Atlas Dataset Research Articles

Multiomics combined with single-cell analysis shows that mitophagy-related genes could accurately predict the prognosis of patients with clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that accounts for a large proportion of kidney cancer, It is prone to recurrence and metastasis, and has a high mortality rate. Although mitophagy is important for metastasis and the recurrence of various tumors, its effect on renal clear cell carcinoma is poorly understood. Mitophagy-related genes were obtained through the GeneCards database. We normalised the data from different sources by removing the batch effect. Next, we conducted a preliminary screening of mitophagy-related genes and obtained prognosis-related genes from differentially expressed genes. We constructed a prognostic model using least absolute shrinkage and selection operator (LASSO) regression with data from The Cancer Genome Atlas (TCGA) and GSE29609 datasets and validated it internally. International Cancer Genome Consortium (ICGC) and E-MTAB-1980 cohorts also provided double external validation. In addition, we combined multi-omics and single-cell data to comprehensively analyse mitophagy-related gene model signature (MRGMS). Combined with the mitophagy-related gene model (MRGM) score, we constructed a nomogram. Finally, we performed pathway enrichment analysis using a variety of methods. Multiomics and single-cell data analysis showed that the MRGMS is important for patients with ccRCC and is expected to become a new biomarker. The construction of a nomogram was conducive to accurately predicting patient survival. Mitophagy-related genes are important for predicting the prognosis of ccRCC and are conducive to the development of more personalised treatment plans for patients.

Mitochondrial Pyruvate Carrier 1 as a Novel Prognostic Biomarker in Non-Small Cell Lung Cancer.

Abnormal mitochondrial pyruvate carrier 1 (MPC1) expression plays a key role in tumor metabolic reprogramming and progression. Understanding its significance in non-small cell lung cancer (NSCLC) is crucial for identifying therapeutic targets. TIMER 2.0 was utilized to assess the expression of MPC1 in both normal and cancer tissues in pan-cancer. Overall survival (OS) differences between high and low MPC1 expression were analyzed in NSCLC using the Cancer Genome Atlas (TCGA) datasets. We also examined the expression of MPC1 in NSCLC cell lines using western blotting and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). In addition, the tissue samples and clinical information of 80 patients with NSCLC from our hospital were collected. Immunohistochemistry (IHC) was used to assess MPC1 expression, and OS was evaluated using Kaplan-Meier curves and the log-rank test. Univariate and multivariate Cox regression analyses were conducted to evaluate the prognostic values of the clinical characteristics and MPC1expression. Analysis of public databases suggested that MPC1 was downregulated in NSCLC compared to that in normal lung tissue and predicted poor prognosis. In addition, the expression of MPC1 in NSCLC cell lines was lower than that in human bronchial epithelial (HBE) cells at both protein and mRNA levels. Further clinical analysis suggested that MPC1 expression was correlated with age, tumor T stage, and TNM stage. Kaplan-Meier analysis revealed that NSCLC patients with high MPC1 expression had a better prognosis, particularly in lung adenocarcinoma (LUAD), whereas no survival benefit was observed in lung squamous cell carcinoma (LUSC). Univariate and multivariate analyses suggested that MPC1 was an independent prognostic factor for patients with NSCLC. MPC1 is poorly expressed in NSCLC, particularly in LUAD, which predicts a poor prognosis and may serve as an independent prognostic factor. Further studies on MPC1 may reveal new targets for the treatment of NSCLC.

Occurrence of NECTIN4 amplification in solid tumors and enfortumab vedotin response in metastatic urothelial cancer.

