Gene Expression Information Research Articles

Research Progress of Single-cell RNA Sequencing in Esophageal Cancer

Esophageal cancer (EC) is a highly invasive malignant tumor with early onset, rapid progression, and high risk of recurrence and metastasis. Despite continuous advances in treatment methods, the five-year survival rate has not significantly improved. Single-cell RNA sequencing (scRNA-seq), a high-throughput sequencing technology that has received much attention in recent years, can identify gene expression information at the single-cell level, addressing the issue of cell heterogeneity in traditional sequencing technologies and greatly promoting the development of personalized diagnosis and precision medicine. This article summarizes its latest research progress in studying the tumor microenvironment, metabolic reprogramming, and neoadjuvant chemotherapy in the EC research, providing new insights and ideas for the study, clinical diagnosis, and precision treatment of EC.

Imputation for Single-cell RNA-seq Data with Non-negative Matrix Factorization and Transfer Learning.

Single-cell RNA sequencing (scRNA-seq) has been proven to be an effective technology for investigating the heterogeneity and transcriptome dynamics due to the single-cell resolution. However, one of the major problems for data obtained by scRNA-seq is excessive zeros in the count matrix, which hinders the downstream analysis enormously. Here, we present a method that integrates non-negative matrix factorization and transfer learning (NMFTL) to impute the scRNA-seq data. It borrows gene expression information from the additional dataset and adds graph-regularized terms to the decomposed matrices. These strategies not only maintain the intrinsic geometrical structure of the data itself but also further improve the accuracy of estimating the expression values by adding the transfer term in the model. The real data analysis result demonstrates that the proposed method outperforms the existing matrix-factorization-based imputation methods in recovering dropout entries, preserving gene-to-gene and cell-to-cell relationships, and in the downstream analysis, such as cell clustering analysis, the proposed method also has a good performance. For convenience, we have implemented the "NMFTL" method with R scripts, which could be available at https://github.com/FocusPaka/NMFTL.

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures

Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.

StAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics.

With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution through Wasserstein distance. The adversarial training process can make cell embeddings better capture spatial domain information and more robust. Moreover, stAA incorporates global graph information into cell embeddings using labels generated by pre-clustering. Our experimental results show that stAA outperforms the state-of-the-art methods and achieves better clustering results across different profiling platforms and various resolutions. We also conducted numerous biological analyses and found that stAA can identify fine-grained structures in tissues, recognize different functional subtypes within tumors and accurately identify developmental trajectories.

The m7G Reader NCBP2 Promotes Pancreatic Cancer Progression by Upregulating MAPK/ERK Signaling.

PDAC is one of the most common malignant tumors worldwide. The difficulty of early diagnosis and lack of effective treatment are the main reasons for its poor prognosis. Therefore, it is urgent to identify novel diagnostic and therapeutic targets for PDAC patients. The m7G methylation is a common type of RNA modification that plays a pivotal role in regulating tumor development. However, the correlation between m7G regulatory genes and PDAC progression remains unclear. By integrating gene expression and related clinical information of PDAC patients from TCGA and GEO cohorts, m7G binding protein NCBP2 was found to be highly expressed in PDAC patients. More importantly, PDAC patients with high NCBP2 expression had a worse prognosis. Stable NCBP2-knockdown and overexpression PDAC cell lines were constructed to further perform in-vitro and in-vivo experiments. NCBP2-knockdown significantly inhibited PDAC cell proliferation, while overexpression of NCBP2 dramatically promoted PDAC cell growth. Mechanistically, NCBP2 enhanced the translation of c-JUN, which in turn activated MEK/ERK signaling to promote PDAC progression. In conclusion, our study reveals that m7G reader NCBP2 promotes PDAC progression by activating MEK/ERK pathway, which could serve as a novel therapeutic target for PDAC patients.

Epigenetic alterations in urothelial bladder cancer associated with disease outcomes.

