Exploring the microRNA-mRNA regulatory network associated with solasonine in bladder cancer.

Background: Solasonine has been demonstrated to exert an inhibitory effect on bladder cancer (BC), but the potential mechanisms remain unclear. Therefore, the aim of this study is to explore the association between microRNAs (miRNAs)-mediated regulation and the anti-tumor activities of solasonine in BC. Methods: MiRNA sequencing was performed to identify the differentially expressed microRNAs (DE-miRNAs) associated with solasonine in BC cells. Functional enrichment analyses of the DE-miRNAs activated and inhibited by solasonine were then conducted. The DE-miRNAs with prognostic value for BC and those differentially expressed in the BC samples were subsequently identified as the hub DE-miRNAs. After identifying the messenger RNAs (mRNAs) that were targeted by the hub DE-miRNAs and those differentially expressed in the BC samples, a protein-protein interaction analysis was performed to identify the core downstream genes, which were then used to construct a solasonine-miRNA-mRNA regulatory network. Results: A total of 27 activated and 19 inhibited solasonine-mediated DE-miRNAs were identified that were found to be associated with several tumor-related biological functions and pathways. After integrating the results of the survival analysis and expression assessment, the following nine hub DE-miRNAs were identified: hsa-miR-127-3p, hsa-miR-450b-5p, hsa-miR-99a-5p, hsa-miR-197-3p, hsa-miR-423-3p, hsa-miR-4326, hsa-miR-625-3p, hsa-miR-625-5p, and hsa-miR-92a-3p. The DE-mRNAs targeted by the hub DE-miRNAs were predicted, and 30 core downstream genes were used to construct the solasonine-miRNA-mRNA regulatory network. miR-450b-5p was shown to be associated with the most mRNAs in this network, which suggests that it plays a crucial role in the solasonine-mediated anti-BC effect. Conclusions: A regulatory network, including solasonine, miRNAs, and mRNAs related to BC, was constructed. This network provides extensive insights into the molecular regulatory mechanisms that underlie the anti-cancer efficacy of solasonine in BC.

Machine learning approach to screen new diagnostic features of adamantinomatous craniopharyngioma and explore personalised treatment strategies.

Background: Adamantinoma craniopharyngioma (ACP) is a non-malignant tumour of unknown pathogenesis that frequently occurs in children and has malignant potential. The main treatment options are currently surgical resection and radiotherapy. These treatments can lead to serious complications that greatly affect the overall survival and quality of life of patients. It is therefore important to use bioinformatics to explore the mechanisms of ACP development and progression and to identify new molecules. Methods: Sequencing data of ACP was downloaded from the comprehensive gene expression database for differentially expressed gene identification and visualized by Gene Ontology, Kyoto Gene, and gene set enrichment analyses (GSEAs). Weighted correlation network analysis was used to identify the genes most strongly associated with ACP. GSE94349 was used as the training set and five diagnostic markers were screened using machine learning algorithms to assess diagnostic accuracy using receiver operating characteristic (ROC) curves, while GSE68015 was used as the validation set for verification. Results: Type I cytoskeletal 15 (KRT15), Follicular dendritic cell secreted peptide (FDCSP), Rho-related GTP-binding protein RhoC (RHOC), Modulates negatively TGFB1 signaling in keratinocytes (CD109), and type II cytoskeletal 6A (KRT6A) (area under their receiver operating characteristic curves is 1 for both the training and validation sets), Nomograms constructed using these five markers can predict progression of ACP patients. Whereas ACP tissues with activated T-cell surface glycoprotein CD4, Gamma delta T cells, eosinophils and regulatory T cells were expressed at higher levels than in normal tissues, which may contribute to the pathogenesis of ACP. According to the analysis of the CellMiner database (Tumor cell and drug related database tools), high CD109 levels showed significant drug sensitivity to Dexrazoxane, which has the potential to be a therapeutic agent for ACP. Conclusions: Our findings extend understandings of the molecular immune mechanisms of ACP and suggest possible biomarkers for the targeted and precise treatment of ACP.

Healing mechanism of diabetic foot ulcers using single-cell RNA-sequencing.

