T cell senescence: a new perspective on immunotherapy in lung cancer.

T cell senescence is an indication of T cell dysfunction. The ability of senescent T cells to respond to cognate antigens is reduced and they are in the late stage of differentiation and proliferation; therefore, they cannot recognize and eliminate tumor cells in a timely and effective manner, leading to the formation of the suppressive tumor microenvironment. Establishing methods to reverse T cell senescence is particularly important for immunotherapy. Aging exacerbates profound changes in the immune system, leading to increased susceptibility to chronic, infectious, and autoimmune diseases. Patients with malignant lung tumors have impaired immune function with a high risk of recurrence, metastasis, and mortality. Immunotherapy based on PD-1, PD-L1, CTLA-4, and other immune checkpoints is promising for treating lung malignancies. However, T cell senescence can lead to low efficacy or unsuccessful treatment results in some immunotherapies. Efficiently blocking and reversing T cell senescence is a key goal of the enhancement of tumor immunotherapy. This study discusses the characteristics, mechanism, and expression of T cell senescence in malignant lung tumors and the treatment strategies.

Prognostic biomarkers correlated with immune infiltration in non-small cell lung cancer.

Lung cancer is the leading cause of cancer-related mortality in men and women globally. Non-small cell lung cancer (NSCLC) is the most prevalent subtype, accounting for 85-90% of all cancers. Although there have been dramatic advances in therapeutic approaches in recent decades, the recurrence and metastasis rates of NSCLC are as high as 30-40% with the 5-year overall survival rate being less than 15%. Therefore, it is necessary to explore the pathogenesis of NSCLC at the genetic level and identify prognostic biomarkers and novel therapeutic targets. Here, we aimed to identify mutated genes with high frequencies in Chinese NSCLC patients using next-generation sequencing and to investigate their relationships with the tumor mutation burden (TMB) and tumor immune microenvironment. A total of 110 NSCLC patients were enrolled to profile the genetic variations. Mutations in EGFR (62.37%), TP53 (61.29%), LRP1B (13.98%), FAT1 (12.90%), KMT2D (11.83%), CREBBP (10.75%), and RB1 (9.68%) were most prevalent. TP53, LRP1B, KMT2D, and CREBBP mutations were all significantly associated with high TMB (P < 0.05 or P < 0.01). The infiltrating levels of immune cells and immune molecules were enriched significantly in the LRP1B mutation group. LRP1B mutations significantly correlated with stimulating and inhibitory immunoregulators. Gene set enrichment analysis revealed that cell cycle, the Notch signaling pathway, the insulin signaling pathway, and the mTOR signaling pathway are related to LRP1B mutations in the immune system. LRP1B mutations may be of clinical importance in enhancing the anti-tumor immune response and may be a promising biomarker for predicting immunotherapy responsiveness.

Immunogenomic landscape analyses of immune molecule signature-based risk panel for patients with triple-negative breast cancer.

Triple-negative breast cancer (TNBC) presented as high heterogeneous immunogenicity that lacks useful clinical signatures to risk-stratify immune-benefit subtypes. We hypothesized that molecular-based phenotypic characterization of TNBC tumors and their immunity may overcome these challenges. We enrolled 1,145 patients with TNBC for analysis. Through combining algorithm integration analysis and TNBC datasets, a tumor immune risk score (TIRS) panel consisting of 8 potential biomarkers was identified. The TIRS panel represented excellent effectiveness as an independent predictor. High- and low risk stratification of patients was further achieved by TIRS, and significant survival and immune-infiltration pattern differences were found in each cohort, both at the transcriptome and protein levels. Non-negative matrix factorization clustering further identified four different tumor immune microenvironment types (TIMTs), among which TIMT-II was associated with the best prognosis and immune status, whereas TIMT-IV had the opposite effect, TIMT-III was associated with highly unstable genomes, and TIMT-I displayed stem-cell-related characteristics along with high stromal scores and may have extensive enrichment of tumor-associated fibroblasts and vascular cells. In conclusion, our TIRS panel could serve as a robust prognostic signature and provide therapeutic benefits for immunotherapy. Additionally, coordinating four TIMTs may be helpful for clinical decision-making in TNBC patients.