673 Background: The anti-NECTIN4 antibody-drug conjugate (ADC) enfortumab vedotin (EV) is approved for patients with metastatic urothelial cancer (mUC). However, durable benefit is only achieved in a small, yet uncharacterized patient subset. NECTIN4 is located on chromosome 1q23.3, and 1q23.3 gains are frequent genomic events in mUC leading to NECTIN4 amplifications. Here, we aimed to evaluate the potential of NECTIN4 amplification as a genomic biomarker to predict EV response in patients with mUC. Methods: We established a NECTIN4-specific fluorescence in-situ hybridization (FISH) assay to assess NECTIN4 copy number variations (CNVs) in a multicenter EV-treated mUC patient cohort (UC-EV, N=77), and CNV data were correlated with membranous NECTIN4 protein expression (H-score) assessed via immunohistochemistry, EV treatment responses and outcomes. Next, we conducted a pan-cancer analysis of the The Cancer Genome Atlas (TCGA) datasets, comprising 10,712 patients across 32 cancer types, to investigate the relationship between NECTIN4 CNV, mRNA expression (RNAseq) and protein levels (RPPA) across entities. Results: In TCGA cohorts, NECTIN4 amplification occurs frequently across different solid cancer types, especially in 15-20% of bladder cancers (17% in TCGA-BLCA), as well as 5-10% in breast cancer (TCGA-BRCA) and lung adenocarcinoma (TCGA-LUAD). We confirmed the amplification frequency in our UC-EV cohort (18%). NECTIN4 amplification is significantly associated with both increased NECTIN4 mRNA expression (e.g., TCGA-BLCA, BRCA, LUAD) and membranous NECTIN4 protein expression (UC-EV), and represents a stable genomic alteration during metastatic progression. In the UC-EV cohort, all patients with NECTIN4 amplification (N=14) responded to EV. In multivariable Cox adjusted for age and sex, NECTIN4 amplifications led to a 93% risk reduction for death compared to a NECTIN4non-amplified status (HR=0.07, 95%-CI 0.01–0.53; P<0.001). Conclusions: Our study highlights the value of NECTIN4 amplifications to predict EV responses in patients with mUC. NECTIN4 amplifications occur frequently in different cancer types and therefore have the potential to be a novel tumor-agnostic genomic biomarker that enables tailored NECTIN4-targeted therapies in various entities.

Chronic stress promotes gastric cancer progression via the adrenoceptor beta 2/PlexinA1 pathway.

Chronic stress is a common emotional disorder in cancer patients. Chronic stress promotes progression of gastric cancer (GC) and leads to poor outcomes. However, the underlying mechanisms remain not clear. Herein, we explored the possible mechanisms of chronic stress in GC progression. The Cancer Genome Atlas (TCGA) datasets were analyzed for differentially expressed genes. Clinical data of GC were evaluated for their association with PlexinA1 using TCGAand Kaplan-Meier-plotter databases. Chronic stress of GC patients was evaluated using the Self-Rating Anxiety Scale and Self-Rating Depression Scale. Chronic unpredictable mild stress (CUMS) was used to induce chronic stress in mice. Gastric xenograft tumor was constructed using the sewing method. Chronic stress-like behaviors were assessed using light/dark box and tail suspension tests. Protein expression was detected using immunohistochemistry and Western blot analysis. Analyses of TCGA and the Kaplan-Meier-plotter databases showed that patients with high levels of PlexinA1 in GC had worse overall survival than those with low levels of PlexinA1. A total of 36 GC patients were enrolled in the study, and about 33% of the patients had chronic stress. Compared with patients without chronic stress, higher expression levels of adrenoceptor beta 2 and PlexinA1 were observed in patients with chronic stress. The tumor size in mice under CUMS was significantly increased compared with the control mice. Adrenoceptor beta 2, PlexinA1, N-cadherin, and alpha-smooth muscle actin, as well as Ki67 were highly expressed in the tumors of CUMS group. However, E-cadherin was lowly expressed in the tumors of CUMS group. Importantly, chemical sympathectomy with 6-hydroxydopamineor treatment with a selective β2 adrenergic receptor antagonist (ICI118,551) could reverse these effects. Our findings suggest that chronic stress plays an important role in GC progression and there is a potential for blocking the epinephrine-β2AR/PlexinA1 pathway in the treatment of GC.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Identification of SRSF10 as a promising prognostic biomarker with functional significance among SRSFs for glioma