Background: Diabetic foot ulcer (DFU) is one of the common and severe complications in diabetic patients, mainly caused by the interaction of various factors such as peripheral neuropathy, peripheral vascular disease, and infection. Moreover, vascular damage, disorder of tissue cells, decreased expression level of neurotrophic factor, and decreased growth factor caused by long-term exposure to a high glucose environment can also lead to prolonged or incomplete wound healing. This imposes a tremendous financial burden on the patients' family and society. Although various innovative techniques and drugs have been developed to treat DFU, the therapeutic effect is still unsatisfactory. Methods: We filtered and downloaded the single-cell dataset of diabetic patients from the Gene Expression Omnibus (GEO) website and used the Seurat package in R for creation of single-cell objects, integration, control of quality, clustering, cell type identification, differential gene analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and intercellular communication analysis. Results: Diabetic healing-related differentially expressed gene (DEG) analysis showed that there were 1,948 differential genes between tissue stem cells in healing and non-healing wounds, of which 1,198 genes were up-regulated and 685 genes were down-regulated. The results of GO functional enrichment analysis in tissue stem cells showed that they were closely related to wound healing. The CCL2-ACKR1 signaling pathway activity in tissue stem cells influenced the biological activity of endothelial cell subpopulation, which ultimately promoted the healing of DFU wounds. Conclusions: The CCL2-ACKR1 axis is closely associated with DFU healing.

Investigation of hub ferroptosis-related genes and the immune landscape in recurrent pregnancy loss and unexplained infertility using bioinformatics analysis.

Background: Recurrent pregnancy loss (RPL) and unexplained infertility (UI) are common pregnancy disorders that affect women's physical and mental health and lack effective treatment. Endometrial factors are one factor that leads to RPL. The latest research indicates that ferroptosis and immunity are closely related to the normal physiological function of the endometrium and may play a role in the pathogenesis of RPL and UI. Therefore, the present study analyzed the relationship between ferroptosis genes and immune infiltration in RPL and UI. Methods: We downloaded the GSE165004 dataset and analyzed differences in ferroptosis-related genes (FRGs) between RPL and UI patients and healthy controls. Hub differentially expressed ferroptosis-related genes (DE-FRGs) were screened using the LASSO algorithm, the SVM-RFE algorithm and the protein-protein interaction (PPI) network. Differences in immune infiltration between healthy endometrium and RPL and UI endometrium was analyzed, and the relationship between hub DE-FRGs and immune cell infiltration was examined. Results: We extracted 409 FRGs and identified 36 up-regulated and 32 down-regulated DE-FRGs in RPL and UI. Twenty-one genes were screened using the LASSO regression algorithm, and 17 genes were screened using the SVM-RFE algorithm. We intersected the LASSO genes, SVM-RFE genes and PPI network proteins to obtain 5 hub DE-FRGs. Gene set enrichment analysis (GSEA) functional enrichment analysis results indicated that the cytokine-cytokine receptor interaction signaling pathway was the common pathway for hub DE-FRGs. T follicular helper cells were highly infiltrated in RPL and UI, and M1 and M2 macrophages were highly infiltrated. The expression levels of MAPK1 and RELA positively correlated with T follicular helper cells. Conclusions: Ferroptosis-related genes may disrupt endometrial functions and signaling pathways and lead to the occurrence of RPL and UI.

Bioinformatics combined with clinical data to analyze clinical characteristics and prognosis in patients with HER2 low expression breast cancer.

Background: Human epidermal growth factor receptor 2 (HER2) is a landmark protein in determining the targeted treatment of breast cancer (BC). However, the latest research shows that different intensity of HER2 protein expression levels in BC leads to different clinical characteristics, treatment, and prognosis, especially in HER2 low expression patients. Therefore, this study intends to analyze and compare the clinicopathologic features and prognosis of BC patients with low and zero HER2 expression from The Cancer Genome Atlas (TCGA) database and the data collected by our center. Methods: First, the BC dataset was downloaded from TCGA database, including 345 eligible and with complete clinical information BC patients, to compare the difference between HER2 low expression groups and HER2 zero expression groups and their correlation with estrogen receptor (ER) and progesterone receptor (PR) expression. Then, the clinicopathological data and follow-up of 405 patients with HER2 low expression and HER2 zero expression diagnosed with BC admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities (YJMU) from January 2017 to December 2021 were collected to verify the consistency of the results of the two data sets. Results: Both the clinical samples and the TCGA data showed that the ER and PR rates were higher in the HER2 low expression group compared with the HER2 zero expression group. There were no significant differences in tumor size, lymph node metastasis, distant metastasis, and disease-free survival (DFS). In addition, the data analysis of 405 clinical samples also showed that the HER2 low expression group had a lower 3-year recurrence or metastasis rate compared with the HER2 zero expression group. Conclusions: Compared with HER2 zero expression, HER2 low patients express more ER and PR, and have less short-term recurrence and metastasis, but there is no obvious difference in DFS between the two groups.