Identifying the Effect of Ursolic Acid Against Triple-Negative Breast Cancer: Coupling Network Pharmacology With Experiments Verification.

Triple negative breast cancer (TNBC) is a subtype of breast cancer with complex heterogeneity, high invasiveness, and long-term poor prognosis. With the development of molecular pathology and molecular genetics, the gene map of TNBC with distinctive biological characteristics has been outlined more clearly. Natural plant extracts such as paclitaxel, vinblastine, colchicine etc., have occupied an important position in the treatment of hormone-independent breast cancer. Ursolic acid (UA), a triterpenoid acid compound derived from apple, pear, loquat leaves, etc., has been reported to be effective in a variety of cancer treatments, but there are few reports on the treatment of TNBC. This study performed comprehensive bioinformatics analysis and in vitro experiments to identify the effect of UA on TNBC treatment and its potential molecular mechanism. Our results showed that UA could not only reduce the proliferation, migration, and invasion in MDA-MB-231 and MDA-MB-468 cell lines with a dose-dependent manner but also induce cell cycle arrest and apoptosis. Meanwhile, we collected the gene expression data GSE45827 and GSE65194 from GEO for comparison between TNBC and normal cell type and obtained 724 DEGs. Subsequently, PLK1 and CCNB1 related to TNBC were screened as the key targets via topological analysis and molecular docking, and gene set enrichment analysis identified the key pathway as the p53 signaling pathway. In addition, quantitative real-time PCR and western blot verified the key genes were PLK1 and CCNB1. In vivo and in vitro experiments showed that UA could inhibit the growth of TNBC cells, and down-regulate the protein expression levels of PLK1 and CCNB1 by mediating p53 signaling pathway. These findings provide strong evidence for UA intervention in TNBC via multi-target therapy.

Tumor Mutation Burden and Immune Invasion Characteristics in Triple Negative Breast Cancer: Genome High-Throughput Data Analysis.

In recent years, the emergence of immunotherapy has provided a new perspective for the treatment and management of triple-negative breast cancer (TNBC). However, the relationship between tumor mutation burden (TMB) and immune infiltration and the prognosis of TNBC remains unclear. In this study, to explore the immunogenicity of TNBC, we divided patients with TNBC into high and low TMB groups based on the somatic mutation data of TNBC in The Cancer Genome Atlas (TCGA), and screened out genes with mutation rate ≥10. Then, Kaplan-Meier survival analysis revealed that the 5-year survival rate of the high TMB group was much higher than that of the low TMB group and the two groups also showed differences in immune cell infiltration. Further exploration found that the FAT3 gene, which displays significant difference and a higher mutation rate between the two groups, is not only significantly related to the prognosis of TNBC patients but also exhibits difference in immune cell infiltration between the wild group and the mutant group of the FAT3 gene. The results of gene set enrichment analysis and drug sensitivity analysis further support the importance of the FAT3 gene in TNBC. This study reveals the characteristics of TMB and immune cell infiltration in triple-negative breast cancer and their relationship with prognosis, to provide new biomarkers and potential treatment options for the future treatment of TNBC. The FAT3 gene, as a risk predictor gene of TNBC, is considered a potential biological target and may provide new insight for the treatment of TNBC.

A review of the biological activity and pharmacology of cryptotanshinone, an important active constituent in Danshen.