AimsThe serine/arginine-rich splicing factor (SRSF) protein family members are essential mediators of the alternative splicing (AS) regulatory network, which is tightly implicated in cancer progression. However, the expression, clinical correlation, immune infiltration, and prognostic value of SRSFs in gliomas remain unclear. Materials and methodsGlioma samples were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Several databases, such as HPA, DAVID, UALCAN were used to comprehensively explore the roles of SRSFs. In addition, experimental validation of SRSF10 was also conducted. Key findingsHere, we found the expression alterations of the SRSF family in glioma samples using data from the TCGA and CGGA_325 datasets. Among the 12 genes, most were found to be closely associated with glioma clinical features, which linked to poor prognosis in glioma patients. Interestingly, survival analysis identified only SRSF10 as a potential independent risk prognostic biomarker for glioma patients. Immune analysis indicated that glioma patients with high SRSF10 expression may respond well to immunotherapies targeting immune checkpoint (ICP) genes. Finally, knocking down SRSF10 reduced glioma cell viability, induced G1 cell cycle arrest, and induced the exclusion of bcl-2-associated transcription factor 1 (BCLAF1) exon 5a. SignificanceOverall, this study uncovers the oncogenic roles of most SRSF family members in glioma, with the exception of SRSF5, while highlighting SRSF10 as a potential novel independent prognostic biomarker for glioma.

Identification of consensus head and neck cancer-associated microbiota signatures: a systematic review and meta-analysis of 16S rRNA and The Cancer Microbiome Atlas datasets.

Introduction. Multiple reports have attempted to describe the tumour microbiota in head and neck cancer (HNSC).Gap statement. However, these have failed to produce a consistent microbiota signature, which may undermine understanding the importance of bacterial-mediated effects in HNSC.Aim. The aim of this study is to consolidate these datasets and identify a consensus microbiota signature in HNSC.Methodology. We analysed 12 published HNSC 16S rRNA microbial datasets collected from cancer, cancer-adjacent and non-cancer tissues to generate a consensus microbiota signature. These signatures were then validated using The Cancer Microbiome Atlas (TCMA) database and correlated with the tumour microenvironment phenotypes and patient's clinical outcome.Results. We identified a consensus microbial signature at the genus level to differentiate between HNSC sample types, with cancer and cancer-adjacent tissues sharing more similarity than non-cancer tissues. Univariate analysis on 16S rRNA datasets identified significant differences in the abundance of 34 bacterial genera among the tissue types. Paired cancer and cancer-adjacent tissue analyses in 16S rRNA and TCMA datasets identified increased abundance in Fusobacterium in cancer tissues and decreased abundance of Atopobium, Rothia and Actinomyces in cancer-adjacent tissues. Furthermore, these bacteria were associated with different tumour microenvironment phenotypes. Notably, high Fusobacterium signature was associated with high neutrophil (r=0.37, P<0.0001), angiogenesis (r=0.38, P<0.0001) and granulocyte signatures (r=0.38, P<0.0001) and better overall patient survival [continuous: HR 0.8482, 95 % confidence interval (CI) 0.7758-0.9273, P=0.0003].Conclusion. Our meta-analysis demonstrates a consensus microbiota signature for HNSC, highlighting its potential importance in this disease.

The macrophage-associated prognostic gene ANXA5 promotes immunotherapy resistance in gastric cancer through angiogenesis