Network pharmacological analysis of active components of Xiaoliu decoction in the treatment of glioblastoma multiforme.

Background: Glioblastoma multiforme (GBM) is the most aggressive primary nervous system brain tumor. There is still a lack of effective methods to control its progression and recurrence in clinical treatment. It is clinically found that Xiaoliu Decoction (XLD) has the effect of treating brain tumors and preventing tumor recurrence. However, its mechanism is still unclear. Methods: Search the Traditional Chinese Medicine System Pharmacology Database (TCSMP) for efficient substances for the treatment of XLD in the treatment of GBM, and target the targeted genes of the effective ingredients to construct a network. At the same time, download GBM-related gene expression data from the TCGA and GTEX databases, screen differential expression bases, and establish a drug target disease network. Through bioinformatics analysis, the target genes and shared genes of the selected Chinese medicines are analyzed. Finally, molecular docking was performed to further clarify the possibility of XLD in multiple GBMs. Results: We screened 894 differentially expressed genes in GBM, 230 XLD active ingredients and 169 predicted targets of its active compounds, of which 19 target genes are related to the differential expression of GBM. Bioinformatics analysis shows that these targets are closely related to cell proliferation, cell cycle regulation, and DNA synthesis. Finally, through molecular docking, it was further confirmed that Tanshinone IIA, the active ingredient of XLD, was tightly bound to key proteins. Conclusion: To sum up, the results of this study suggest that the mechanism of XLD in the treatment of GBM involves multiple targets and signal pathways related to tumorigenesis and development. This study not only provides a new theoretical basis for the treatment of glioblastoma multiforme with traditional Chinese medicine, but also provides a new idea for the research and development of targeted drugs for the treatment of glioblastoma multiforme.

Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis.

Background: Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients. However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients. Methods: The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs). Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model ("glm" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed. Results: Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells. Conclusions: This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.

Bioinformatics analysis combined with experiments to verify potential autophagy genes in wound healing.

Background: The skin is the most exposed tissue and has multiple functions. Wound healing is a major medical problem due to trauma and pathophysiological alterations suffered by patients. The aim of the present study was to search for potential autophagy genes associated with wound healing. Methods: The GSE168760 dataset was obtained from the Gene Expression Omnibus (GEO) database, and sequencing results were obtained for 14 patient traumas at different time periods. Differentially expressed gene (DEG) analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Immune cell and correlation analysis were performed for autophagy genes and DEGs. Peripheral blood was collected from patients at different time periods and Western blot (WB) assay was performed to verify autophagy genes. Results: A total of 226 DEGs were screened on days 0, 7, and 14, of which 162 genes were upregulated and 64 genes were downregulated. Of these, eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2) and retinoblastoma 1 (RB1) were autophagy-associated genes. The DEGs were mainly involved in response to virus, cellular response to type I interferon Epstein-Barr virus infection, human papillomavirus infection, ribosome, hepatitis B and RIG-I-like. EIF2AK2 and RB1 showed positive correlation with some of the immune cells, and WB showed that EIF2AK2 and RB1 proteins were significantly increased with wound healing. Conclusions: The comprehensive analysis of GEO data in the present study provides a new theoretical basis for the molecular pathogenesis of trauma healing and potential autophagy-related therapeutic targets.

Risk score model of autophagy-related genes in osteosarcoma.

Background: Osteosarcoma (OS) is a common pediatric malignancy with high mortality and disability rates. Autophagy is an essential process in regulating the apoptosis and invasion of tumor cells, so constructing a risk score model of OS autophagy-related genes (ARGs) will bring benefit to the evaluation of both treatment and prognosis. Methods: We downloaded a dataset of OS from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) database, and found OS-related ARGs through the Human Autophagy Database (HADb). Five hub ARGs (CCL2, AMBRA1, VEGFA, MYC and EGFR) were obtained using a multivariate Cox regression model. We then generated the risk scores and constructed a prediction model. Another dataset obtained from the Gene Expression Omnibus (GEO) was used to test accuracy and validity. The role of immune cell infiltration was systematically explored, and prediction of response to targeted drugs was assessed. Immunohistochemistry was carried out to verify the expression of the key ARGs. Results: Based on the five hub ARGs, we established a risk score model related to OS. High accuracy and validity were demonstrated by datasets downloaded from the GEO. The five ARGs played a role in the PI3K and MAPK pathways. Results from targeted drug sensitivity analyses were consistent with pathway analyses. Immunohistochemistry demonstrated that the expression differences of the five ARGs were significant between the OS group and the paracancerous group. Conclusions: We constructed a risk score model related to autophagy of OS, explored the diagnostic value of ARGs, and present possible therapeutic targets.

