Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks.

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

An NGS-based assay for accurate detection and quantification of immune gene expression in mouse tumor models.

Tumor microenvironment (TME) is a complex dynamic system with many tumor-interacting components including tumor-infiltrating leukocytes (TILs), cancer associated fibroblasts, blood vessels, and other stromal constituents. It intrinsically affects tumor development and pharmacology of oncology therapeutics, particularly immune-oncology (IO) treatments. Accurate measurement of TME is therefore of great importance for understanding the tumor immunity, identifying IO treatment mechanisms, developing predictive biomarkers, and ultimately, improving the treatment of cancer. Here, we introduce a mouse-IO NGS-based (NGSmIO) assay for accurately detecting and quantifying the mRNA expression of 1080 TME related genes in mouse tumor models. The NGSmIO panel was shown to be superior to the commonly used microarray approach by hosting 300 more relevant genes to better characterize various lineage of immune cells, exhibits improved mRNA and protein expression correlation to flow cytometry, shows stronger correlation with mRNA expression than RNAseq with 10x higher sequencing depth, and demonstrates higher sensitivity in measuring low-expressed genes. We describe two studies; firstly, detecting the pharmacodynamic change of interferon-γ expression levels upon anti-PD-1: anti-CD4 combination treatment in MC38 and Hepa 1-6 tumors; and secondly, benchmarking baseline TILs in 14 syngeneic tumors using transcript level expression of lineage specific genes, which demonstrate effective and robust applications of the NGSmIO panel.

GTSE1: A potential prognostic and diagnostic biomarker in various tumors including lung adenocarcinoma.

OBJECTIVE: This research was aimed to comprehensively investigate the expression levels, diagnostic and prognostic implications, and the relationship with immune infiltration of G2 and S phase-expressed-1 (GTSE1) across 33 tumor types, including lung adenocarcinoma (LUAD), through gene expression profiling. METHODS: GTSE1 mRNA expression data together with clinical information were acquired from Xena database of The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) database for this study. The Wilcoxon rank-sum test was used to detect differences in GTSE1 expression between groups. The ability of GTSE1 to accurately predict cancer status was evaluated by calculating the area under the curve (AUC) value for the receiver operating characteristic curve. Additionally, we investigated the predictive value of GTSE1 in individuals diagnosed with neoplasms using univariate Cox regression analysis as well as Kaplan-Meier curves. Furthermore, the correlation between GTSE1 expression and levels of immune infiltration was assessed by utilizing the Tumor Immune Estimate Resource (TIMER) database to calculate the Spearman rank correlation coefficient. Finally, the pan-cancer analysis findings were validated by examining the association between GTSE1 expression and prognosis among patients with LUAD. RESULTS: GTSE1 exhibited significantly increased expression levels in a wide range of tumor tissues in contrast with normal tissues (p < 0.05). The expression of GTSE1 in various tumors was associated with clinical features, overall survival, and disease-specific survival (p < 0.05). In immune infiltration analyses, a strong correlation of the level of immune infiltration with the expression of GTSE1 was observed. Furthermore, GTSE1 demonstrated good discriminative and diagnostic value for most tumors. Additional experiments confirmed the relationship between elevated GTSE1 expression and unfavorable prognosis in individuals diagnosed with LUAD. These findings indicated the crucial role of GTSE1 expression level in influencing the development and immune infiltration of different types of tumors. CONCLUSIONS: GTSE1 might be a potential biomarker for the prognosis of pan-cancer. Meanwhile, it represented a promising target for immunotherapy.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Single-cell transcriptome analysis reveals heterogeneity of neutrophils in non-small cell lung cancer.