Gastric cancer (GC) remains a predominant form of malignant tumor globally, necessitating innovative non-surgical therapeutic approaches. This investigation aimed to delineate the expression landscape of macrophage-associated genes in GC and to evaluate their prognostic significance and influence on immunotherapeutic responsiveness. Utilizing the CellMarker2.0 database, we identified 69 immune cell markers with prognostic relevance in GC, including 12 macrophage-specific genes. A Weighted Gene Co-Expression Network Analysis (WGCNA) isolated 3,181 genes correlated with these macrophage markers. The Cancer Genome Atlas (TCGA-STAD) dataset was employed as the training set, while data from the GSE62254 served as the validation cohort. 13 genes were shortlisted through LASSO-Cox regression to formulate a prognostic model. Multivariable Cox regression substantiated that the calculated risk score serves as an imperative independent predictor of overall survival (OS). Distinct macrophage infiltration profiles, pathway associations, treatment susceptibilities, and drug sensitivities were observed between high- and low-risk groups. The preliminary validation of ANXA5 in predicting the survival rates of GC patients at 1 year, 3 years, and 5 years, as well as its expression levels were higher and role in promoting tumor angiogenesis in GC through immunohistochemistry and angiogenesis experiments. In summary, macrophage-related genes were potentially a novel crosstalk mechanism between macrophages and endothelial cells in the tumor microenvironment, and the interplay between inflammation and angiogenesis might have also offered new therapeutic targets, providing a new avenue for personalized treatment interventions.

Fatty acid binding protein 5 is a novel therapeutic target for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive subtype of liver cancer and is one of the most common cancers with high mortality worldwide. Reprogrammed lipid metabolism plays crucial roles in HCC cancer cell survival, growth, and evolution. Emerging evidence suggests the importance of fatty acid binding proteins (FABPs) in contribution to cancer progression and metastasis; however, how these FABPs are dysregulated in cancer cells, especially in HCC, and the roles of FABPs in cancer progression have not been well defined. To understand the genetic alterations and expression of FABPs and their associated cancer hallmarks and oncogenes in contributing to cancer malignancies. We used The Cancer Genome Atlas datasets of pan cancer and liver hepatocellular carcinoma (LIHC) as well as patient cohorts with other cancer types in this study. We investigated genetic alterations of FABPs in various cancer types. mRNA expression was used to determine if FABPs are abnormally expressed in tumor tissues compared to non-tumor controls and to investigate whether their expression correlates with patient clinical outcome, enriched cancer hallmarks and oncogenes previously reported for patients with HCC. We determined the protein levels of FABP5 and its correlated genes in two HCC cell lines and assessed the potential of FABP5 inhibition in treating HCC cells. We discovered that a gene cluster including five FABP family members (FABP4, FABP5, FABP8, FABP9 and FABP12) is frequently co-amplified in cancer. Amplification, in fact, is the most common genetic alteration for FABPs, leading to overexpression of FABPs. FABP5 showed the greatest differential mRNA expression comparing tumor with non-tumor tissues. High FABP5 expression correlates well with worse patient outcomes (P < 0.05). FABP5 expression highly correlates with enrichment of G2M checkpoint (r = 0.33, P = 1.1e-10), TP53 signaling pathway (r = 0.22, P = 1.7e-5) and many genes in the gene sets such as CDK1 (r = 0.56, P = 0), CDK4 (r = 0.49, P = 0), and TP53 (r = 0.22, P = 1.6e-5). Furthermore, FABP5 also correlates well with two co-expressed oncogenes PLK1 and BIRC5 in pan cancer especially in LIHC patients (r = 0.58, P = 0; r = 0.58, P = 0; respectively). FABP5high Huh7 cells also expressed higher protein levels of p53, BIRC5, CDK1, CDK2, and CDK4 than FABP5low HepG2 cells. FABP5 inhibition more potently inhibited the tumor cell growth in Huh7 cells than in HepG2 cells. We discovered that FABP5 gene is frequently amplified in cancer, especially in HCC, leading to its significant elevated expression in HCC. Its high expression correlates well with worse patient outcome, enriched cancer hallmarks and oncogenes in HCC. FABP5 inhibition impaired the cell viability of FABP5high Huh7 cells. All these support that FABP5 is a novel therapeutic target for treating FABP5high HCC.