Autophagy-related genes are potential diagnostic biomarkers for dermatomyositis.

Background: Dermatomyositis (DM) is an autoimmune disease mainly diagnosed by its symptoms and a physical examination, with only some subtypes of DM showing clear molecular changes. To date, few biomarkers have been identified to assess DM progression. Autophagy-related genes have been significantly correlated with inflammation, several types of autoimmune diseases, and the immune response, but few studies have explored the role of autophagy-related genes in DM. Therefore, this study aimed to investigate the roles of autophagy-related genes in DM. Methods: We collected three datasets of dermatomyositis-related transcriptome from the Gene Expression Omnibus (GEO) database: GSE1551, GSE46239, and GSE143323 and analyzed the differentially expressed genes (DEGs). We also conducted functional enrichment analyses with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. To explore whether the autophagy-related genes were differentially expressed in DM compared with normal samples, we performed an intersection between the DEGs and autophagy-related genes obtained from the Human Autophagy Database (HADb, http://www.autophagy.lu/). Finally, we used selected autophagy-related genes as biomarkers for diagnosing and analyzing the correlation with immune cell infiltration. Results: Our results showed that 143 genes were upregulated, and 14 were downregulated in the DM samples compared with healthy samples. The functional enrichment analysis revealed that these DEGs played a significant role in the type I interferon signaling pathway, cytokine activity, chemokine activity, double-stranded RNA binding, and blood microparticles. The intersection results identified CCL2, CDKN1A, FOS, MYC, and TNFSF10 as the primary autophagy-related genes in DM. All showed significantly increased expressions in DM samples compared with healthy samples. We were also curious to investigate immune cell infiltration in DM. Our results showed that the selected autophagy-related genes significantly influenced the infiltration of multiple immune cells, such as B cells, macrophages, and natural killer cells. Finally, we assessed the diagnostic sensitivity of CCL2, CDKN1A, FOS, MYC, and TNFSF10 for DM. The results showed the area under the curve (AUC) values of the ROC were 0.855, 0.889, 0.744, 0.826, and 0.816, respectively. The combined genes' diagnostic AUC value was 0.951. Conclusions: CCL2, CDKN1A, FOS, MYC, and TNFSF10 are potential diagnostic biomarkers for DM.

IL-1B can serve as a healing process and is a critical regulator of diabetic foot ulcer.

Background: Diabetic foot ulcer (DFU) is the main cause of disability in diabetic patients. However, the molecular changes underlying the occurrence and progression of DFU remain unclear. We conducted this study to examine gene alterations in different DFU patients. Methods: GSE143735 and GSE134431 transcriptome data sets were acquired from the Gene Expression Omnibus database, and differential expression analyses of the genes in these data sets were performed. A functional enrichment analysis of the differentially expressed genes (DEGs) was performed using clusterProfiler package in R. To examine the correlations between DEGs and significant immune-related genes, we identified the intersecting ulcer-related DEGs, healing-related DEGs, and immune-related DEGs. Finally, we further investigate the relationship between the selected genes with immune cell regulation via a single-sample gene set enrichment analysis, and the infiltration of 28 immune cells in common diabetes samples, unhealed DFU samples, and healed samples DFU were compared. Results: We found 238 upregulated genes and 207 downregulated genes in the diabetic foot (DF) patients with ulcers compared to the DF patients without ulcers, and 74 upregulated genes and 28 downregulated genes in the healed samples compared to the unhealed samples. To examine the main biological functions, we conducted a functional enrichment analysis. The results showed that the biological functions of functional enrichment analysis included neutrophil degranulation, leukocyte chemotaxis, myeloid leukocyte migration, phagosome, cytokine-cytokine receptor interaction, and the chemokine signaling pathway. Interleukin (IL)-1B was more highly expressed in patients with ulcers and healed DFU patients than those without ulcers and unhealed DFU patients. Finally, the immune cell abundance difference results showed that activated cluster of differentiation (CD)8 T cells, central memory CD8 T cells, T follicular helper cells, myeloid-derived suppressor cells, natural killer T cells and monocytes were more highly infiltrated in normal diabetes patients and healed DFU patients than unhealed DFU patients. However, no difference was found between DF patients with and without ulcers. Conclusions: IL-1B is an inflammation gene that can be used to assess and regulate DFU progression.