BACKGROUND: Lung cancer stands out as a highly perilous malignant tumor with severe implications for human health. There has been a growing interest in neutrophils as a result of their role in promoting cancer in recent years. Thus, the present study aimed to investigate the heterogeneity of neutrophils in non-small cell lung cancer (NSCLC). METHODS: Single-cell RNA sequencing of tumor-associated neutrophils (TANs) and polymorphonuclear neutrophils sourced from the Gene Expression Omnibus database was analyzed. Moreover, cell-cell communication, differentiation trajectories and transcription factor analyses were performed. RESULTS: Neutrophils were found to be closely associated with macrophages. Four major types of TANs were identified: a transitional subcluster that migrated from blood to tumor microenvironment (TAN-0), an inflammatory subcluster (TAN-1), a subpopulation that displayed a distinctive transcriptional signature (TAN-2) and a final differentiation state that promoted tumor formation (TAN-3). Meanwhile, TAN-3 displayed a marked increase in glycolytic activity. Finally, transcription factors were analyzed to uncover distinct TAN cluster-specific regulons. CONCLUSIONS: The discovery of the dynamic characteristics of TANs in the present study is anticipated to contribute to yielding a better understanding of the tumor microenvironment and advancing the treatment of NSCLC.

Gene Expression and DNA Methylation Profiling Suggest Potential Biomarkers for Azacitidine Resistance in Myelodysplastic Syndrome.

Myelodysplastic syndrome/neoplasm (MDS) comprises a group of heterogeneous hematopoietic disorders that present with genetic mutations and/or cytogenetic changes and, in the advanced stage, exhibit wide-ranging gene hypermethylation. Patients with higher-risk MDS are typically treated with repeated cycles of hypomethylating agents, such as azacitidine. However, some patients fail to respond to this therapy, and fewer than 50% show hematologic improvement. In this context, we focused on the potential use of epigenetic data in clinical management to aid in diagnostic and therapeutic decision-making. First, we used the F-36P MDS cell line to establish an azacitidine-resistant F-36P cell line. We performed expression profiling of azacitidine-resistant and parental F-36P cells and used biological and bioinformatics approaches to analyze candidate azacitidine-resistance-related genes and pathways. Eighty candidate genes were identified and found to encode proteins previously linked to cancer, chronic myeloid leukemia, and transcriptional misregulation in cancer. Interestingly, 24 of the candidate genes had promoter methylation patterns that were inversely correlated with azacitidine resistance, suggesting that DNA methylation status may contribute to azacitidine resistance. In particular, the DNA methylation status and/or mRNA expression levels of the four genes (AMER1, HSPA2, NCX1, and TNFRSF10C) may contribute to the clinical effects of azacitidine in MDS. Our study provides information on azacitidine resistance diagnostic genes in MDS patients, which can be of great help in monitoring the effectiveness of treatment in progressing azacitidine treatment for newly diagnosed MDS patients.

How the Ethylene Biosynthesis Pathway of Semi-Halophytes Is Modified with Prolonged Salinity Stress Occurrence?

The mechanism of ethylene (ET)-regulated salinity stress response remains largely unexplained, especially for semi-halophytes and halophytes. Here, we present the results of the multifaceted analysis of the model semi-halophyte Mesembryanthemum crystallinum L. (common ice plant) ET biosynthesis pathway key components' response to prolonged (14 days) salinity stress. Transcriptomic analysis revealed that the expression of 3280 ice plant genes was altered during 14-day long salinity (0.4 M NaCl) stress. A thorough analysis of differentially expressed genes (DEGs) showed that the expression of genes involved in ET biosynthesis and perception (ET receptors), the abscisic acid (ABA) catabolic process, and photosynthetic apparatus was significantly modified with prolonged stressor presence. To some point this result was supported with the expression analysis of the transcript amount (qPCR) of key ET biosynthesis pathway genes, namely ACS6 (1-aminocyclopropane-1-carboxylate synthase) and ACO1 (1-aminocyclopropane-1-carboxylate oxidase) orthologs. However, the pronounced circadian rhythm observed in the expression of both genes in unaffected (control) plants was distorted and an evident downregulation of both orthologs' was induced with prolonged salinity stress. The UPLC-MS analysis of the ET biosynthesis pathway rate-limiting semi-product, namely of 1-aminocyclopropane-1-carboxylic acid (ACC) content, confirmed the results assessed with molecular tools. The circadian rhythm of the ACC production of NaCl-treated semi-halophytes remained largely unaffected by the prolonged salinity stress episode. We speculate that the obtained results represent an image of the steady state established over the past 14 days, while during the first hours of the salinity stress response, the view could be completely different.

Kernel-based testing for single-cell differential analysis.

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops.