Bladder cancer (BLCA) is a molecular heterogeneous disease with known genetic distinctive signatures. However, DNA methylation is highly prevalent across a wide range of tumors, suggesting its potential in oncogenesis. Here, we aimed to interrogate the role of nine epigenetic alterations as diagnostic and prognostic markers in BLCA. DNA methylation, gene expression, and clinicopathological information were retrieved from The Cancer Genome Atlas data portal. Methylation values and gene expression were assessed to determine their association with normal and malignant tissue. Additionally, we studied the association between methylation values and clinicopathological variables. For the prognostic model, Kaplan-Meier Survival curves were generated. Lastly, univariate and multivariate analysis were performed to evaluate the simultaneous impact of methylation and clinicopathological variables on the risk of tumor progression and survival. Nine CpG sites' methylation -values involved in our study demonstrated different methylation signatures between normal and malignant urothelium. Hypermethylated CpGs were overrepresented in tumor tissue (p < 0.0001). Opposingly, 4 CpG sites showed lower methylation values in tumor samples (p < 0.0001). Cg12743248high and cg17192862low are risk factors for progression-free survival, whereas cg12374721high (HR:3.003 (1.283-7.030)) also demonstrated to be the most valuable independent risk factor for disease progression and a risk factor for overall survival. We have identified that methylated cg12374721 shows promise as a diagnostic and independent prognostic marker in BLCA progression.

The expression of neuraminidase-1 in Ewing sarcoma and its impact on sarcoma cell proliferation and migration

Objective: To investigate the expression of neuraminidase-1 (NEU1) in Ewing sarcoma (ES) tissue and its effect on the proliferation and migration of ES cells. Methods: To obtain datasets of ES from the National Center for Biotechnology Information's High-Throughput Gene Expression Omnibus (GEO) for the analysis of NEU1 expression in ES; to acquire ES patient dataset from the International Cancer Genome Consortium (ICGC) database and apply Kaplan-Meier survival analysis to investigate the relationship between NEU1 and the prognosis of ES patients; adopting both univariate and multivariate Cox regression analysis to determine whether NEU1 is a prognostic factor for ES; adopting the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation to analyze the potential mechanism of NEU1 in regulating the malignant biological behavior of ES; adopting the real-time fluorescence quantitative polynucleotide chain reaction (RT-qPCR) to verify the expression of NEU1 in the human bone marrow mesenchymal stem cells (hBMSC) and the ES cell line RD-ES; adopting the transfection technology to knock down the expression of NEU1 in ES cell lines and divide them into two groups: shRNA-NEU1 and shRNA-NC to explore the effects of altered NEU1 expression on ES malignant behavior; adopting the cell counting kit (CCK-8) and cell clone formation experiment to detect the proliferation ability of two groups of cells; adopting the scratch healing experiment to test the cell migration ability of the two groups. Results: We retrieved and analyzed data from the GEO database, including GSE17674 (44 ES tissues and 18 normal tissues) and GSE17679 (87 ES tissues and 18 normal tissues), and found that NEU1 expression was significantly higher in ES tissues compared to normal control tissues (P<0.001). The complete gene expression and clinical information of 56 ES patients obtained from the ICGC database revealed that the ES patients with high NEU1 expression (n=28) had a significantly lower overall survival rates at different time points compared to those with low NEU1 expression (n=28) (HR=2.830, 95%CI:1.324-6.051, P=0.005). Univariate analysis indicated that NEU1 could impact ES patient prognosis (HR=1.049, 95%CI: 1.008-1.092, P=0.019), and multivariate analysis further suggested that NEU1 could serve as a risk factor for ES prognosis (HR=1.087, 95%CI: 1.028-1.148, P=0.003). KEGG results show that MAPK signaling pathway and cell adhesion molecule signaling pathway were potential mechanisms regulating the malignant process of ES. The RT-qPCR results showed that the expression level of NEU1 in the RD-ES cell line is significantly higher than that in the control cell hBMSC (2 184.23±527.32 vs 1.00±0.08, P<0.001). The CCK-8 experiment results show that the proliferation of RD-ES cells in the NEU1 knockdown group was lower than that in the control group at 24, 48, and 72 hours (0.494±0.126 vs 0.696±0.118, 0.657±0.096 vs 1.142±0.182, 1.053±0.064 vs 1.980±0.146, all P<0.001). The results of single cell clone formation experiment show that the number of colony formation in the low expression NEU1 group was significantly lower than that in the control group (184.2±123.9 vs 362.8±78.0, P=0.021). The cell scratch healing experiment finds that the average scratch distance of the NEU1 knockdown group was significantly lower than that of the control group (19.6%±5.7% vs 56.0%±7.6%, P<0.001). Conclusion: NEU1 may be a prognostic factor in ES, and its abnormal expression in ES can affect the proliferation and migration ability of the ES cells, leading to poor prognosis in ES patients.

