Gene Expression Profiling Data Research Articles

Hemangiosarcoma Cells Promote Conserved Host-Derived Hematopoietic Expansion.

Hemangiosarcoma and angiosarcoma are soft-tissue sarcomas of blood vessel-forming cells in dogs and humans, respectively. These vasoformative sarcomas are aggressive and highly metastatic, with disorganized, irregular blood-filled vascular spaces. Our objective was to define molecular programs which support the niche that enables progression of canine hemangiosarcoma and human angiosarcoma. Dog-in-mouse hemangiosarcoma xenografts recapitulated the vasoformative and highly angiogenic morphology and molecular characteristics of primary tumors. Blood vessels in the tumors were complex and disorganized, and they were lined by both donor and host cells. In a series of xenografts, we observed that the transplanted hemangiosarcoma cells created exuberant myeloid hyperplasia and gave rise to lymphoproliferative tumors of mouse origin. Our functional analyses indicate that hemangiosarcoma cells generate a microenvironment that supports expansion and differentiation of hematopoietic progenitor populations. Furthermore, gene expression profiling data revealed hemangiosarcoma cells expressed a repertoire of hematopoietic cytokines capable of regulating the surrounding stromal cells. We conclude that canine hemangiosarcomas, and possibly human angiosarcomas, maintain molecular properties that provide hematopoietic support and facilitate stromal reactions, suggesting their potential involvement in promoting the growth of hematopoietic tumors.

Molecular features and prognostic factors of locally advanced microsatellite instability-high gastric cancer.

Microsatellite instability-high (MSI-H) tumors are distinct molecular subtypes in gastric cancer. However, a few studies have comprehensively reported the molecular features of MSI-H tumors and their prognostic factors in locally advanced gastric cancer. This study aimed to clarify the molecular features and prognostic factors of locally advanced MSI-H gastric cancer. This study included 499 patients with locally advanced gastric cancer who underwent radical gastrectomy. We evaluated the MSI status and compared with previously published whole-exome sequencing, panel sequencing, and gene expression profiling data. Clinicopathological characteristics and molecular profiles were compared between patients with MSI-H and microsatellite stable (MSS) gastric cancer. A subgroup analysis of survival was performed in patients with MSI-H gastric cancer. MSI-H tumors were detected in 79 of 499 patients (15.8%). MSI-H tumors were associated with an increased tumor mutational burden, MLH1 downregulation, CD274 (PD-L1) upregulation, and enrichment of cell cycle pathways. Among patients with MSI-H gastric cancer, the disease-specific survival (DSS) tended to be better in the surgery plus tegafur, gimeracil, and oteracil potassium (S-1) adjuvant chemotherapy group than in the surgery alone group, especially for stage III patients. Furthermore, DSS was better in the T cell-inflamed gene expression signature-high group, and it tended to be worse in the non-solid type poorly differentiated adenocarcinoma group. The molecular features and prognostic factors of locally advanced MSI-H gastric cancer were clarified. S-1 adjuvant chemotherapy appears to be beneficial, and the T cell-inflamed gene expression signature and histopathological type are prognostic factors in MSI-H tumors.

Identification of Prognostic Genes in Acute Myeloid Leukemia Microenvironment: A Bioinformatic and Experimental Analysis.

Acute myeloid leukemia (AML) is a lethal hematologic malignancy with a variable prognosis that is highly dependent on the bone marrow microenvironment. Consequently, a better understanding of the AML microenvironment is crucial for early diagnosis, risk stratification, and personalized therapy. In recent years, the role of bioinformatics as a powerful tool in clarifying the complexities of cancer has become more prominent. Gene expression profile and clinical data of 173 AML patients were downloaded from the TCGA database, and the xCell algorithm was applied to calculate the microenvironment score (MS). Then, the correlation of MS with FAB classification, and CALGB cytogenetic risk category was investigated. Differentially expressed genes (DEGs) were identified, and the correlation analysis of DEGs with patient survival was done using univariate cox. The prognostic value of candidate prognostic DEGs was confirmed based on the GEO database. In the last step, real-time PCR was used to compare the expression of the top three prognostic genes between patients and the control group. During TCGA data analysis, 716 DEGs were identified, and survival analysis results showed that 152 DEGs had survival-related changes. In addition, the prognostic value of 31 candidate prognostic genes was confirmed by GEO data analysis. Finally, the expression analysis of FLVCR2, SMO, and CREB5 genes, the most related genes to patients' survival, was significantly different between patients and control groups. In summary, we identified key microenvironment-related genes that influence the survival of AML patients and may serve as prognostic and therapeutic targets.