Expression heterogeneity, tumor immune characteristics and the prognosis effects of OPRL1 in patients with tumors: a pan-cancer study combined with bioinformation analyses and in vitro validation

PurposeOpioids are currently the most frequently prescribed analgesics in clinical practice. However, their effect on cancer progression remains a topic of debate. Opioid receptors (ORs) are present in various types of tumor cells and their expression levels vary depending on the type of tumor. This study aims to explore and preliminarily characterize the association between four different ORs (μ, δ, κ, and nociception/orphanin FQ peptide receptor) and the prognosis of different types of tumors for comparison, with a focus on nociception/ orphanin FQ peptide receptor.MethodsThe expression levels of four ORs in normal tissues and immune cells were obtained from Human Protein Atlas (HPA) RNA-seq dataset, Monaco dataset, and Consensus dataset. Pan-cancer analysis was performed using the The Cancer Genome Atlas (TCGA) dataset, which included the expression of four ORs in different cancer types, significant copy-number alterations (sCNA), gene mutations of the four ORs, survival analysis, co-expression genes analysis, functional enrichment analyses, and correlations between ORs and immune cell infiltration levels. Based on the results of bioinformatic analysis, we selected 10 cancer cell lines for validation in vitro using specific agonists for the four ORs.ResultsOPRL1 (opioid related nociceptin receptor 1 gene) exhibited the highest abundance across different types of cancers, while OPRM1 (opioid receptor mu 1 gene) and OPRD1 (opioid receptor delta 1 gene) were barely detectable in multiple cancer types. Pan-cancer survival analysis revealed the overall worse/better prognosis of the four ORs in certain cancer types. Elevated levels of OPRM1 appear to be associated with poorer outcomes in breast invasive carcinoma and kidney renal clear cell carcinoma. Elevated OPRD1 levels are connected to worsen outcomes in kidney renal clear cell carcinoma and liver hepatocellular carcinoma, but better prognosis in bladder urothelial carcinoma. Increased OPRK1 (opioid receptor kappa 1 gene) expression is linked to a poorer prognosis in kidney renal papillary cell carcinoma. Furthermore, high OPRL1 expression relates to worse outcomes in bladder urothelial carcinoma and liver hepatocellular carcinoma, but better outcomes in breast invasive carcinoma and pancreatic adenocarcinoma. Functional enrichment analyses found that immune-related pathways were enriched in OPRK1 and OPRL1, with OPRL1 exhibiting the highest correlation with immune cell infiltration. Different effects on cell growth, migration, and invasion were observed in different cancer types upon the administration of agonists for the four ORs.ConclusionOPRL1 may play a vital role in monocytes and regulating the immune response and tumor-infiltrating macrophages. Due to its high abundance in different types of tumors, it may hold greater clinical significance for oncology patients. OPRK1 also participates in immune-related pathways. OPRL1 could potentially serve as therapeutic targets for different types of cancers.Graphical

TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

Deep Learning-Based Identification of Tissue of Origin for Carcinomas of Unknown Primary Using MicroRNA Expression: Algorithm Development and Validation.

Carcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation. This study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers. We used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations. Our results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work. Our findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.

Prognostic Significance of Glycolysis-Related Genes in Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC) is one of the most common malignancies. There is growing evidence that glycolysis-related genes play a critical role in tumor development, maintenance, and therapeutic response by altering tumor metabolism and thereby influencing the tumor immune microenvironment. However, the overall impact of glycolysis-related genes on the prognostic significance, tumor microenvironment characteristics, and treatment outcome of patients with LUSC has not been fully elucidated. We used The Cancer Genome Atlas (TCGA) dataset to screen glycolysis-related genes with prognostic effects in LUSC and constructed signature and nomogram models using Lasso and Cox regression, respectively. In addition, we analyzed the immune infiltration and tumor mutation load of the genes in the models. We finally obtained a total of glycolysis-associated DEGs. The signature model and nomogram model had good prognostic power for LUSC. Gene expression in the models was highly correlated with multiple immune cells in LUSC. Through this analysis, we have identified and validated for the first time that glycolysis-related genes are highly associated with the development of LUSC. In addition, we constructed the signature model and nomogram model for clinical decision-making.

Novel quantitative immunohistochemistry method using histone H3, family 3B as the internal reference standard for measuring human epidermal growth factor receptor 2 expression in breast cancer.