Genetic testing in clinical laboratory diagnostics: Present and future

Authors discuss the current state and prospects for the development of genetic testing in clinical laboratory diagnostics, recent nucleic acids sequencing technologies, their advantages and applications. Although genome-wide association studies (GWAS) have become a standard practice in identifying SNPs to determine disease susceptibility, this approach has limitations. A novel approach is proposed: integrative genome-wide association analysis (iGWAS), which relies on gene expression information to investigate the associations between SNPs and disease phenotype. Numerous studies have shown that iGWAS can significantly facilitate the search for genetic correlations and is superior to a method that relies only on the search for SNPs. Genetic testing will facilitate the molecular-based reclassification of human diseases. Authors describe the technical aspects of nanopore sequencing, the development of an iPhone app to complement miniature sequencing devices, and the world’s first mobile genomic sequence analyzer, iGenomics.

Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data.

Single-cell RNA sequencing (scRNA-Seq) technology has emerged as a powerful tool to investigate cellular heterogeneity within tissues, organs, and organisms. One fundamental question pertaining to single-cell gene expression data analysis revolves around the identification of cell types, which constitutes a critical step within the data processing workflow. However, existing methods for cell type identification through learning low-dimensional latent embeddings often overlook the intercellular structural relationships. In this paper, we present a novel non-negative low-rank similarity correction model (NLRSIM) that leverages subspace clustering to preserve the global structure among cells. This model introduces a novel manifold learning process to address the issue of imbalanced neighbourhood spatial density in cells, thereby effectively preserving local geometric structures. This procedure utilizes a position-sensitive hashing algorithm to construct the graph structure of the data. The experimental results demonstrate that the NLRSIM surpasses other advanced models in terms of clustering effects and visualization experiments. The validated effectiveness of gene expression information after calibration by the NLRSIM model has been duly ascertained in the realm of relevant biological studies. The NLRSIM model offers unprecedented insights into gene expression, states, and structures at the individual cellular level, thereby contributing novel perspectives to the field.

Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis

Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed data on mRNA gene expression and clinical information from public databases in a systematic manner. These data were utilized to construct a reliable risk prediction model for six regulators of PANoptosis. The Gene Expression Omnibus (GEO) database validated six genes with risk characteristics. The prognosis of LUAD patients could be accurately estimated by the six-gene-based model: NLR family CARD domain-containing protein 4 (NLRC4), FAS-associated death domain protein (FADD), Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD), Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2), and Mixed lineage kinase domain-like protein (MLKL). Group of higher risk and Cluster 2 indicated a poor prognosis as well as the reduced expression of immune infiltrate molecules and human leukocyte antigen. Distinct expression of PANoptosis-related genes (PRGs) in lung cancer cells was verified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, we evaluated the relationship between PRGs and somatic mutations, tumor immune dysfunction exclusion, tumor stemness indices, and immune infiltration. Using the risk signature, we conducted analyses including nomogram construction, stratification, prediction of small-molecule drug response, somatic mutations, and chemotherapeutic response.

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.

Dual spatially resolved transcriptomics for human host–pathogen colocalization studies in FFPE tissue sections

Technologies to study localized host–pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host–pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

Selection of Suitable Reference Genes for RT-qPCR Gene Expression Analysis in Centipedegrass under Different Abiotic Stress.

As a C4 warm-season turfgrass, centipedegrass (Eremochloa ophiuroides (Munro) Hack.) is known for its exceptional resilience to intensive maintenance practices. In this research, the most stably expressed reference genes in the leaves of centipedegrass under different stress treatments, including salt, cold, drought, aluminum (Al), and herbicide, were screened by the quantitative real-time PCR (RT-qPCR) technique. The stability of 13 candidate reference genes was evaluated by software GeNorm V3.4, NormFinder V20, BestKeeper V1.0, and ReFinder V1.0. The results of this experiment demonstrated that the expression of the UBC (ubiquitin-conjugating enzyme) remained the most stable under cold and Al stress conditions. On the other hand, the MD (malate dehydrogenase) gene exhibited the best performance in leaf tissues subjected to salt and drought stresses. Under herbicide stress, the expression level of the RIP (60S ribosomal protein L2) gene ranked the highest. The expression levels of abiotic stress-associated genes such as PIP1, PAL, COR413, ALMT9, and BAR were assessed to validate the reliability of the selected reference genes. This study provides valuable information and reference points for gene expression under abiotic stress conditions in centipedegrass.