Cryptotanshinone (IUPAC name: (R)-1,2,6,7,8,9-hexahydro-1,6,6-trimethyl-phenanthro(1,2-b)furan-10,11-dione), a biologically active constituent extracted from the roots and rhizomes of the plant Salvia miltiorrhiza, has been studied in depth as a medicinally active compound and shown to have efficacy in the treatment of numerous diseases and disorders. In this review, we describe in detail the current status of cryptotanshinone research, including findings relating to the structure, pharmacokinetics, pharmacological activity, and derivatives of this compound. Cryptotanshinoneh as a diverse range of pharmacological effects, including anti-cancer, anti-inflammatory, immune regulatory, neuroprotective, and anti-fibrosis activities. Studies on the molecular mechanisms underlying the activities of cryptotanshinone have established that the JAK2/STAT3, PI3K/AKT, NF-κB, AMPK, and cell cycle pathways are involved in the inhibitory and pro-apoptotic effects of cryptotanshinone on different tumor cell lines, these molecular pathways interact in a coordinated manner to inhibit cell proliferation, migration and invasion,and induce transformation, autophagy, necrosis, and cellular immunity. The anti-inflammatory mechanisms of cryptotanshinone have been found to be associated with the TLR4-MyD88/PI3K/Nrf2 and TLR4-MyD88/NF-κB/MAPK pathways, whereasthe Hedgehog, NF-κB, and Nrf-2/HO-1 pathways are regulated by cryptotanshinone to reduce organ fibrosis, and its inhibitory effects on the PI3K/AKT-eNOS pathway have been linked to neuroprotective effects. Given the potential medicinal utility of cryptotanshinone, further research is needed to verify the efficacy and safety of this compound in clinical use, evaluate its pharmacological activity, and identify molecular targets.

Gene signatures of 6-methyladenine regulators in women with lung adenocarcinoma and development of a risk scoring system: a retrospective study using the cancer genome atlas database.

Although the emergence of new treatments has improved the prognosis of women with lung adenocarcinoma (LUAD), the emergence of drug resistance limits their clinical efficacy. Therefore, there is an urgent need to identify new targets and develop a risk scoring system to evaluate the prognosis of patients. 6-methyladenine (M6A), as the most common methyl modification in RNA modification, its clinicopathological features, diagnosis and prognostic value in lung cancer, especially in LUAD remain to be discussed. We analyzed the clinical and sequencing data of the female LUAD cohort from The Cancer Genome Atlas (TCGA), evaluated the expression profiles of 16 M6A regulation-related genes in the cohort and the relationships between genetic changes and clinical characteristics, developed an M6A-related risk scoring system using Cox analysis. Finally, the copy number variations (CNVs) of the related genes in the samples were analyzed and verified using the cBioPortal platform. Compared with other clinical factors, this risk scoring system showed a higher predictive sensitivity and specificity. The M6A-related risk scoring system developed in this study may help to improve the screening of female patients at high risk of LUAD and provides important theoretical bioinformatics support for evaluating the prognosis of such patients.

An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database.

BACKGROUND: Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer. OBJECTIVE: To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformaticsMETHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses. RESULTS: We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer. CONCLUSIONS: The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.

ADRB1 was identified as a potential biomarker for breast cancer by the co-analysis of tumor mutational burden and immune infiltration.

Breast cancer (BRCA) has traditionally been considered as having poor immunogenicity and is characterized by relatively low tumor mutational burden (TMB). Improving immunogenicity may improve the response to clinical immunotherapy of BRCA. However, the relationship between TMB, immune infiltration, and prognosis in BRCA remains unclear. We aimed to explore their interrelations and potential biomarkers. In this study, based on somatic mutation data of BRCA from The Cancer Genome Atlas (TCGA), patients were categorized into high and low TMB groups utilizing the TMB values. CIBERSOFT algorithm indicated significant infiltration of activated partial immune cells in high TMB group. Besides, ADRB1 had been identified as a prognosis-related immune gene in the mutant genes by the combination of the ImmPort database and the univariate Cox analysis. ADRB1 mutation was associated with lower TMB and manifested a satisfactory clinical prognosis. Various database applications (Gene Set Enrichment Analysis, Tumor IMmune Estimation Resource, Connectivity Map, KnockTF) supported the selection of treatment strategies targeting ADRB1. In conclusion, TMB was not an independent prognostic factor for BRCA and high TMB was more likely to activate a partial immune response. ADRB1 was identified as a potential biomarker and may provide new insights for co-therapy of BRCA.