Abstract 5196: Stepwise molecular mechanisms involved in chemoresistance in bladder cancer cells

Abstract Bladder cancer (BC) is one of the most prevalent genitourinary tumors worldwide, with approximately 573,000 new cases reported. Approximately 80% of BC patients are diagnosed with non-muscle-invasive bladder cancer (NMIBC) with a high five-year survival rate, while the remaining 20% are diagnosed with muscle-invasive bladder cancer (MIBC). Although the surgical operation has been utilized to BC, patients with NMIBC frequently experience disease recurrence, of which about 20% progress to MIBC. However, the response rate of MIBC patients to gemcitabine treatment has been shown to be limited to less than 40% efficacy, and only a small number of patients have potential benefits.Chemotherapy resistance is an obstacle to cancer therapy and is considered a major cause of recurrence. Thus, understanding the mechanisms of chemoresistance is critical to improving the prognosis of patients. Here, we have established a stepwise gemcitabine-resistant T24 bladder cancer cell line to understand the molecular mechanisms of chemoresistance within cancer cells. The characteristics of the stepwise chemoresistance cell line were divided into 4 phases (parental, early, intermediate, and late phases). These four phase cells showed increasingly aggressive phenotypes in vitro and in vivo experiments with increasing phases and revealed the molecular properties of the biological process from parent cells to phased gemcitabine-resistant cell line (GRC). Taken together, through the analysis of gene expression profile data, we have characterized gene set of each phase indicating the response to anticancer drug treatment. Specifically, we identified a multigene signature (23-genes including GATA3, APOBEC3G, NT5E, MYC, STC1, FOXD1, SMAD9) and developed a chemoresistance score consisting of that could predict eventual responsiveness to gemcitabine treatment. We suggest that our findings improve prognosis by predicting to acquiring of chemotherapy resistance in bladder cancer patients. Citation Format: Jae-Jun Kim, Gi-Eun Yang, Tae Hwan Shin, Sun-Hee Leem. Stepwise molecular mechanisms involved in chemoresistance in bladder cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5196.

Bi-level gene selection of cancer by combining clustering and sparse learning

The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.

A Feature Selection Method Based on Graph Theory for Cancer Classification.

Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification. In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data. In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods. In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

C5a–C5AR1 axis as a potential trigger of the rupture of intracranial aneurysms

Recent studies have indicated the involvement of neutrophil-mediated inflammatory responses in the process leading to intracranial aneurysm (IA) rupture. Receptors mediating neutrophil recruitment could thus be therapeutic targets of unruptured IAs. In this study, complement C5a receptor 1 (C5AR1) was picked up as a candidate that may cause neutrophil-dependent inflammation in IA lesions from comprehensive gene expression profile data acquired from rat and human samples. The induction of C5AR1 in IA lesions was confirmed by immunohistochemistry; the up-regulations of C5AR1/C5ar1 stemmed from infiltrated neutrophils, which physiologically express C5AR1/C5ar1, and adventitial fibroblasts that induce C5AR1/C5ar1 in human/rat IA lesions. In in vitro experiments using NIH/3T3, a mouse fibroblast-like cell line, induction of C5ar1 was demonstrated by starvation or pharmacological inhibition of mTOR signaling by Torin1. Immunohistochemistry and an experiment in a cell-free system using recombinant C5 protein and recombinant Plasmin indicated that the ligand of C5AR1, C5a, could be produced through the enzymatic digestion by Plasmin in IA lesions. In conclusion, we have identified a potential contribution of the C5a–C5AR1 axis to neutrophil infiltration as well as inflammatory responses in inflammatory cells and fibroblasts of IA lesions. This cascade may become a therapeutic target to prevent the rupture of IAs.

Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. Mitophagy is associated with tumorigenesis and development of malignancy. However, the specific role of mitophagy has yet not been systematically explored in CRC. The RNA-sequencing dataset of CRC from The Cancer Genome Atlas (TCGA) and microarray data of gene expression profiles of CRC from Gene Expression Omnibus (GEO) were downloaded. Mitophagy-related gene sets were obtained from the Pathway Unification database. The package "limma" was used for differential gene expression analysis. Kaplan-Meier (KM) survival analyses were utilized to evaluate the prognostic value of the mitophagy regulators. Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the infiltrating immune cells and the activity of immune response. The ConsensusClusterPlus algorithm was used to determine mitophagy-related subtypes. Principal component analysis (PCA) was used to create composite measurement of mitophagy scores. The R packages "survminer" and "ReGlot" were used to plot the nomogram and calibration curves. Integrated analysis of the GEO and TCGA databases revealed some common differentially expressed genes (DEGs) in CRC. MFN2, UBB, PINK1, and PRKN were significantly downregulated in CRC samples as compared to normal samples, and other genes were significantly upregulated in CRC samples. KM survival analyses showed that high expression of ATG12 and MAP1LC3B predicted a poor prognosis, whereas high expression of TOMM22 and TOMM40 predicted a better prognosis. Mitophagy showed significant correlation with immune-related pathways in CRC samples. We identified 2 distinct CRC subtypes with different mitophagy accumulation, of which subtype B had better prognosis and immune activity. The mitophagy score may be employed as a new and efficient clinical predictor in conjunction with other clinical indicators to predict the prognosis of CRC patients. We systematically investigated the CRC heterogeneity with reference to mitophagy based on bioinformatics analyses, and the findings of this study might provide some guidance for future research into potential biomarkers for diagnosis and prognosis prediction of CRC patients.

Identification of prognostic biomarkers of smoking-related lung cancer.

The early diagnosis and effective prognostic treatment measures for lung cancer are still limited, leading to a 5-year survival rate of less than 15% for these patients. Smoking is one of the causes of lung cancer, but it is not the initial carcinogenic factor. It is not clear what specific mechanism cigarette induces lung cancer, and there is a lack of research on the relationship between related genes and the prognosis of patients with smoking lung cancer. The objective of this study was to provide new theoretical evidence and potential therapeutic targets for the mechanisms of smoking-related lung cancer formation. The gene expression profile data from the GSE12428 dataset which includes 63 lung cancer and normal tissue pairs were downloaded from the Gene Expression Omnibus (GEO) database, and data from smokers with lung cancer [both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)] from The Cancer Genome Atlas (TCGA) database were analyzed. The differential genes in smokers with lung cancer were screened using the linear model for microarray data via R software. The differential gene enrichment analysis was performed using the online analysis software Database for Annotation, Visualization and Integrated Discovery (DAVID). The expression levels of differential genes and their correlation with patient tumor clinical stage were analyzed using gene expression profiling interactive analysis (GEPIA). The overall survival rate was analyzed using Kaplan-Meier curves. In the GSE12428 dataset, 225 upregulated genes and 565 downregulated genes were identified in cancer tissues; based on smoking status, 1 upregulated gene and 4 downregulated genes were identified. Among smokers who also had lung cancer, 4 genes were downregulated, namely CSH1, BPIFA1, SLPI, and SCGB3A1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these genes were mainly associated with biological functions such as antibacterial response, humoral immune response, and response to external stimuli. Among them, BPIFA1, SLPI, and SCGB3A1 expression was decreased in lung cancer tissues, with SCGB3A1 showing significant differences. Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer. Three genes BPIFA1, SLPI and SCGB3A1, were identified as being associated with smokers with lung cancer, with SCGB3A1 showing a close correlation with patient prognosis. These findings provide potential new targets for the treatment of lung cancer. Certainly, this study needs to more investigate the mechanism of these genes regulation.

Comparison of immune-related gene signatures and immune infiltration features in early- and late-onset preeclampsia.