BACKGROUND: Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions. RESULTS: Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in "glycolysis/gluconeogenesis" and "fatty acid synthesis" were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production. CONCLUSIONS: This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

Reference-free inferring of transcriptomic events in cancer cells on single-cell data.

BACKGROUND: Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference. RESULTS: Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment. CONCLUSIONS: Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression.

Temporal RNA-sequencing (RNA-seq) studies of bulk samples provide an opportunity for improved understanding of gene regulation during dynamic phenomena such as development, tumor progression or response to an incremental dose of a pharmacotherapeutic. Moreover, single-cell RNA-seq (scRNA-seq) data implicitly exhibit temporal characteristics because gene expression values recapitulate dynamic processes such as cellular transitions. Unfortunately, temporal RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are often difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a framework for analyzing RNA-seq data that incorporates measurement uncertainty, while introducing a special formulation for those acquired to monitor dynamic phenomena. This method is specifically suited for RNA-seq studies in which low-count transcripts with small-fold changes lead to significant biological effects. Such transcripts include genes involved in signaling and non-coding RNAs that inherently exhibit low levels of expression. Using simulation studies, we show that this framework down-weights samples that exhibit extreme responses such as batch effects allowing them to be modeled with the rest of the samples and maintain the degrees of freedom originally envisioned for a study. Using temporal experimental data, we demonstrate the framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and an scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate of false negative discoveries compared to common approaches. Finally, we provide two packages in Python and R that are self-contained and easy to use, including test data.

Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer.

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes.

Methods to Enable Spatial Transcriptomics of Bone Tissues.

Understanding the relationship between the cells and their location within each tissue is critical to uncover the biological processes associated with normal development and disease pathology. Spatial transcriptomics is a powerful method that enables the analysis of the whole transcriptome within tissue samples, thus providing information about the cellular gene expression and the histological context in which the cells reside. While this method has been extensively utilized for many soft tissues, its application for the analyses of hard tissues such as bone has been challenging. The major challenge resides in the inability to preserve good quality RNA and tissue morphology while processing the hard tissue samples for sectioning. Therefore, a method is described here to process freshly obtained bone tissue samples to effectively generate spatial transcriptomics data. The method allows for the decalcification of the samples, granting successful tissue sections with preserved morphological details while avoiding RNA degradation. In addition, detailed guidelines are provided for samples that were previously paraffin-embedded, without demineralization, such as samples collected from tissue banks. Using these guidelines, high-quality spatial transcriptomics data generated from tissue bank samples of primary tumor and lung metastasis of bone osteosarcoma are shown.

Practical considerations for pathological diagnosis and molecular profiling of cholangiocarcinoma: an expert review for best practices.

INTRODUCTION: Advances in precision medicine have expanded access to targeted therapies and demand for molecular profiling of cholangiocarcinoma (CCA) patients in routine clinical practice. However, pathologists face challenges in establishing a definitive intrahepatic CCA (iCCA) diagnosis while preserving sufficient tissue for molecular profiling. Additionally, they frequently face challenges in optimal tissue handling to preserve nucleic acid integrity. AREAS COVERED: This article first identifies the challenges in establishing a definitive diagnosis of iCCA in a lesional liver biopsy while preserving sufficient tissue for molecular profiling. Then, the authors explore the clinical value of molecular profiling, the basic principles of single gene and next-generation sequencing (NGS) techniques, and the challenges in tissue sampling for genomic testing. They also propose an algorithm for best practice in tissue management for molecular profiling of CCA. EXPERT OPINION: Several practical challenges face pathologists during tissue sampling and processing for molecular profiling. Optimized tissue processing, careful tissue handling, and selection of appropriate approaches to molecular testing are essential to ensure that the highest possible quality of diagnostic information is provided in the greatest proportion of cases.

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied. METHODS: We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis. CONCLUSION: We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.

Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms.