Target Analysis and Mechanism of Podophyllotoxin in the Treatment of Triple-Negative Breast Cancer.

BACKGROUND: As the original compound of many podophyllotoxin derivatives, podophyllotoxin has a beneficial antitumor effect. The mechanism of podophyllotoxin activity in triple-negative breast cancer still needs to be explored. METHODS: We used cell proliferation assay, scratch and transwell experiments, and cell cycle and apoptosis analyses to observe the intervention effect of podophyllotoxin on breast cancer. Furthermore, we analyzed the differences between GSE31448, GSE65194, and GSE45827 in the Gene Expression Omnibus database (GEO) and explored the differential genes using a STRING database. Centiscape2.2, MCODE cluster analysis and KEGG pathway analysis were used to identify the most significant gene differences. Next, we utilized BATMAN-TCM and TCMSP databases for further screening to identify key genes. Finally, quantitative RT-PCR (qRT-PCR) and Western blotting were performed to detect the expression of key targets. RESULTS: Our research confirmed that podophyllotoxin could not only inhibit the migration and invasion of triple-negative breast cancer but also affect the cell cycle and induce apoptosis. In total, 566 differential genes were obtained by using the GEO database. After topological network analysis, cluster analysis, and molecular docking screening, we finally identified PLK1, CCDC20, and CDK1 as key target genes. The results of the qRT-PCR assay showed that the mRNA levels of PLK1, CDC20, and CDK1 decreased, while the expression of upstream P53 increased, after drug induction. The Gene Set Enrichment Analysis (GSEA) and conetwork analysis showed that PLK1 is a more critical regulatory factor. Further Western blotting analysis revealed that there was a negative regulatory relationship between the key gene PLK1 and P53 on the protein level. The results were presented as the mean ± standard deviation of triplicate experiments and P<0.05 was considered to indicate a statistically significant difference. CONCLUSION: Podophyllotoxin has an intervention effect on the development of triple-negative breast cancer. The expression of PLK1, CDC20, and CDK1 in the cell cycle pathway is inhibited by regulating P53. Our research shows that natural drugs inhibit tumor activity by regulating the expression of cyclins, and the combination of natural drugs and modern extensive database analysis has a wide range of potential applications in the development of antitumor therapies.

Construction and Analysis of Competing Endogenous RNA Networks for Breast Cancer Based on TCGA Dataset.

BACKGROUND: Long noncoding RNAs (lncRNAs) act as competing endogenous RNAs for microRNAs in cancer metastasis. However, the roles of lncRNA-mediated competing endogenous RNA (ceRNA) networks for breast cancer (BC) are still unclear. Material and Methods. The expression profiles of mRNAs, lncRNAs, and miRNAs with BC were extracted from The Cancer Genome Atlas database. Weighted gene coexpression network analysis was conducted to extract differentially expressed mRNAs (DEmRNAs) that might be core genes. Through miRWalk, TargetScan, and miRDB to predict the target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network with BC was constructed. The survival possibilities of mRNAs, miRNAs, and lncRNAs for patients with BC were determined by Kaplan-Meier survival curves and Oncomine. RESULTS: We identified 2134 DEmRNAs, 1059 differentially expressed lncRNAs (DElncRNAs), and 86 differentially expressed miRNAs (DEmiRNAs). We then compose a ceRNA network for BC, including 72 DElncRNAs, 8 DEmiRNAs, and 12 DEmRNAs. After verification, 2 lncRNAs (LINC00466, LINC00460), 1 miRNA (Hsa-mir-204), and 5 mRNAs (TGFBR2, CDH2, CHRDL1, FGF2, and CHL1) were meaningful as prognostic biomarkers for BC patients. In the ceRNA network, we found that three axes were present in 10 RNAs related to the prognosis of BC, namely, LINC00466-Hsa-mir-204-TGFBR2, LINC00466-Hsa-mir-204-CDH2, and LINC00466-Hsa-mir-204-CHRDL1. CONCLUSION: This study highlighted lncRNA-miRNA-mRNA ceRNA related to the pathogenesis of BC, which might be used for latent diagnostic biomarkers and therapeutic targets for BC.

