Gene Expression Values Research Articles

Predicting patient outcomes with gene-expression biomarkers from colorectal cancer organoids and cell lines

IntroductionColorectal cancer (CRC) is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date, chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for the treatment of metastatic colorectal cancer are 5-fluorouracil, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct a predictive gene expression-based classifier to separate responders and non-responders.MethodsIn this study, we analyzed gene expression profiles of seven patient-derived CRC organoids and performed correlation analyses between gene expression and IC50 values for the three standard-of-care chemotherapeutic drugs. We also included in the study publicly available datasets of colorectal cancer cell lines, thus combining two different in vitro models relevant to cancer research. Logistic regression was used to build gene expression-based classifiers for metastatic Stage IV and non-metastatic Stage II/III CRC patients. Prognostic performance was evaluated through Kaplan-Meier survival analysis and log-rank tests, while independent prognostic significance was assessed using multivariate Cox proportional hazards modeling.ResultsA small set of genes showed consistent correlation with resistance to chemotherapy across different datasets. While some genes were previously implicated in cancer prognosis and drug response, several were linked to drug resistance for the first time. The resulting gene expression signatures successfully stratified Stage II/III and Stage IV CRC patients, with potential clinical utility for improving treatment outcomes after further validation.DiscussionThis study highlights the advantages of integrating diverse experimental models, such as organoids and cell lines, to identify novel prognostic biomarkers and enhance the understanding of chemotherapy resistance in CRC.

Efficacy outcomes of CDK4/6 inhibitors in combination with endocrine therapy treatment in hormone receptor-positive/HER2-negative advanced breast cancer according to PAM50 intrinsic subtype: Primary results of SOLTI-1801 CDK-PREDICT study.

The prognostic value of PAM50 intrinsic subtypes (IS), cell cycle, and immune-related gene expression in HR+ /HER2- advanced breast cancer (BC) treated with CDK4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) in a first-line metastatic setting is unclear. This study evaluates these biomarkers in metastatic biopsies from patients diagnosed with HR+ /HER2- advanced BC. CDK-PREDICT study is a multicentric, ambispective observational cohort study conducted in six Spanish hospitals. It included patients diagnosed with HR+ /HER2- advanced BC treated in the first-line setting with CDK4/6i and ET. Baseline biopsies were obtained prior to treatment to determine research-based PAM50 IS, cell cycle and immune-related gene expression. The primary objective was to evaluate progression-free survival (PFS) differences among PAM50 IS using uni- and multivariable Cox regression models. Secondary objectives included overall survival (OS), overall response rate (ORR), and correlating cell cycle and immune response gene expression with PFS. A total of 185 patients were included, with a median follow-up of 38.5 months. PAM50 luminal subtypes were predominant (82.7 %). Non-luminal subtypes showed significantly shorter median PFS (10.2 vs. 25.7 months; HR, 2.50; p < 0.001) and OS (32.3 vs. 58.1 months; HR, 2.54; p < 0.001) than luminal subtypes. Higher cell cycle and immune-related genes expression, such as CCNE1 and PDCD1, as well as tumor infiltrating lymphocytes were associated with poorer outcomes. This study confirms the independent prognostic value of PAM50 IS in HR+ /HER2- advanced BC treated with CDK4/6i and ET. Non-luminal subtypes exhibited the worst prognosis, underscoring the need for novel therapeutic strategies in this population.

MicroRNA 29 gene Expression and Progesterone Receptor Values in Iraqi Women with Breast Cancer

MicroRNA 29 gene Expression and Progesterone Receptor Values in Iraqi Women with Breast Cancer

Comprehensive analysis of selenoprotein gene expression and prognostic value in ovarian cancer.