Preeclampsia, a severe pregnancy syndrome, is widely accepted divided into early- and late-onset preeclampsia (EOPE and LOPE) based on the onset time of preeclampsia, with distinct pathophysiological origins. However, the molecular mechanism especially immune-related mechanisms for EOPE and LOPE is currently obscure. In the present study, we focused on placental immune alterations between EOPE and LOPE and search for immune-related biomarkers that could potentially serve as potential therapeutic targets through bioinformatic analysis. The gene expression profiling data was obtained from the Gene Expression Omnibus database. ESTIMATE algorithm and Gene Set Enrichment Analysis were employed to evaluate the immune status. The intersection of differentially expressed genes in GSE74341 series and immune-related genes set screened differentially expressed immune-related genes. Protein-protein interaction network and random forest were used to identify hub genes with a validation by a quantitative real-time PCR. Kyoto Encyclopedia of Genes and Genomes pathways, Gene Ontology and gene set variation analysis were utilized to conduct biological function and pathway enrichment analyses. Single-sample gene set enrichment analysis and CIBERSORTx tools were employed to calculate the immune cell infiltration score. Correlation analyses were evaluated by Pearson correlation analysis. Hub genes-miRNA network was performed by the NetworkAnalyst online tool. Immune score and stromal score were all lower in EOPE samples. The immune system-related gene set was significantly downregulated in EOPE compared to LOPE samples. Four hub differentially expressed immune-related genes (IL15, GZMB, IL1B and CXCL12) were identified based on a protein-protein interaction network and random forest. Quantitative real-time polymerase chain reaction validated the lower expression levels of four hub genes in EOPE compared to LOPE samples. Immune cell infiltration analysis found that innate and adaptive immune cells were apparent lacking in EOPE samples compared to LOPE samples. Cytokine-cytokine receptor, para-inflammation, major histocompatibility complex class I and T cell co-stimulation pathways were significantly deficient and highly correlated with hub genes. We constructed a hub genes-miRNA regulatory network, revealing the correlation between hub genes and hsa-miR-374a-5p, hsa-miR-203a-3p, hsa-miR-128-3p, hsa-miR-155-3p, hsa-miR-129-2-3p and hsa-miR-7-5p. The innate and adaptive immune systems were severely impaired in placentas of EOPE. Four immune-related genes (IL15, GZMB, IL1B and CXCL12) were closely correlated with immune-related pathogenesis of EOPE. The result of our study may provide a new basis for discriminating between EOPE and LOPE and acknowledging the role of the immune landscape in the eventual interference and tailored treatment of EOPE.

Molecular subtype identification and prognosis stratification based on lysosome-related genes in breast cancer

BackgroundLysosomes are known to have a significant impact on the development and recurrence of breast cancer. However, the association between lysosome-related genes (LRGs) and breast cancer remains unclear. This study aims to explore the potential role of LRGs in predicting the prognosis and treatment response of breast cancer. MethodsBreast cancer gene expression profile data and clinical information were downloaded from TCGA and GEO databases, and prognosis-related LRGs were screened for consensus clustering analysis. Lasso Cox regression analysis was used to construct risk features derived from LRGs, and immune cell infiltration, immune therapy response, drug sensitivity, and clinical pathological feature differences were evaluated for different molecular subtypes and risk groups. A nomogram based on risk features derived from LRGs was constructed and evaluated. ResultsOur study identified 176 differentially expressed LRGs that are associated with breast cancer prognosis. Based on these genes, we divided breast cancer into two molecular subtypes with significant prognostic differences. We also found significant differences in immune cell infiltration between these subtypes. Furthermore, we constructed a prognostic risk model consisting of 7 LRGs, which effectively divides breast cancer patients into high-risk and low-risk groups. Patients in the low-risk group have better prognostic characteristics, respond better to immunotherapy, and have lower sensitivity to chemotherapy drugs, indicating that the low-risk group is more likely to benefit from immunotherapy and chemotherapy. Additionally, the risk score based on LRGs is significantly correlated with immune cell infiltration, including CD8 T cells and macrophages. This risk score model, along with age, chemotherapy, clinical stage, and N stage, is an independent prognostic factor for breast cancer. Finally, the nomogram composed of these factors has excellent performance in predicting overall survival of breast cancer. ConclusionsIn conclusion, this study has constructed a novel LRG-derived breast cancer risk feature, which performs well in prognostic prediction when combined with clinical pathological features.