Down regulated oncogene KIF2C inhibits growth, invasion, and metastasis of hepatocellular carcinoma through the Ras/MAPK signaling pathway and epithelial-to-mesenchymal transition.

Background: Hepatocellular carcinoma (HCC) is the leading cause of cancer death. Kinesin family member 2C (KIF2C) has been shown as oncogene in a variety of tumors. However, its role in HCC remains unclear. Methods: In this study, the expression level of KIF2C in HCC was detected by immunohistochemical staining and RT-PCR, and verified by Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine database. A curve was established to evaluate the diagnostic efficiency of KIF2C. The effect of KIF2C on HCC was investigated by flow cytometry, Cell Counting Kit-8, Transwell, and the wound-healing assay. We explored the underlying mechanism through epithelial-to-mesenchymal transition (EMT) and transcriptome sequences analysis. Results: KIF2C was overexpression in HCC tissue and related to neoplasm histologic grade (P<0.001), pathology stage (P=0.001), and a dismal prognosis (overall, recurrence-free, and disease-free survival). The diagnostic efficacy of KIF2C was >90% in diagnosing HCC. The HCC cell function experiments showed that KIF2C promoted HCC cell proliferation, migration, invasion, and an accelerated cell cycle, and inhibited apoptosis. Based on western blot analysis and RT-PCR, we found that KIF2C promoted HCC invasion and metastasis through activation of the EMT. Based on transcriptome sequences, we showed that KIF2C promoted HCC through the Ras/MAPK and PI3K/Akt signaling pathway. Conclusions: KIF2C was found to promote the progression of HCC and is anticipated to serve as a biomarker for HCC diagnosis, prognosis, and targeted therapy.

Constructing a competing endogenous RNA network for osteoarthritis.

Background: Osteoarthritis (OA) is one of the most common diseases in elderly people; however, the correlation between molecular alterations and the occurrence and progression of OA are still not well understood. We conducted this study to investigate the molecular changes in OA via the competing endogenous ribonucleic acid (ceRNA) network. Methods: We downloaded the messenger RNA (mRNA) data set, GSE48556, the microRNA (miRNA) data set, GSE105027, and the long non-coding (lncRNA) data set, GSE126963 from the Gene Expression Omnibus (GEO) database, and examined the differentially expressed genes (DEGs) in these data sets. Further, we constructed a ceRNA network of the differentially expressed miRNAs, mRNAs, and lncRNAs. To determine the biological functions of the ceRNA network, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Finally, we conducted an immune cell infiltration analysisusing single-sample gene set enrichment analysis to examine the abundance of immune cells in healthy and OA patients, and compared the infiltration of 28 immune cells between the healthy and OA samples. We also analyzed the relationship between the abundance of immune cells and mRNA expression levels in the ceRNA network. Results: Ultimately hsa-mir-425-3p, dual specificity phosphatase 1, and 24 lncRNAs were identified in the ceRNA network. The functional enrichment analyses showed that these lncRNAs, miRNAs, and mRNAs are involved in various significant biological process, such as the regulation of leukocyte migration, Mitogen-Activated Protein (MAP) kinase tyrosine/serine/threonine phosphatase activity, the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and osteoclast differentiation, and can also have a strong effect on immune cell infiltration. Conclusions: The dual-specificity phosphatase 1-specific ceRNA network can be used as a diagnostic tool to assess the progression of OA patients.

Autophagy related long non-coding RNA and breast cancer prognosis analysis and prognostic risk model establishment.