To comprehensively analyze the expression and prognostic value of selenoprotein in ovarian cancer (OV). GEPIA and cBioPortal were used to analyze selenoprotein expression and mutations and copy number variations. Kaplan-Meier plotter and the tumor immune estimation resource were used to evaluate the impact of these genes on clinical prognosis and their correlation with tumor immune infiltration. Compared with normal tissues, the expression of iodothyronine deiodinase 3 (DIO3), glutathione peroxidase 4, SECISBP2, SELM, and SELP was decreased in the four gynecological malignancies. In OV, selenoprotein had the highest number of mutations (309) and mutation frequency (52.91%), whereas the lowest was observed in endometrial cancer (29.72%). DIO3, selenoprotein O (SELO), and selenoprotein T (SELT) are significantly related to the prognosis of OV. Immune infiltration analysis showed that DIO3 was associated with tumor-associated macrophages, SELO with CD4+ T-cells and monocytes, and SELT with T-cells. Enrichment analysis revealed that DIO3 is mainly involved in inflammatory immune responses and the Ras signaling pathway, SELO is primarily related to innate immune responses, and SELT is closely associated with mitochondrial oxidative phosphorylation. This study explored the expression characteristics of 25 selenoprotein in patients with gynecological malignancies and found that DIO3, SELO, and SELT were significantly associated with the prognosis and clinical features of OV, which are potential therapeutic targets.

Association of Metabolic Diseases and Moderate Fat Intake with Myocardial Infarction Risk.

Myocardial infarction (MI) can range from mild to severe cardiovascular events and typically develops through complex interactions between genetic and lifestyle factors. We aimed to understand the genetic predisposition associated with MI through genetic correlation, colocalization analysis, and cells' gene expression values to develop more effective prevention and treatment strategies to reduce its burden. A polygenic risk score (PRS) was employed to estimate the genetic risk for MI and to analyze the dietary interactions with PRS that affect MI risk in adults over 45 years (n = 58,701). Genetic correlation (rg) between MI and metabolic syndrome-related traits was estimated with linkage disequilibrium score regression. Single-cell RNA sequencing (scRNA-seq) analysis was performed to investigate cellular heterogeneity in MI-associated genes. Ten significant genetic variants associated with MI risk were related to cardiac, immune, and brain functions. A high PRS was associated with a threefold increase in MI risk (OR: 3.074, 95% CI: 2.354-4.014, p < 0.001). This increased the risk of MI plus obesity, hyperglycemia, dyslipidemia, and hypertension by about twofold after adjusting for MI-related covariates (p < 0.001). The PRS interacted with moderate fat intake (>15 energy percent), alcohol consumption (<30 g/day), and non-smoking, reducing MI risk in participants with a high PRS. MI was negatively correlated with the consumption of olive oil, sesame oil, and perilla oil used for cooking (rg = -0.364). MI risk was associated with storkhead box 1 (STOX1) and vacuolar protein sorting-associated protein 26A (VPS26A) in atrial and ventricular cardiomyocytes and fibroblasts. This study identified novel genetic variants and gene expression patterns associated with MI risk, influenced by their interaction with fat and alcohol intake, and smoking status. Our findings provide insights for developing personalized prevention and treatment strategies targeting this complex clinical presentation of MI.

SURG-03. GENE EXPRESSION CHANGES ASSOCIATED WITH RECURRENCE AFTER GROSS TOTAL RESECTION OF NEWLY DIAGNOSED WHO GRADE 1 MENINGIOMA

Abstract Patients who undergo gross total resection (GTR) of Central Nervous System World Health Organization (WHO) grade 1 meningioma constitute a “low risk” group, but some low risk meningiomas can recur despite reassuring clinical and histological features. In this study, gene expression in newly diagnosed WHO grade 1 meningiomas that had undergone GTR were evaluated for their association with recurrence. This was a retrospective, international, multi-center cohort study that included WHO grade 1 meningiomas that underwent GTR as first treatment. Normalized gene expression values from a previously validated 34-gene panel were evaluated for their association with recurrence. Kaplan-Meier, multivariable Cox proportional hazard analyses, and K-means clustering were performed to assess the association of genes of interest with recurrence and identify molecular subgroups among clinically and histologically low risk meningiomas. 442 patients with WHO grade 1 meningiomas that underwent GTR and had available gene expression profiling data were included in the study. Median follow-up was 5.0 years (interquartile range 2.6-7.7 years), local recurrence occurred in 36 patients (8.1%), 5-year local freedom from recurrence (LFFR) was 90.5%, and median time to recurrence of 2.9 years (range 0.5-10.7 years). Eleven genes were associated with local recurrence, including lower expression of ARID1B, ESR1, LINC02593, PGR, and TMEM30B and higher expression of CDK6, CDKN2C, CKS2, KIF20A, PGK1, and TAGLN. K-means clustering based on these 11 genes distinguished 2 molecular groups of clinically and histologically low risk meningiomas with significant differences in LFFR (HR 2.5, 95% CI 1.2-5.1, p=0.016). In conclusion, gene expression profiling may help to identify newly diagnosed WHO grade 1 meningiomas that have an elevated risk of recurrence despite GTR.