Development of a risk scoring system for evaluating the prognosis of patients with Her2-positive breast cancer.

BACKGROUND: As one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, although the advent of anti-Her2 drugs has brought good news to patients. However, the emergence of drug resistance still limits its clinical efficacy, so there is an urgent need to explore new targets and develop a risk scoring system to improve treatments and evaluate patient prognosis. METHODS: Differentially expressed mRNAs associated with Her2-positive breast cancer were screened from a TCGA cohort. The prognostic risk scoring system was constructed according to univariate and Lasso Cox regression model analyses and combined with clinical factors (such as age and TNM) for univariate and multivariate analyses to verify the specificity and sensitivity of the risk scoring system. Finally, based on correlation and CNV mutation analyses, we explored the research value of the mRNAs involved in the system as key genes of the model. RESULTS: In this study, six mRNAs were screened and identified to construct a prognostic risk scoring system, including four up-regulated mRNA (RDH16, SPC25, SPC24, and SCUBE3) and two down-regulated mRNA (DGAT2 and CCDC69). The risk scoring system can divide Her2-positive breast cancer samples into high-risk and low-risk groups to evaluate patient prognosis. In addition, whether through the time-dependent receiver operating characteristics curve or compared with clinical factors, the risk scoring system showed high predictive sensitivity and specificity. Moreover, some CNV mutations in mRNA increase patient risk by influencing expression levels. CONCLUSION: The risk scoring system constructed in this study is helpful to improve the screening of high-risk patients with Her2-positive breast cancer and is beneficial for implementing early diagnosis and personalized treatment. It is suggested that these mRNAs may play an important role in the progression of Her2-positive breast cancer.

Cryptotanshinone Is a Intervention for ER-Positive Breast Cancer: An Integrated Approach to the Study of Natural Product Intervention Mechanisms.

Background: Resistance to endocrine therapy has hampered clinical treatment in patients with ER-positive breast cancer (BRCA). Studies have confirmed that cryptotanshinone (CPT) has cytotoxic effects on BRCA cells and can significantly inhibit the proliferation and metastasis of ER-positive cancer cells. Methods: We analyzed the gene high-throughput data of ER-positive and negative BRCA to screen out key gene targets for ER-positive BRCA. Finally, the effects of CPT on BRCA cells (MCF-7 and MDA-MB-231) were examined, and quantitative RT-PCR was used to evaluate the expression of the key targets during CPT intervention. Results: A total of 169 differentially expressed genes were identified, and revealed that CPT affects the ER-positive BRCA cells by regulating CDK1, CCNA2, and ESR1. The overall experimental results initially show that MCF-7 cells were more sensitive to CPT than MDA-MB-231 cells, and the expression of ESR1 was not affected in the BRCA cells during CPT intervention, while the expression of CDK1 and CCNA2 were significantly down-regulated. Conclusion: CPT can inhibit the proliferation and migration of BRCA cells by regulating CDK1, CCNA2, and ESR1, especially in ER-positive BRCA samples. On the one hand, our research has discovered the possible mechanism that CPT can better interfere with ER+ BRCA; on the other hand, the combination of high-throughput data analysis and network pharmacology provides valuable information for identifying the mechanism of drug intervention in the disease.

Biomarker expression analysis in different age groups revealed age was a risk factor for breast cancer.

The relationship between age and breast cancer is ambiguous. Here, we analyzed the differential expression pattern of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in different age groups to provide an effective association between age and breast cancer risk at the molecular level. We integrated the microarray information from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. The patients were divided into young ( < 50 years) and old ( ≥ 50 years) age groups and evaluated by differential gene expression, weighted gene correlation network analysis (WGCNA), functional enrichment analyses, and coexpression analysis. To determine their potential clinical significance, univariate Cox regression analysis and survival assessment were conducted. We identified two lncRNAs (AL139280.1 and AP000851.1) and three mRNAs (MT1M, HBB, and TFPI2) as the risk markers, and Gene set enrichment analysis (GSEA) focusing on a single gene revealed that "pyrimidine metabolism," "cell cycle," and "P53 signaling pathway" were coenriched. These data demonstrated that age may be a risk factor for breast carcinogenesis and prognosis and provide an in-depth molecular characterization based on the expression patterns of lncRNAs and mRNAs.