Background: The role of autophagy-related long-stranded non-coding RNA (lncRNA) in breast cancer (BRCA) is unclear. We proposed to screen autophagy-related lncRNAs in BRCA and construct a prognostic risk assessment model to explore prognostic correlates. Methods: We extracted BRCA lncRNAs from The Cancer Genome Atlas (TCGA) database and autophagy-related genes from the Human Autophagy Database (HADb), to screen for autophagy-related lncRNA pairs (ARLP) in BRCA. Single-factor Cox regression analysis and multi-factor Cox regression analysis were used to screen lncRNAs associated with BRCA prognosis, and risk models were established. We divided BRCA patients into high-risk and low-risk groups based on median risk scores. The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to calculate the abundance of 28 immune cells in the TCGA-BRCA cohort and to analyze the relationship between the risk score and the level of immune cell infiltration by ARLP characteristics. Results: Univariate Cox regression results showed that 42 ARLPs were significantly associated with overall survival (OS) in BRCA patients. Further multifactorial analysis showed that a total of 11 lncRNAs, including SEMA3B-AS1, ST7-AS1, AL136295.7, AC090912.1, LINC01871, AL136531.1, AC024361.1, OTUD6B-AS1, LINC01786, AL122010.1, and MAPT-AS1, were prognostically independent influencers of BRCA. The risk model developed was further validated as a new independent prognostic factor for BRCA patients by Kaplan-Meier (KM) analysis, univariate and multivariate Cox regression analysis to calculate the risk score. In addition, the results of the relationship between risk score and immune infiltration showed that low risk score was associated with T-lymphocyte subpopulation. Conclusions: Our study suggested that a risk model consisting of 11 autophagy-related lncRNAs can be used to assess the prognosis of BRCA patients.

Identification of solute carrier family genes related to the prognosis and tumor-infiltrating immune cells of pancreatic ductal adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC) has persisted as one of the worst prognostic tumors with a 5-year survival rate of lower than 6%. Although many studies have investigated PDAC, new biomarkers are required to ensure early diagnosis and predict the prognosis of PDAC. Methods: In this study, we used bioinformatics methods to evaluate differences in the expression of solute carrier (SLC) family genes in tumors and non-tumors. A Kaplan-Meier analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazards regression analysis were used to evaluate the relationship between SLC genes and prognosis using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic signature was constructed depending on the risk score to assess the impact of multiple genes on the prognosis, receiver operating characteristic (ROC) curves and forest plot was constructed to assess the ability to predict the prognosis and effects of clinical variables in both high- and low-risk groups. Tumor-infiltrating immune cells were evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) in both high- and low-risk groups. Results: In 32 SLC genes, 9 were significantly associated with the OS after LASSO analysis. SLC19A3 (P=0.007), SLC25A39 (P=0.027), SLC39A11 (P=0.043) were significantly associated with prognosis and included into the prognostic model. CIBERSORT demonstrated that memory B cells (P=0.004), naive B cells (P=0.007), CD8 T cells (P=0.003), activated memory CD4 T cells (P=0.004), and activated NK cells (P=0.019) were significantly higher in the low-risk group. Gene set enrichment analysis (GSEA) showed that potential molecular mechanisms enriched in MYC and p53 signaling pathways. Conclusions: SLC19A3, SLC25A35, and SLC39A11 were significantly relative to the prognosis of PDAC and changed the tumor microenvironment, as well as the MYC and p53 signaling pathways. The SLC19A3 gene may represent a new tumor suppressor in PDAC.

Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia.

BACKGROUND: The tumor microenvironment (TME) has an essential role in tumorigenesis, progression, and therapeutic response in many cancers. Currently, the role of TME in acute myeloid leukemia (AML) is unclear. This study investigated the correlation between immune-related genes and prognosis in AML patients. METHODS: Transcriptome RNA-Seq data for 151 AML samples were downloaded from TCGA database (https://portal.gdc.cancer.gov/), and the immune related genes (irgs) were selected from Immport database. Bioinformatics screening was used to identify irgs for AML, and genes with a critical role in the prognosis of AML were selected for further analysis. To confirm the prognostic role of irgs in AML, we undertook protein-protein interaction (PPI) network analysis of the top 30 interacting genes. We then investigated associations between immune cell infiltration and prognosis in AML patients. Immunohistochemistry was used to validate protein expression levels between AML and normal bone marrow samples. Analysis of the drug sensitivity of the selected gene was then performed. RESULTS: The integrin lymphocyte function-associated antigen 1 (CD11A/CD18; ITGAL/ITGB2) was identified as the key immune-related gene that significantly influenced prognosis in AML patients. Overexpression of ITGB2 indicated poor prognosis in AML patients (P=0.007). Risk modeling indicated that a high-risk score led to poor outcomes (P=3.076e-08) in AML patients. The risk model showed accuracy for predicting prognosis in AML patients, with area under curve (AUC) at 1 year, 0.816; AUC at 3 years, 0.82; and AUC at 5 years, 0.875. In addition, we found that ITGB2 had a powerful influence on immune cell infiltration into AML TME. The results of immunohistochemistry showed that AML patients had significantly higher ITGB2 protein expression than normal samples. The AML patients were divided into 2 groups based on ITGB2 risk scores. Drug sensitivity test results indicated that the high-risk group was sensitive to cytarabine, axitinib, bosutinib, and docetaxel, but resistant to cisplatin and bortezomib. CONCLUSIONS: In the present study, we found that ITGB2 may be able to serve as a biomarker for assessing prognosis and drug sensitivity in AML patients.