Auxiliary diagnosis of primary bone tumors based on Machine learning model

ObjectiveResearch on auxiliary diagnosis of primary bone tumors can enhance diagnostic accuracy, facilitate early detection, and enable personalized treatment, thereby reducing misdiagnosis and missed cases, ultimately leading to improved patient prognosis and survival rates. In this study, we established a whole slide imaging (WSI) database comprising histopathological samples from all categories of bone tumors and integrated multiple neural network architectures for machine learning models. We then evaluated the accuracy of these models in diagnosing primary bone tumors. MethodsIn this paper, the machine learning model based on the deep convolutional neural network (DC-NN) method was combined with imaging omics analysis to analyze and discuss its clinical value in diagnosing primary bone tumors. In addition, this paper proposed a screening method for differentially expressed genes. Based on the paired T-test method, the process first estimated the tumor purity in the experimental data of each sample case, then assessed the actual gene expression value of the experimental data of each sample case, and finally calculated the optimized paired T-test statistics, and screened differentially expressed genes according to the threshold value. ResultsThe selected model demonstrated excellent diagnostic accuracy in distinguishing between normal and tumor images, with overall accuracy of (99.8 ± 0.4) % for five rounds of testing using the DCNN model and positive and negative predictive values of (100.0 ± 0.0) % and (99.6 ± 0.8) %, respectively. The mean area under each dataset’s curve (AUC) was (0.998 ± 0.004). Further, ten rounds of testing using the DCNN model showed an overall accuracy of (71.2 ± 1.6) % and a substantial positive predictive value of (91.9 ± 8.5) % in distinguishing benign from malignant bone tumors, with an average AUC of (0.62 ± 0.06) across datasets. ConclusionThe deep learning model accurately classifies bone tumor histopathology based on the degree of infiltration, achieving diagnostic performance comparable to that of senior pathologists. These findings affirm the feasibility and effectiveness of histopathological diagnosis in bone tumors, providing a theoretical foundation for the application and advancement of machine learning-assisted histopathological diagnosis in this field.

Defining the optimal setting for transcriptomic analyses on blood samples for response prediction in immunotherapy-treated NSCLC patients

Transcriptomic profiling of blood immune cells offers a promising alternative to invasive, sampling bias-prone tissue-based biomarker assays for predicting immune checkpoint inhibitor (ICI) therapy response in non-small cell lung cancer (NSCLC) patients. However, the optimal analytical approach to identify systemic correlates of response still needs to be explored. We collected peripheral blood mononuclear cells and whole blood (WB) samples from 33 ICI-treated NSCLC patients before ICI treatment and at the first response evaluation. After bulk polyadenylated RNA-sequencing, we assessed differences in gene expression profiles between non-responders and responders using differential expression analysis, single sample gene set enrichment analysis (ssGSEA), and cell type deconvolution. We evaluated gene expression values, ssGSEA scores, and deconvolved cell type proportions to distinguish non-responders from responders via ROC curve (AUC) analysis, training a logistic regression classification model. Gene expression values and deconvolved proportions yielded the best results with WB samples after treatment (AUC = 0.87 and 0.85, respectively). Overall, ssGSEA scores showed superior classification performance across all sample types and timepoints (AUC > 0.7). In conclusion, transcriptomic analysis through ssGSEA demonstrated the best performance as a non-invasive biomarker for predicting clinical benefit in ICI-treated NSCLC patients, with gene expression and deconvolution on post-treatment WB samples also showing promising results.

Collapsible tree: interactive web app to present collapsible hierarchies.