Immunohistochemistry (IHC) is an essential technique in surgical and clinical pathology for detecting diagnostic, prognostic, and predictive biomarkers for personalized cancer therapy. However, the lack of standardization and reference controls results in poor reproducibility, and a reliable tool for IHC quantification is urgently required. The objective of this study was to describe a novel approach in which H3F3B (histone H3, family 3B) can be used as an internal reference standard to quantify protein expression levels using IHC. The authors enrolled 89 patients who had human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). They used a novel IHC-based assay to measure protein expression using H3F3B as the internal reference standard. H3F3B was uniformly expressed at the protein level in all tumor regions in cancer tissues. HER2 expression levels were measured with the H-score using HALO software. Kaplan-Meier analysis indicated that, among patients who had HER2-positive BC in The Cancer Genome Atlas data set and the authors' data set, the subgroup with low HER2 expression had a significantly better prognosis than the subgroup with high HER2 expression. Furthermore, the authors observed that HER2 expression levels were precisely evaluated using the proposed method, which can classify patients who are at higher risk of HER2-positive BC to receive trastuzumab-based adjuvant therapy. Dual-color IHC with H3F3B is an excellent tool for internal and external quality control of HER2 expression assays. The proposed IHC-based quantification method accurately assesses HER2 expression levels and provides insights for predicting clinical prognosis in patients with HER2-positive BC who receive trastuzumab-based adjuvant therapy.

Identification of a new gene signature for prognostic evaluation in cervical cancer: based on cuproptosis-associated angiogenesis and multi-omics analysis

Patients with recurrent or metastatic cervical cancer are in urgent need of novel prognosis assessment or treatment approaches. In this study, a novel prognostic gene signature was discovered by utilizing cuproptosis-related angiogenesis (CuRA) gene scores obtained through weighted gene co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. To enhance its reliability, the gene signature was refined by integrating supplementary clinical variables and subjected to cross-validation. Meanwhile, the activation of the VEGF pathway was inferred from an analysis of cell-to-cell communication, based on the expression of ligands and receptors in cell transcriptomic datasets. High-CuRA patients had less infiltration of CD8 + T cells and reduced expression of most of immune checkpoint genes, which indicated greater difficulty in immunotherapy. Lower IC50 values of imatinib, pazopanib, and sorafenib in the high-CuRA group revealed the potential value of these drugs. Finally, we verified an independent prognostic gene SFT2D1 was highly expressed in cervical cancer and positively correlated with the microvascular density. Knockdown of SFT2D1 significantly inhibited ability of the proliferation, migration, and invasive in cervical cancer cells. CuRA gene signature provided valuable insights into the prediction of prognosis and immune microenvironment of cervical cancer, which could help develop new strategies for individualized precision therapy for cervical cancer patients.

Glucocorticoid and Adrenergic Receptor Distribution Across Human Organs and Tissues: A Map for Stress Transduction.

Psychosocial stress is transduced into disease risk through energy-dependent release of hormones from the hypothalamic-pituitary-adrenal and sympathetic-adrenal-medullary axes. The levels of glucocorticoid and adrenergic hormones, together with the sensitivity of tissues to their signaling, define stress responses. To understand existing pathways responsible for the psychobiological transduction of stressful experiences, we provide a quantitative whole-body map of glucocorticoid and adrenergic receptor (AR) expression. We systematically examined gene expression levels for the glucocorticoid receptor (GR), α- and β-ARs (AR-α1B, AR-α2B AR-β2, and AR-β3), across 55 different organs using the Human Protein Atlas and Human Proteome Map datasets. Given that mitochondria produce the energy required to respond to stress, we leveraged the Human Protein Atlas and MitoCarta3.0 data to examine the link between stress hormone receptor density and mitochondrial gene expression. Finally, we tested the functional interplay between GR activation and AR expression in human fibroblast cells. The GR was expressed ubiquitously across all investigated organ systems, whereas AR subtypes showed lower and more localized expression patterns. Receptor co-regulation, meaning the correlated gene expression of multiple stress hormone receptors, was found between GR and AR-α1B, as well as between AR-α1B and AR-α2B. In cultured human fibroblasts, activating the GR selectively increased AR-β2 and AR-α1B expression. Consistent with the known energetic cost of stress responses, GR and AR expressions were positively associated with the expression of specific mitochondrial pathways. Our results provide a cartography of GR and AR expression across the human body. Because stress-induced GR and AR signaling triggers energetically expensive cellular pathways involving energy-transforming mitochondria, the tissue-specific expression and co-expression patterns of hormone receptor subtypes may in part determine the resilience or vulnerability of different organ systems.