Abstract B016: Recapitulation of gene expression-based immune subtypes of breast cancer using immunohistochemical staining

Abstract Using gene expression profiling data, we previously identified three immune subtypes (low-TIL, high-TIL, high-ISG) of breast cancer (BC) among Chinese BC patients in Hong Kong (HK) (Zhu et al., Breast Cancer Res, 2017). To investigate if standard in situ immunohistochemical (IHC) staining could capture the BC immune landscape, we analyzed archival tissue sections from 148 HKBC patients who were previously characterized based on gene expression. Dual IHC staining for 8 immune markers (CD3, CD8, CD20, FOXP3, CD68, CD163, PDL1, and IDO1) and PanCK was performed. Digitized whole slide images for each marker were analyzed using high-accuracy machine learning algorithms trained to quantify the percentage of positively stained regions. Strong correlations were found between IHC immune markers and gene expression-based immune and intrinsic subtypes. Compared to patients with lower level of tumor-infiltrating lymphocytes (low-TIL) classified based on gene expression data, those in the high-TIL subtype (characterized by high T cell and immune checkpoint gene expression) showed higher proportions of CD3, CD8, CD20, FOXP3, CD68, and PDL1 IHC positively stained cells. Similarly, the high-ISG subtype (with increased interferon-stimulated gene expression and TP53 somatic mutations) had higher proportions of CD3, CD8, CD20, FOXP3, and CD68 stained cells than the low-TIL subtype and in particular exhibited high CD163 staining, indicative of a proinflammatory tumor microenvironment. CD8, CD20, CD163, PDL1, and IDO1 expression varied significantly by PAM50 subtypes, with luminal (A and B) subtypes showing lower staining than non-luminal (HER2 and Basal) subtypes. A distinctive feature, however, of luminal B tumors was the higher infiltration by CD163-expressing cells when compared with the luminal A subtype. Except for FOXP3, all examined IHC markers showed significantly higher expression in patients with somatic TP53 mutations (CD3: P= 0.003, CD8: P= 0.005, CD20: P= 0.003, CD68: P= 0.002, CD163: P= 0.002, PDL1: P= 0.003, and IDO1: P= 0.013). On the other hand, expression of these markers did not significantly vary by PIK3CA mutation or germline APOBEC3A/B deletion polymorphism, which is more prevalent among East Asians than other populations and was previously linked to BC risk and immune environment. Of the BC risk factors assessed, only body mass index (BMI) demonstrated associations with immune cell densities, with higher BMI associated with lower expression of CD3, CD8, and CD20. This study sheds further light on the in situ immune landscape of gene expression-based BC subtypes, suggesting that standard IHC assessment of immune markers could refine BC subtype classification for clinical and epidemiological use. Citation Format: Mustapha Abubakar, Hela Koka, Priscilla Lee, Scott Lawrence, Karun Mutreja, Difei Wang, Bin Zhu, Shelly Tse, Xiaohong R Yang. Recapitulation of gene expression-based immune subtypes of breast cancer using immunohistochemical staining [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B016.

Bioinformatic analysis reveals prognostic value and immunotherapy potential of Siglec-15 in laryngeal squamous cell carcinoma

BackgroundLaryngeal squamous cell carcinoma (LSCC) is the ultimate common malignant head and neck cancer with dismal prognosis. The expression pattern and clinical significance of Siglec-15 (Sialic acid-binding immunoglobulin-like lectin 15) in LSCC are poorly understood. In order to lay the groundwork for future immune-related research on Siglec-15 in LSCC, we set out to study its expression and prognostic importance in the disease, as well as to use bioinformatics to investigate the immune features modulated by Siglec-15 in LSCC. Methods① In order to get the gene expression profile and clinical data for TCGA head and neck cancer (TCGA-HNSC), you may access the relevant data from UCSC xena and use 110 cases of laryngeal cancer as a training set. Two datasets, GSE27020 and GSE25727, were obtained from the GEO databank and utilized as validation sets. These datasets include expression profiles and clinical information. The Siglec-15 gene and immune characteristics were analyzed by bioinformatics methods. ② Retrospectively collected routine paraffin specimens from patients with pathological diagnosis of squamous cell carcinoma from December 2012 to November 2015 in Sun Yat-sen Memorial Hospital and fresh frozen tissue of patients from June 2021 to March 2022. Immunohistochemistry method, immunofluorescence technique and real-time quantitative PCR was used to examine the difference of Siglec-15 appearance in LSCC tissue and adjacent tissue, and its correlation of prognosis, clinic pathological characteristics and CD8+T lymphocyte infiltration. Using human laryngeal cancer cell line (LCC), we studied the influence of Siglec-15 in cell proliferation and invasion. ResultsWe identified Siglec-15 was upregulated in LSCC. The patients in Siglec-15 high expression group had a poor overall survival (OS) based on the clinical information from TGCA and 111 LSCC patients that hospitalized in Sun Yat-sen Memorial Hospital. The COX regression analysis indicated Siglec-15 as an independent predictor for poor prognosis of LSCC. Bioinformatic analysis suggested that the high expression of Siglec-15 shape an immune suppressive tumor microenvironment (TEM), leading to poor response to immunotherapy in LSCC. Siglec-15 enhanced cell invasion and proliferation, as we showed in vitro. ConclusionOur study support Siglec-15 as a potential predictor for LSCC prognosis and an attractive target for LSCC immunotherapy.