Predicting Survival Signature of Bladder Cancer Related to Cancer-Associated Fibroblast (CAF) Constructed by Intersecting Genes in TCGA and GEO.

Bladder cancer was one of the most common carcinomas around the world. However, the mechanism of the disease still remained to be investigated. We expected to establish a prognostic survival model with 9 prognostic genes to predict overall survival (OS) in patients of bladder cancer. The gene expression data of bladder cancer were obtained from TCGA and GEO datasets. TCGA and GEO datasets were used for screening prognostic genes along with developing and validating a 9-gene prognostic survival model by method of weighted gene co-expression network analysis (WGCNA) and LASSO with Cox regression. The relative analysis of evaluate tumor burden mutation (TBM), GO, KEGG, chemotherapy drug and functional pathway were also performed based on CAF-related mRNAs. 151 Overlapping CAF-related genes were distinguished after intersecting differentially expressed genes from 945 genes in TCGA and 491 genes in GEO dataset. 9 Prognostic genes (MSRB2, AGMAT, KLF6, DDAH2, GADD45B, SERPINE2, STMN3, TEAD2, and COMP) were used for construction of prognostic model after LASSO with Cox regression. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Functional analysis indicated chemokine, cytokine, ECM interaction, oxidative stress and apoptosis were highly appeared. Potential drugs targeted different CAF-related genes like vemurafenib, irofulven, ghiotepa, and idarubicin were found as well. We constructed a novel 9 CAF-related mRNAs prognostic model and explored the gene expression and potential functional information of related genes, which might be worthy of clinical application. In addition, potential chemotherapy drugs could provide useful insights into the potential clinical treatment of bladder cancer.

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES.

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet.

Cell-surface proteins play a critical role in cell function and are primary targets for therapeutics. CITE-seq is a single-cell technique that enables simultaneous measurement of gene and surface protein expression. It is powerful but costly and technically challenging. Computational methods have been developed to predict surface protein expression using gene expression information such as from single-cell RNA sequencing (scRNA-seq) data. Existing methods however are computationally demanding and lack the interpretability to reveal underlying biological processes. We propose CrossmodalNet, an interpretable machine learning model, to predict surface protein expression from scRNA-seq data. Our model with a customized adaptive loss accurately predicts surface protein abundances. When samples from multiple time points are given, our model encodes temporal information into an easy-to-interpret time embedding to make prediction in a time-point-specific manner, and is able to uncover noise-free causal gene-protein relationships. Using three publicly available time-resolved CITE-seq data sets, we validate the performance of our model by comparing it with benchmarking methods and evaluate its interpretability. Together, we show that our method accurately and interpretably profiles surface protein expression using scRNA-seq data, thereby expanding the capacity of CITE-seq experiments for investigating molecular mechanisms involving surface proteins.

Predicting lung adenocarcinoma prognosis, immune escape, and pharmacomic profile from arginine and proline-related genes

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease that ranks first in morbidity and mortality. Abnormal arginine metabolism is associated with inflammatory lung disease and may influence alterations in the tumor immune microenvironment. However, the potential role of arginine and proline metabolic patterns and immune molecular markers in LUAD is unclear. Gene expression, somatic mutations, and clinicopathological information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify metabolic genes associated with overall survival (OS). Unsupervised clustering divided the sample into two subtypes with different metabolic and immunological profiles. Gene set enrichment analysis (GESA) and gene set variation analysis (GSVA) were used to analyze the underlying biological processes of the two subtypes. Drug sensitivity between subtypes was also predicted; then prognostic features were developed by multivariate Cox regression analysis. In addition, validation was obtained in the GSE68465, and GSE50081 dataset. Then, gene expression, and clinical characterization of hub genes CPS1 and SMS were performed; finally, in vitro validation experiments for knockdown of SMS were performed in LUAD cell lines. In this study, we first identified 12 arginine and proline-related genes (APRGs) significantly associated with OS and characterized the clinicopathological features and tumor microenvironmental landscape of two different subtypes. Then, we established an arginine and proline metabolism-related scoring system and identified two hub genes highly associated with prognosis, namely CPS1, and SMS. In addition, we performed CCK8, transwell, and other functional experiments on SMS to obtain consistent results. Our comprehensive analysis revealed the potential molecular features and clinical applications of APRGs in LUAD. A model based on 2 APRGs can accurately predict survival outcomes in LUAD, improve our understanding of APRGs in LUAD, and pave a new pathway to guide risk stratification and treatment strategy development for LUAD patients.