A crucial component of intuitive data visualization is presenting a hierarchical tree structure with interactive functions. For example, single-cell transcriptomics studies may generate gene expression values with developmental trajectories or cell lineage structures. Two common visualization methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), require two separate figures to depict the distribution of cell types and gene expression data, with low-dimension projections that may not capture the hierarchical structures among cells. Here, we present a JavaScript framework and an interactive web app named Collapsible Tree, which presents values jointly with interactive, expandable, and collapsible lineage structures. For example, the Collapsible Tree presents cellular states and gene expression from single-cell transcriptomics within a single hierarchical plot, enabling comparisons of gene expression across lineages and subtle patterns between sub-lineages. Our framework can facilitate the exploration of complicated value patterns that are not evident in UMAP or t-SNE plots. The Collapsible Tree web interface is available at https://collapsibletree.data2in.net. The JavaScript library source code is available at https://github.com/data2intelligence/collapsible_tree.

LMD: Cluster-Independent Multiscale Marker Identification in Single-cell RNA-seq Data.

Identifying accurate cell markers in single-cell RNA-seq data is crucial for understanding cellular diversity and function. Localized Marker Detector (LMD) is a novel tool to identify "localized genes" - genes exclusively expressed in groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD constructs a cell-cell affinity graph, diffuses the gene expression value across the cell graph, and assigns a score to each gene based on its diffusion dynamics. LMD's candidate markers can be grouped into functional gene modules, which accurately reflect cell types, subtypes, and other sources of variation such as cell cycle status. We apply LMD to mouse bone marrow and hair follicle dermal condensate datasets, where LMD facilitates cross-sample comparisons, identifying shared and sample-specific gene signatures and novel cell populations without requiring batch effect correction or integration methods. Furthermore, we assessed the performance of LMD across nine single-cell RNA sequencing datasets, compared it with six other methods aimed at achieving similar objectives, and found that LMD outperforms the other methods evaluated.

Kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq.

The term 'RNA-seq' refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, single cells or single nuclei. The kallisto, bustools and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data. Execution of this protocol requires basic familiarity with a command line environment. With this protocol, quantification of a moderately sized RNA-seq dataset can be completed within minutes.

Incomplete filling in the basal region of maize endosperm: timing of development of starch synthesis and cell vitality.

Starch synthesis in maize endosperm adheres to the basipetal sequence from the apex downwards. However, the mechanism underlying nonuniformity among regions of the endosperm in starch accumulation and its significance is poorly understood. Here, we examined the spatiotemporal transcriptomes and starch accumulation dynamics in apical (AE), middle (ME), and basal (BE) regions of endosperm throughout the filling stage. Results demonstrated that the BE had lower levels of gene transcripts and enzymes facilitating starch synthesis, corresponding to incomplete starch storage at maturity, compared with AE and ME. Contrarily, the BE showed abundant gene expression for genetic processing and slow progress in physiological development (quantified by an index calculated from the expression values of development progress marker genes), revealing a sustained cell vitality of the BE. Further analysis demonstrated a significant parabolic correlation between starch synthesis and physiological development. An in-depth examination showed that the BE had more active signaling pathways of IAA and ABA than the AE throughout the filling stage, while ethylene showed the opposite pattern. Besides, SNF1-related protein kinase1 (SnRK1) activity, a regulator for starch synthesis modulated by trehalose-6-phosphate (T6P) signaling, was kept at a lower level in the BE than the AE and ME, corresponding to the distinct gene expression in the T6P pathway in starch synthesis regulation. Collectively, the findings support an improved understanding of the timing of starch synthesis and cell vitality in regions of the endosperm during development, and potential regulation from hormone signaling and T6P/SnRK1 signaling.

Evaluation of Biocides Susceptibility and Expression of Qace-Δ1 And Suge1 Genes of Pseudomonas Aeruginosa in Exposure to Biocides and Meropenem