Potential diagnostic markers and therapeutic targets for periodontitis and Alzheimer's disease based on bioinformatics analysis.

As a chronic inflammatory disease, periodontitis threatens oral health and is a risk factor for Alzheimer's disease (AD). There is growing evidence that these two diseases are closely related. However, current research is still incomplete in understanding the common genes and common mechanisms between periodontitis and AD. In this study, we aimed to identify common genes in periodontitis and AD and analyze the relationship between crucial genes and immune cells to provide new therapeutic targets for clinical treatment. We evaluated differentially expressed genes (DEGs) specific to periodontitis and AD. Co-expressed genes were identified by obtaining gene expression profile data from the Gene Expression Omnibus (GEO) database. Using the STRING database, protein-protein interaction (PPI) networks were constructed, and essential genes were identified. We also used four algorithms to identify critical genes and constructed regulatory networks. The association of crucial genes with immune cells and potential therapeutic effects was also assessed. PDGFRB, VCAN, TIMP1, CHL1, EFEMP2, and IGFBP5 were obtained as crucial common genes. Immune infiltration analysis showed that Natural killer cells and Myeloid-derived suppressor cells were significantly differentially expressed in patients with PD and AD compared with the normal group. FOXC1 and GATA2 are important TFs for PD and AD. MiR-23a, miR-23b, miR-23a, and miR-23b were associated with AD and PD. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecule and drug-target interactions. This study reveals commonalities in common hub genes and immune infiltration between periodontitis and AD, and the analysis of six hub genes and immune cells may provide new insights into potential therapeutic directions for the pathogenesis of periodontitis complicated by AD.

Integrated Bioinformatics Analysis and Target Drug Prediction of Inflammatory Bowel Disease Co-existent Diabetes Mellitus.

Inflammatory bowel disease (IBD) has become one of the public problems worldwide and its incidence rate is increasing year by year. Its concomitant disease i.e. diabetes mellitus (DM) has attracted more and more attention due to DM altering the progression of IBD and leading to long periods of intermittent recurrence and deterioration. The common mechanism and potential target drug of IBD with comorbid chronic conditions of DM were explored. Gene expression profile data were downloaded from the Gene Expression Omnibus (GEO) public database. The differentially expressed genes (DEGs) were identified by R software. GO annotation and pathway enrichment were performed, a protein-protein interaction (PPI) network was constructed, associated lncRNAs were predicted and drug prediction targeting key genes was made. Additionally, the regulatory network among core genes, associated pathways, and predicted lncRNA in IBD with coexistent DM were visualized. We identified the critical gene MMP3 with lncRNA CDKN2BAS involved in the PPAR pathway, which uncovered the underlying regulatory mechanism of IBD with coexistent DM. We also predicted the potential therapeutic compound ZINC05905909 acting on MMP3. Our findings revealed the regulatory mechanism chain of critical gene MMP3, lncRNA CDKN2BAS, and PPAR pathway and provided potential therapeutic compound ZINC05905909 for drug therapy to treat comorbid IBD DM.

