeutic target.

Identification of genetic and immune signatures for the recurrence of HER2-positive breast cancer after trastuzumab-based treatment

To determine the genetic and immune features associated with the recurrence of human epidermal growth factor receptor2-positive (HER2 +) breast cancer (BC) after trastuzumab-based treatment. A retrospective cohort study of 48 patients who received trastuzumab-based treatment was divided into recurrent and non-recurrent groups according to clinical follow-up. Baseline samples from all 48 patients were analyzed for genetic variation, HLA allele type, gene expression, and immune features, which were linked to HER2 + BC recurrence. Statistics included logistic regression models, Kaplan-Meier plots, and Univariate Cox proportional hazards models. Compared with the non-recurrent group, the extracellular matrix-related pathway and 3 Hallmark gene sets were enriched in the recurrent group. The infiltration levels of immature B cells and activated B cells were significantly increased in the non-recurrent group, which correlated remarkably with improved overall survival (OS) in two other published gene expression datasets, including TCGA and METABRIC. In the TCGA cohort (n = 275), activated B cells (HR 0.23, 95%CI 0.13-0.43, p < 0.0001), and immature B cells (HR 0.26, 95%CI 0.12-0.59, p < 0.0001). In the METABRIC cohort (n = 236), activated B cells (HR 0.60, 95%CI 0.43-0.83, p = 0.002), and immature B cells (HR 0.65, 95%CI 0.47-0.91, p = 0.011). Cox regression suggested that immature B cells and activated B cells were protective factors for outcome OS. Aberrant activation of multiple pathways and low baseline tumor-infiltrating B cells are related to HER2 + BC trastuzumab-based recurrence, which primarily affects the antitumor activity of trastuzumab.

CD8+ CD226high T cells in liver metastases dictate the prognosis of colorectal cancer patients treated with chemotherapy and radical surgery

CD226 has been reported to participate in the rescue of CD8+ T cell dysfunction. In this study, we aimed to assess the prognostic value of CD226 in tumor-infiltrating lymphocytes (TILs) derived from colorectal cancer (CRC) liver metastases treated with chemotherapy and radical surgery. TILs from 43 metastases were isolated and analyzed ex vivo using flow cytometry. CD155 and CD3 levels in the tumor microenvironment were assessed by immunohistochemistry. Exploration and validation of biological processes highlighted in this study were performed by bioinformatics analysis of bulk RNA-seq results for 28 CRC liver metastases pretreated with chemotherapy as well as public gene expression datasets. CD226 expression contributes to the definition of the immune context in CRC liver metastases and primary tumors. CD226 on CD8+ T cells was not specifically coexpressed with other immune checkpoints, such as PD1, TIGIT, and TIM3, in liver metastases. Multivariate Cox regression analysis revealed CD226 expression on CD8+ T cells to be an independent prognostic factor (p = 0.003), along with CD3 density at invasion margins (p = 0.003) and TIGIT expression on CD4+ T cells (p = 0.019). CD155 was not associated with the prognostic value of CD226. Gene expression analysis in a validation dataset confirmed the prognostic value of CD226 in CRC liver metastases but not in primary tumors. Downregulation of CD226 on CD8+ TILs in the liver microenvironment was restored by IL15 treatment. Overall, CD226 expression on liver metastasis-infiltrating CD8+ T cells selectively contributes to immune surveillance of CRC liver metastases and has prognostic value for patients undergoing radical surgery.

A new enrichment approach for candidate gene detection in unexplained recurrent pregnancy loss and implantation failure.

Recurrent pregnancy loss (RPL) and implantation failure (RIF) are obstacles to livebirth and multifactorial conditions in which nearly half of the cases remain unexplained, and we aimed to identify maternal candidate gene variants and pathways for RPL and RIF by analyzing whole-exome sequencing (WES) data via a new detailed bioinformatics approach. A retrospective cohort study was applied to 35 women with normal chromosomal configuration diagnosed with unexplained RPL and/or RIF. WES and comprehensive bioinformatics analyses were performed. Published gene expression datasets (n = 46) were investigated for candidate genes. Variant effects on protein structure were analyzed for 12 proteins, and BUB1B was visualized in silico. WES and bioinformatics analyses are effective and applicable for studying URPL and RIF to detect mutations, as we suggest new candidates to explain the etiology. Forty-three variants in 39 genes were detected in 29 women, 7 of them contributing to oligogenic inheritance. These genes were related to implantation, placentation, coagulation, metabolism, immune system, embryological development, cell cycle-associated processes, and ovarian functions. WES, genomic variant analyses, expression data, and protein configuration studies offer new and promising ways to investigate the etiology of URPL and RIF. Discovering etiology-identifying genetic factors can help manage couples' needs and develop personalized therapies and new pharmaceutical products in the future. The classical approach with chromosomal analysis and targeted gene panel testing is insufficient in these cases; the exome data provide a promising way to detect and understand the possible clinical effects of the variant and its alteration on protein structure.

TMEM158 expression is negatively regulated by AR signaling and associated with favorite survival outcomes in prostate cancers.

Membrane protein TMEM158 was initially reported as a Ras-induced gene during senescence and has been implicated as either an oncogenic factor or tumor suppressor, depending on tumor types. It is unknown if TMEM158 expression is altered in prostate cancers. Multiple public gene expression datasets from RNA-seq and cDNA microarray assays were utilized to analyze candidate gene expression profiles. TMEM158 protein expression was assessed using an immunohistochemistry approach on a tissue section array from benign and malignant prostate tissues. Comparisons of gene expression profiles were conducted using the bioinformatics software R package. COX regression-based screening identified the membrane protein TMEM158 gene as negatively associated with disease-specific and progression-free survival in prostate cancer patients. Gene expression at the mRNA and protein levels revealed that TMEM158 expression was significantly reduced in malignant tissues compared to benign compartments. Meanwhile, TMEM158 downregulation was strongly correlated with advanced clinicopathological features, including late-stage diseases, lymph node invasion, higher PSA levels, residual tumors after surgery, and adverse Gleason scores. In castration-resistant prostate cancers, TMEM158 expression was negatively correlated with AR signaling activity but positively correlated with neuroendocrinal progression index. Consistently, in cell culture models, androgen treatment reduced TMEM158 expression, while androgen deprivation led to upregulation of TMEM158 expression. Correlation analysis showed a tight correlation of TMEM158 expression with the level of R-Ras gene expression, which was also significantly downregulated in prostate cancers. Tumor immune infiltration profiling analysis discovered a strong association of TMEM158 expression with NK cell and Mast cell enrichment. The membrane protein TMEM158 is significantly downregulated in prostate cancer and is tightly associated with disease progression, anti-tumor immune infiltration, and patient survival outcome.