CCR2 and PTPRC are regulators of tumor microenvironment and potential prognostic biomarkers of lung adenocarcinoma.

BACKGROUND: Tumor microenvironment (TME) plays an essential role in lung adenocarcinoma (LUAD) development and metastasis. With the development of TME research, it has been proved that differences in tumor-infiltrating immune cells (TICs) and gene expression profile are related to the prognosis of cancer. The aim of our study was to identify key genes affecting immune state in TME of LUAD. METHODS: The RNA-seq data and clinical characteristics of 594 LUAD patients were downloaded from the TCGA database. ImmuneScore, StromalScore and ESTIMATEScore of each LUAD sample were calculated using ESTIMATE algorithm. Based on the median of different scores, LUAD samples were divided into high and low score groups. Differentially expressed genes (DEGs) between groups were obtained, and univariate Cox regression analysis and protein-protein interaction (PPI) network were used to screen the shared DEGs generating in the intersection analysis. Finally, the CIBORSORT algorithm was performed to calculate the relative contents of TICs for each LUAD sample, and the correlation analysis between TICs and key genes was used to determine the influence of key genes to the TME. RESULTS: In the presented study, we found that three different scores were positively correlated with the prognosis of LUAD patients, and correlation analysis showed the different scores were closely related to tumor progression and metastasis. After performing the intersection analysis, a total of 585 up-regulated and 107 down-regulated DEGs between the high and low score groups were obtained, all of which were enriched in immune-related functions. Having used univariate COX regression analysis and PPI network, the key genes, CCR2 and PTPRC, affecting the immune status of TME and the prognosis of LUAD were acquired. Analysis based on the CIBERSORT algorithm suggested that CCR2 and PTPRC were correlated with a variety of TICs, and closely related to the clinical characteristics of the LUAD patients. CONCLUSIONS: Our research showed that CCR2 and PTPRC may be potential prognostic markers in LUAD, which may affect the function of γδT cells and other immune cells by participating in the regulation of TME immune state.

Atractylodin may induce ferroptosis of human hepatocellular carcinoma cells.

BACKGROUND: It has been reported that atractylodin has a potential antitumor effect. This study aimed to investigate the effects of atractylodin on Huh7 and Hccm hepatocellular carcinoma (HCC) cells and its molecular mechanism. METHODS: Huh7 and Hccm cells were cultured in vitro, and their viability was detected by CCK-8 assay and the half inhibitory concentration (IC50) was calculated. The cells were treated with different concentrations of atractylodin, and the migration and invasion ability of cells was detected by scratch assay and Transwell assay. The cell cycle change and apoptosis rate were detected by flow cytometry. IlluminaHiSeq4000 platform was used for transcriptome sequencing, and the results were analyzed for gene differential expression, gene function, and signal pathway enrichment. Morphological changes of cells were detected by transmission electron microscopy, reactive oxygen species (ROS) levels were detected by DCFH-DA probe, and the expressions of ferroptosis related proteins GPX4, ACSL4, FTL, and TFR1 were detected by Western blot. RESULTS: The results showed that atractylodin could inhibit the proliferation, migration, and invasion of Huh7 and Hccm cells, regulate the cell cycle, and induce cell apoptosis and G1 phase cell cycle arrest. In addition, it could significantly induce the increase of intracellular ROS levels, decrease the expression of GPX4 and FTL proteins, and up-regulate the expression of ACSL4 and TFR1 proteins. CONCLUSIONS: Atractylodin can inhibit the proliferation, migration, and invasion of Huh7 and Hccm liver cancer cells, and induce cell apoptosis and cell cycle arrest. In addition, our results suggest that atractylodin may induce ferroptosis in HCC cells by inhibiting the expression of GPX4 and FTL proteins, and up-regulating the expression of ACSL4 and TFR1 proteins.