Exercise alleviates diabetic kidney disease through PPAR��-CPT1�� pathway-dependent fatty acid ��-oxidation

<p>Diabetic kidney disease (DKD), a severe diabetic complication affecting approximately one-third of diabetic patients, is the leading cause of end-stage chronic kidney disease. The benefits of regular exercise for patients with DKD have been well documented, particularly in overweight patients with DKD. However, the underlying mechanisms are incompletely understood. The present study demonstrates that exercise improves kidney function in diabetic <i>db/db</i> mice through activating PPAR��-mediated fatty acid ��-oxidation (FAO). Twelve-week treadmill running exercise improved kidney function in <i>db/db</i> mice. Metabolomics and transcriptomics profiling analysis collectively revealed that dysregulation of FAO in <i>db/db</i> mice was largely corrected by running exercise. KEGG pathway enrichment revealed that PPAR pathway, a critical signalling cascade in FAO and exercise, is involved in DKD, suggesting PPAR�� activation protects renal function through promoting FAO. Which was confirmed by improved renal function and enhanced FAO in <i>db/db</i> mice treated with PPAR�� agonist GW501516. Oppositely, PPAR��-specific inhibitor GSK0660 abolished exercise-mediated improvement in renal function and FAO. In combining with gene expression profiling data we identified CPT1��, the rate-limiting enzyme in fatty acid oxidation, was suppressed in the kidney of diabetic mice and reversed by exercise and the PPAR�� agonist GW501516. The findings hint at a potential role for the PPAR��-CPT1�� pathway in exercise-induced improvements in diabetic renal function and may warrant further exploration of the PPAR��-CPT1�� pathway as a therapeutic target in DKD.</p>

Advances in genomics and genome editing for improving strawberry (Fragaria ×ananassa).

The cultivated strawberry, Fragaria ×ananassa, is a recently domesticated fruit species of economic interest worldwide. As such, there is significant interest in continuous varietal improvement. Genomics-assisted improvement, including the use of DNA markers and genomic selection have facilitated significant improvements of numerous key traits during strawberry breeding. CRISPR/Cas-mediated genome editing allows targeted mutations and precision nucleotide substitutions in the target genome, revolutionizing functional genomics and crop improvement. Genome editing is beginning to gain traction in the more challenging polyploid crops, including allo-octoploid strawberry. The release of high-quality reference genomes and comprehensive subgenome-specific genotyping and gene expression profiling data in octoploid strawberry will lead to a surge in trait discovery and modification by using CRISPR/Cas. Genome editing has already been successfully applied for modification of several strawberry genes, including anthocyanin content, fruit firmness and tolerance to post-harvest disease. However, reports on many other important breeding characteristics associated with fruit quality and production are still lacking, indicating a need for streamlined genome editing approaches and tools in Fragaria ×ananassa. In this review, we present an overview of the latest advancements in knowledge and breeding efforts involving CRISPR/Cas genome editing for the enhancement of strawberry varieties. Furthermore, we explore potential applications of this technology for improving other Rosaceous plant species.

S-adenosylmethionine biosynthesis is a targetable metabolic vulnerability in multiple myeloma.

Multiple myeloma (MM) is the second most prevalent hematologic malignancy and is incurable because of the inevitable development of drug resistance. Methionine adenosyltransferase 2α (MAT2A) is the primary producer of the methyl donor S-adenosylmethionine (SAM) and several studies have documented MAT2A deregulation in different solid cancers. As the role of MAT2A in MM has not been investigated yet, the aim of this study was to clarify the potential role and underlying molecular mechanisms of MAT2A in MM, exploring new therapeutic options to overcome drug resistance. By analyzing publicly available gene expression profiling data, MAT2A was found to be more highly expressed in patient-derived myeloma cells than in normal bone marrow plasma cells. The expression of MAT2A correlated with an unfavorable prognosis in relapsed patients. MAT2A inhibition in MM cells led to a reduction in intracellular SAM levels, which resulted in impaired cell viability and proliferation, and induction of apoptosis. Further mechanistic investigation demonstrated that MAT2A inhibition inactivated the mTOR-4EBP1 pathway, accompanied by a decrease in protein synthesis. MAT2A targeting in vivo with the small molecule compound FIDAS-5 was able to significantly reduce tumor burden in the 5TGM1 model. Finally, we found that MAT2A inhibition can synergistically enhance the anti-MM effect of the standard-of-care agent bortezomib on both MM cell lines and primary human CD138+ MM cells. In summary, we demonstrate that MAT2A inhibition reduces MM cell proliferation and survival by inhibiting mTOR-mediated protein synthesis. Moreover, our findings suggest that the MAT2A inhibitor FIDAS-5 could be a novel compound to improve bortezomib-based treatment of MM.

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay.

Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.