Pseudomonas aeruginosa is a species of medical significance that plays a role in nosocomial infections, particularly those resulting from burns and wounds. Bacterial antibiotic resistance may have developed as a result of the abuse of biocides in hospitals. This study aimed to determine the minimum inhibitory concentrations (MICs) of carbapenem-resistant P. aeruginosa isolates in relation to ethylenediaminetetraacetic acid (EDTA), chlorohexidine (CHX), and resazurin dye using a microtiter plate assay. Additionally, it evaluated the expression of the efflux pump genes (qacE-Δ1 and sugE1) at sub-inhibitory concentrations of the biocides using RT-qPCR (delta delta Ct method) to improve their efflux pump system. The MICs of 24 P. aeruginosa isolates (14 isolates were resist to carbapenems and 10 isolates sensitive to carbapenems) against Chloroxylenol (Dettol) revealed that the range of MICs were (0. 125-2%). The results of MICs of Chlorohexidine (CHX) revealed that the range of inhibitory activity was (0.8-102.4 µg/ml), also the findings of MICs of EDTA demonstrated that the range of inhibitory activity was (125-2000 µg/ml). The genes involved in efflux pump (qacE -Δ1 and sugE1) were found to be significantly up-regulated in the local isolates, according to our findings.. The four isolates' sugE1 gene expression folds ranged from 1.81 to 5.39; this result was more than the fold of the control (the fold change is 1), with the meropenem isolate having the highest fold value (5.39) and sub-MIC value (8 µg/ml). Furthermore, the four isolates' qacE-Δ1 gene expression folds ranged from 2.11 to 5.01, which was higher than the fold of the control, where the highest fold value was recorded for the EDTA (5.01) with the sub-MIC value (125-500 µg/ml). Also, a high value of upregulation of the gene qacE –Δ1, during the treatment with dettol with sub-MIC (0.25-0.5%), and the fold change was 4.19. In conclusion, the present findings indicated to the significant relationship between the treatments with meropenem and the high values of gene expression of the gene sugE1 in comparison with the control and other agents, Furthermore, compared to the antibiotic and control, there was a strong correlation found between the high fold changes of the gene qacE -Δ1 with the presence of dettol and EDTA.

A Comparison of GAPDH and ACTB As Internal Control for Gene Expression Studies in Different Cancer Cell Lines

Housekeeping genes are used as internal controls to normalize the expression of target genes in gene expression studies. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and Actin beta (ACTB) are frequently preferred as housekeeping genes in gene expression studies. Due to the general alterations in the gene expression pattern in cancer cases, the selection of the appropriate housekeeping genes for these studies are challenging. In this study, we aimed to analyze the expression of the well-known housekeeping genes GAPDH and ACTB in 6 different cancer cell lines. Furthermore, the relative gene expression of the selected target gene (Tissue inhibitor of metalloproteinases 2-TIMP2) was normalized separately using GAPDH and ACTB and the obtained results were compared with each other. Finally, the stability of GAPDH and ACTB was analyzed using the in-silico tool, Bestkeeper. As a result of the study, it is found that the expression of GAPDH and ACTB were significantly different in the Jurkat (p&lt; 0.01), MOLT4 (p&lt; 0.05), REH (p&lt; 0.001) and HT29 (p&lt; 0.001) cell lines. Based on this finding, significantly different relative target gene expression values were obtained in different cell lines depending on whether the selected housekeeping gene was GAPDH or ACTB. In addition, GAPDH was found to show less variation among the samples used in all cell lines and more stability based on Bestkeeper analysis. These results support that the appropriate housekeeping gene selection, especially in cancer cell lines, may be an effective factor in obtaining accurate results for the studies in the field provide guidance to researchers. Keywords: Cancer cell lines, Gene expression, Housekeeping genes, GAPDH, ACTB

Potential of quantum machine learning for solving the real-world problem of cancer classification

Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world biological large datasets including ten different cancer types based on gene expression values. By comparing the classification results obtained from the quantum algorithm with those from classical approaches, we disclose that the QML algorithm overall achieves comparable and reliable results. Moreover, we identify novel biomarkers that can contribute to the understanding of cancer biology. Some of these biomarkers are consistent with DNA promoter methylation. Our findings highlight the potential of QML in cancer classification and biomarker discovery, paving the way for future advancements in other disease research and clinical applications.