Compressed primary-to-transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder.

Major depressive disorder (MDD) has been shown to involve widespread changes in low-level sensorimotor and higher-level cognitive functions. Recent research found that a primary-to-transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type-specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex- and age-matched healthy controls derived from the REST-meta-MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary-to-transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as their paralleled subcortical impairments. The correlations between MDD-related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary-to-transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case-control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findingsmapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co-occurrence of high-level cognitive impairments with low-level sensorimotor abnormalities in MDD.

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud. Its main advantage is not only using a convolutional neural network and long short-term memory to extract original data features and estimate gene expression of RNA-seq data but also combining the statistical method of the cloud model to quantify the uncertainty and carry out in-depth analysis of the DEGs between the disease groups and the control groups. Compared with traditional analysis software of DEGs, the Deep-cloud model further improves the sensitivity and accuracy of obtaining DEGs from RNA-seq data. Overall, the proposed new approach Deep-cloud paves a new pathway for mining RNA-seq data in the biomedical field.

Mechanism of Astragali Radix-Curcumae Rhizoma in treating gastric cancer based on network pharmacology and experimental verification

This study aims to investigate the mechanism of Astragali Radix-Curcumae Rhizoma(HQEZ) in the treatment of gastric cancer based on network pharmacology. Further, the SGC7901 cell model of gastric cancer was employed to validate the efficacy and key targets of the herb pair. Firstly, the CCK-8 assay was employed to evaluate the direct effect of HQEZ on the proliferation of gastric cancer SGC7901 cells. Then, network pharmacology methods were employed to investigate the active ingredients, key targets, and key signaling pathways involved in the treatment of gastric cancer with HQEZ. The results showed that HQEZ contained 18 potential active ingredients, such as quercetin, naringenin, and curcumin. The results of gene ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment suggested that the main targets of HQEZ in treating gastric cancer were involved in the regulation of protein serine/threonine kinase activity, activation of mitogen-activated protein kinase(MAPK) activity, cysteine-type endopeptidase activity, and negative regulation of protein serine/threonine kinase activity. The hypoxia-inducible factor-1(HIF-1) signaling pathway, ATP-binding cassette(ABC) transporters, cytochrome P450-mediated metabolism of xenobiotics, p53 signaling pathway, and cell apoptosis were key signaling pathways of HQEZ in treating gastric cancer. The cell experiments demonstrated that HQEZ significantly downregulated the expression of ATP-binding cassette subfamily B member 1(ABCB1), epidermal growth factor receptor(EGFR), phosphorylated serine/threonine kinase(p-AKT), hypoxia inducible factor 1 subunit alpha(HIF1A), B-cell lymphoma 2(BCL2), breast cancer susceptibility protein 1(BRCA1), DNA polymerase theta(POLH), ribonucleotide reductase M1(RRM1), and excision repair cross-complementation group 1(ERCC1), and upregulated the expression of tumor protein P53(TP53) and cysteinyl aspartate-specific proteinase(CAPS3). Finally, a multivariate COX regression model was adopted to study the relationship between gene expression and clinical information data of gastric cancer patients in the TCGA database, which demonstrated that the key targets of HQEZ were associated with the poor prognosis in gastric cancer patients. Further feature selection using the LASSO algorithm showed that EGFR, HIF1A, TP53, POLH, RRM1, and ERCC1 were closely associated with the survival of gastric can-cer patients. In conclusion, HQEZ regulates the expression of genes involved in DNA repair, survival, and apoptosis in gastric cancer cells via multiple targets and pathways, assisting the treatment of gastric cancer.