Catalase expression is an independent prognostic marker in liver hepatocellular carcinoma

Abstract Objectives Liver hepatocellular carcinoma (LIHC) is the most common type of primary liver cancer and originates from hepatocytes, the main functional cells of the liver. It is a serious and aggressive cancer with a generally poor prognosis, especially when diagnosed at advanced stages. Reactive oxygen species (ROS) have been detected in LIHC and are involved in carcinogenesis and tumor progression. Here, a comprehensive analysis was performed to evaluate the effects of ROS-related genes on the prognosis of LIHC. Methods Using bioinformatical tools including Gene Expression Profiling Interactive Analysis (GEPIA2) and Q-omics, a comprehensive analysis was performed to evaluate the effects of ROS-related genes, including superoxide dismutases (SODs), glutathione peroxidases (GPXs), peroxiredoxins (PRDXs) and catalase (CAT) on the prognosis of LIHC using The Cancer Genome Atlas (TCGA) dataset and identified the most appropriate candidate genes. Then we further explored their effects on LIHC cell proliferation and drug selection for LIHC treatment. Results We found that CAT expression was significantly downregulated in late stage’s LIHC tissues compared to normal liver or early stage’s LIHC tissues, and high CAT expression was correlated with a favorable survival prognosis in LIHC. The expression of the CAT gene was associated with an inhibition of the “cell cycle” pathway. HepG2 and Hep3B cells’ growth was increased with a decrease in CAT expression by silencing its mRNA. As silencing of CAT in HepG2 and Hep3B cells, and its association with an increase in the expression of PLK1, CCNB1, CDC20, and PTTG1. A comparative 426 drug response in LIHC cells with different CAT expression, SU11274, a Met inhibitor, could serve as a therapeutic option when CAT levels are low in LIHC cells. Conclusions Our findings revealed that Met inhibitors could potentially control tumor progression and be used as a therapeutic option against LIHC with low CAT.

ITGB4 upregulation is associated with progression of lower grade glioma

Gliomas originating in the neuroepithelium account for about 80% of brain malignancies and are the most common cancer of the central nervous system. Clinical management of gliomas remains challenging despite significant advances in comprehensive therapies, including radiotherapy, chemotherapy, and surgery. The ITGB4 (Integrin subunit beta 4) gene encodes a receptor for laminins and its upregulation in tumor tissues is associated with poor prognosis. However, its role in glioma is not well understood. First, we performed a pan cancer analysis of ITGB4 expression in The Cancer Genome Atlas (TCGA) dataset. Survival analysis was done on Chinese Glioma Genome Atlas (CGGA) and TCGA. Immunohistochemistry was then used to validate the expression and role of ITGB4 in glioma. We finally analyzed the possible mechanism by immune infiltration and single-cell sequencing analysis. Here, we found that ITGB4 is upregulated in glioma and accurately predicts the prognosis of lower grade glioma (LGG). Univariate and multivariate Cox regression analyses showed that ITGB4 is a risk factor for LGG. Immunohistochemical analysis confirmed that ITGB4 accurately predicts LGG prognosis. Non-negative matrix factorization (NMF) cluster analysis showed that ITGB4 was closely related to immune related genes. Immune cell infiltration and single cell sequencing analyses indicated that ITGB4 may be closely related to the microenvironment of gliomas, especially tumor-associated fibroblasts. ITGB4 is a promising diagnostic and therapeutic factor in LGG patients.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer.

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS. The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform. We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression. Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.