Effect and mechanism of the PTMAP5–hsa-miR-22-3p–KIF2C regulatory axis on the occurrence and development of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a primary liver cancer, is known for its high malignancy potential. Despite extensive research, the etiology of HCC remains elusive, with numerous molecular mechanisms yet to be deciphered. This study delves into the influence and mechanism of the PTMAP5&amp;ndash;hsa-miR-22-3p&amp;ndash;KIF2C regulatory axis in HCC pathogenesis. A comparative analysis of gene expression profiles between the GSE87630 and GSE45267 datasets unveiled a cohort of 346 differentially expressed genes. Protein&amp;ndash;protein interaction networks were established using the STRING database, and key genes were identified through receiver operating characteristic curve analysis and LASSO regression analysis. The expression and prognostic value of these key genes were further evaluated using GEPIA and Kaplan&amp;ndash;Meier analysis. Upstream miRNAs were predicted using the MiRTarBase and StarBase databases, facilitating the construction of the PTMAP5&amp;ndash;hsa-miR-22-3p&amp;ndash;KIF2C regulatory axis. Co-expression analysis of KIF2C was performed through UALCAN and StarBase, and the regulatory axis was found to play a pivotal role in HCC development through the cell cycle, oocyte meiosis, and FOXO signaling, as revealed by DAVID enrichment analysis. Furthermore, the expression and prognostic significance of KIF2C in various cancer types were assessed using TIMER and GEPIA, while TISIDB analysis revealed correlations between KIF2C expression and relevant major histocompatibility complex molecules, immunomodulators, chemokines, receptors, and immune variants in HCC. These findings also extended to HCC molecular subtypes. In conclusion, we constructed a novel PTMAP5&amp;ndash;hsa-miR-22-3p&amp;ndash;KIF2C regulatory subnetwork, which could provide valuable therapeutic targets and diagnostic markers for HCC.

Importance of Transcript Variants in Transcriptome Analyses.

RNA sequencing (RNA-Seq) has become a widely adopted technique for studying gene expression. However, conventional RNA-Seq analyses rely on gene expression (GE) values that aggregate all the transcripts produced under a single gene identifier, overlooking the complexity of transcript variants arising from different transcription start sites or alternative splicing. Transcript variants may encode proteins with diverse functional domains, or noncoding RNAs. This study explored the implications of neglecting transcript variants in RNA-Seq analyses. Among the 1334 transcription factor (TF) genes expressed in mouse embryonic stem (ES) or trophoblast stem (TS) cells, 652 were differentially expressed in TS cells based on GE values (365 upregulated and 287 downregulated, ≥absolute 2-fold changes, false discovery rate (FDR) p-value ≤ 0.05). The 365 upregulated genes expressed 883 transcript variants. Further transcript expression (TE) based analyses identified only 174 (<20%) of the 883 transcripts to be upregulated. The remaining 709 transcripts were either downregulated or showed no significant changes. Meanwhile, the 287 downregulated genes expressed 856 transcript variants and only 153 (<20%) of the 856 transcripts were downregulated. The other 703 transcripts were either upregulated or showed no significant change. Additionally, the 682 insignificant TF genes (GE values < absolute 2-fold changes and/or FDR p-values > 0.05) between ES and TS cells expressed 2215 transcript variants. These included 477 (>21%) differentially expressed transcripts (276 upregulated and 201 downregulated, ≥absolute 2-fold changes, FDR p-value ≤ 0.05). Hence, GE based RNA-Seq analyses do not represent accurate expression levels due to divergent transcripts expression from the same gene. Our findings show that by including transcript variants in RNA-Seq analyses, we can generate a precise understanding of a gene's functional and regulatory landscape; ignoring the variants may result in an erroneous interpretation.

Bioinformatic prediction of miR-320a as a potential negative regulator of CDGSH iron-sulfur domain 2 (CISD2), involved in lung adenocarcinoma bone metastasis via MYC activation, and associated with tumor immune infiltration.