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These profiles represent human NASH and control samples and were used for differential genes (DEGs) expression screening. Two machine learning methods, the Least Absolute Shrinkage and Selection Operator regression model and Support Vector Machine Recursive Feature Elimination, were used to identify candidate disease signature genes. The CIBERSORT deconvolution algorithm was employed to analyze the infiltration of 22 immune cell types in NASH. Additionally, we constructed a NASH cell model using HepG2 cells treated with oleic acid and free fatty acids. The construction of the cell model was verified using oil red O staining, and Western blotting was used to detect the protein expression of the disease signature genes in both control and model groups. As a result, a total of 262 DEGs were identified. These DEGs were primarily associated with metal ion transmembrane transporter activity, sodium ion transmembrane transporter protein activity, calcium ion, and neuroactive ligand-receptor interactions. FOS, IGFBP2, dual-specificity phosphatase 1 (DUSP1), and IKZF3 were identified as disease signature genes of NASH by the least absolute shrinkage and selection operator and Support Vector Machine Recursive Feature Elimination algorithms for DEGs analysis. The receiver operating characteristic curves showed that FOS, IGFBP2, DUSP1, and IKZF3 had good diagnostic value (area under receiver operating characteristic curve > 0.8). These findings were validated in the GSE89632 dataset and through cellular assays. Immunocyte infiltration analysis revealed that NASH was associated with CD8 T cells, CD4 T cells, follicular helper T cells, resting NK cells, eosinophils, regulatory T cells, and γδ T cells. The FOS, IGFBP2, DUSP1, and IKZF3 genes were specifically associated with follicular helper T cells. Lipid droplet aggregation significantly increased in HepG2 cells treated with oleic acid and free fatty acids, indicating successful construction of the cell model. In this model, the expression of FOS, IGFBP2, and DUSP1 was significantly decreased, while that of IKZF3 was significantly elevated (P < .01, P < .001) compared with the control group. Therefore, FOS, IGFBP2, DUSP1, and IKZF3 can be considered as disease signature genes associated with immune infiltration in NASH.

Stellae-123 gene expression signature improved risk stratification in taiwanese acute myeloid leukemia patients.

The European Leukemia Net recommendations provide valuable guidance in treatment decisions of patients with acute myeloid leukemia (AML). However, the genetic complexity and heterogeneity of AML are not fully covered, notwithstanding that gene expression analysis is crucial in the risk stratification of AML. The Stellae-123 score, an AI-based model that captures gene expression patterns, has demonstrated robust survival predictions in AML patients across four western-population cohorts. This study aims to evaluate the applicability of Stellae-123 in a Taiwanese cohort. The Stellae-123 model was applied to 304 de novo AML patients diagnosed and treated at the National Taiwan University Hospital. We find that the pretrained (BeatAML-based) model achieved c-indexes of 0.631 and 0.632 for the prediction of overall survival (OS) and relapse-free survival (RFS), respectively. Model retraining within our cohort further improve the cross-validated c-indexes to 0.667 and 0.667 for OS and RFS prediction, respectively. Multivariable analysis identify both pretrained and retrained models as independent prognostic biomarkers. We further show that incorporating age, Stellae-123, and ELN classification remarkably improves risk stratification, revealing c-indices of 0.73 and 0.728 for OS and RFS, respectively. In summary, the Stellae-123 gene expression signature is a valuable prognostic tool for AML patients and model retraining can improve the accuracy and applicability of the model in different populations.

High-throughput mechanical phenotyping and transcriptomics of single cells.

The molecular system regulating cellular mechanical properties remains unexplored at single-cell resolution mainly due to a limited ability to combine mechanophenotyping with unbiased transcriptional screening. Here, we describe an electroporation-based lipid-bilayer assay for cell surface tension and transcriptomics (ELASTomics), a method in which oligonucleotide-labelled macromolecules are imported into cells via nanopore electroporation to assess the mechanical state of the cell surface and are enumerated by sequencing. ELASTomics can be readily integrated with existing single-cell sequencing approaches and enables the joint study of cell surface mechanics and underlying transcriptional regulation at an unprecedented resolution. We validate ELASTomics via analysis of cancer cell lines from various malignancies and show that the method can accurately identify cell types and assess cell surface tension. ELASTomics enables exploration of the relationships between cell surface tension, surface proteins, and transcripts along cell lineages differentiating from the haematopoietic progenitor cells of mice. We study the surface mechanics of cellular senescence and demonstrate that RRAD regulates cell surface tension in senescent TIG-1 cells. ELASTomics provides a unique opportunity to profile the mechanical and molecular phenotypes of single cells and can dissect the interplay among these in a range of biological contexts.