Ferroptosis, a form of regulated cell death associated with iron-dependent lipid peroxidation, plays a role in cancer progression. However, the specific mechanisms of ferroptosis in lung adenocarcinoma (LUAD) bone metastasis (BM) remain unclear. Using bioinformatics analysis, this study sought to identify the ferroptosis-associated genes involved in BM in LUAD, thus providing potential novel targets for the treatment of BM in LUAD. The RNA expression dataset GSE10799 was acquired from the Gene Expression Omnibus (GEO) database, and intersected with the ferroptosis dataset to identify ferroptosis-related differentially expressed genes (DEGs). The expression of candidate genes and their correlation with the prognosis of LUAD patients were validated in The Cancer Genome Atlas (TCGA) database. A protein gene interaction network was constructed using GeneMania and Retrieval of Interacting Genes/Proteins (STRING) databases. The association between the candidate genes and immune cells was assessed via TCGA and Tumor IMmune Estimation Resource (TIMER) databases. The potential mechanisms were elucidated by a gene set enrichment analysis (GSEA). The relevant microRNAs (miRNAs or miRs) that bind to the 3'untranslated region (3'UTR) end of candidate genes' mRNA was explored using the TargetScan database. The expression of these candidate miRNAs in LUAD was validated and the correlation between candidate miRNAs and candidate mRNAs was tested using the TCGA database. Finally, the clinical data of 40 LUAD patients were retrospectively analyzed to evaluate the clinical value of candidate gene expression for LUAD BM patients. In this research, 15 ferroptosis-related DEGs in LUAD BM were identified. TCGA database analysis indicated that patients with low levels of CDGSH iron-sulfur domain 2 (CISD2) in LUAD had better disease-specific survival (DSS), overall survival (OS), and a better progression-free interval (PFI) than those with high levels of CISD2. The TIMER database results show that the expression of CISD2 is correlated with the infiltration levels of various immune cells. The GSEA indicated that CISD2 might influence biological activity in LUAD by participating in cell-cycle regulation, mitochondrial translation, DNA damage repair, c-Myc (MYC) activation, and the P53 signaling pathway. Through the combined analysis of the TargetScan and TCGA databases, hsa-miR-320a was identified as the optimal upstream regulatory miRNA. The immunohistochemistry data indicated that the positive CISD2 expression rates and immunohistochemistry scores of the patients with BM were significantly higher than those of the patients without BM (P<0.05). The high expression of CISD2 is a significant risk factor for BM in LUAD. The downregulation of CISD2 expression may extend DSS, OS, and the PFI of LUAD patients. Thus, CISD2 could serve as a novel predictive biomarker for LUAD patients. Further, miR-320a might negatively regulate CISD2 and participate in LUAD BM by activating MYC. These data provide a potential perspective for developing anticancer therapies for LUAD-BM patients.

ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities.

High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools. Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1, that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses. ExpOmics is available at http://www.biomedical-web.com/expomics.

Gene Expression Changes Associated With Recurrence After Gross Total Resection of Newly Diagnosed World Health Organization Grade 1 Meningioma.

Patients who undergo gross total resection (GTR) of Central Nervous System World Health Organization (WHO) grade 1 meningioma constitute a "low-risk" group, but some low-risk meningiomas can recur despite reassuring clinical and histological features. In this study, gene expression values in newly diagnosed WHO grade 1 meningiomas that had undergone GTR were evaluated for their association with recurrence. This was a retrospective, international, multicenter cohort study that included WHO grade 1 meningiomas that underwent GTR, as first treatment, based on postoperative magnetic resonance imaging. Normalized gene expression values from a previously validated 34-gene panel were evaluated for their association with recurrence. Kaplan-Meier, multivariable Cox proportional hazard analyses, and K-means clustering were performed to assess the association of genes of interest with recurrence and identify molecular subgroups among clinically and histologically low-risk meningiomas. In total, 442 patients with WHO grade 1 meningiomas that underwent GTR and had available gene expression profiling data were included in the study. The median follow-up was 5.0 years (interquartile range 2.6-7.7 years), local recurrence occurred in 36 patients (8.1%), 5-year local freedom from recurrence was 90.5%, and median time to recurrence was 2.9 years (range 0.5-10.7 years). Eleven genes were associated with local recurrence, including lower expression of ARID1B, ESR1, LINC02593, PGR, and TMEM30B and higher expression of CDK6, CDKN2C, CKS2, KIF20A, PGK1, and TAGLN. Of these genes, PGK1 had the largest effect size. K-means clustering based on these 11 genes distinguished 2 molecular groups of clinically and histologically low-risk meningiomas with significant differences in local freedom from recurrence (hazard ratio 2.5, 95% CI 1.2-5.1, P = .016). Gene expression profiling may help to identify newly diagnosed WHO grade 1 meningiomas that have an elevated risk of recurrence despite GTR.