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.

Background: Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA. Methods: BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and multivariate analyses to predict the prognosis of patients with BRCA. The efficacy of the model was verified by TCGA-based breast cancer samples. Identified lncRNA-related mRNA based on the co-expression method. Results: We constructed a 7-lncRNA breast cancer prediction model including LINC00377, LINC00536, LINC01224, LINC00668, LINC01234, LINC02037, and LINC01456. The breast cancer samples were divided into high-risk and low-risk groups based on the model, which verified the specificity and sensitivity of the model. The Area Under Curve (AUC) of the 3- and 5-year Receiver Operating Characteristic curve were 0.711 and 0.734, respectively, indicating that the model has good performance. Conclusion: We constructed a 7-lncRNA model to predict the prognosis of patients with BRCA, and suggest that these lncRNAs may play a specific role in the carcinogenesis of BRCA.

The Modulatory Properties of Astragalus membranaceus Treatment on Triple-Negative Breast Cancer: An Integrated Pharmacological Method.

Background: Studies have shown that the natural products of Astragalus membranaceus (AM) can effectively interfere with a variety of cancers, but their mechanism of action on breast cancer remains unclear. Triple-negative breast cancer (TNBC) is associated with a severely poor prognosis due to its invasive phenotype and lack of biomarker-driven-targeted therapies. In this study, the potential mechanism of the target composition acting on TNBC was explored by integrated pharmacological models and in vitro experiments. Materials and Methods: Based on the Gene Expression Omnibus (GEO) database and the relational database of Traditional Chinese Medicines (TCMs), the drug and target components were initially screened to construct a common network module, and multiattribute analysis was then used to characterize the network and obtain key drug-target information. Furthermore, network topology analysis was used to characterize the betweenness and closeness of key hubs in the network. Molecular docking was used to evaluate the affinity between compounds and targets and obtain accurate combination models. Finally, in vitro experiments verified the key component targets. The cell counting kit-8 (CCK-8) assay, invasion assay, and flow cytometric analysis were used to assess cell viability, invasiveness, and apoptosis, respectively, after Astragalus polysaccharides (APS) intervention. We also performed western blot analysis of key proteins to probe the mechanisms of correlated signaling pathways. Results: We constructed "compound-target" (339 nodes and 695 edges) and "compound-disease" (414 nodes and 6458 edges) networks using interaction data. Topology analysis and molecular docking were used as secondary screens to identify key hubs of the network. Finally, the key component APS and biomarkers PIK3CG, AKT, and BCL2 were identified. The in vitro experimental results confirmed that APS can effectively inhibit TNBC cell activity, reduce invasion, promote apoptosis, and then counteract TNBC symptoms in a dose-dependent manner, most likely by inhibiting the PIK3CG/AKT/BCL2 pathway. Conclusion: This study provides a rational approach to discovering compounds with a polypharmacology-based therapeutic value. Our data established that APS intervenes with TNBC cell invasion, proliferation, and apoptosis via the PIK3CG/AKT/BCL2 pathway and could thus offer a promising therapeutic strategy for TNBC.

Integrated analysis of co-expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer.

Long non-coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co-expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan-Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co-expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2-AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.

A four-mRNA model to improve the prediction of breast cancer prognosis.

BACKGROUND: Breast cancer (BRCA) is the most prevalent cancer that threatens female health. A growing body of evidence has demonstrated the non-negligible effects of messenger RNAs (mRNAs) on biological processes involved in cancers; however, there is no definite conclusion regarding the role of mRNAs in predicting the prognosis of BRCA patients. MATERIALS AND METHODS: We systematically screened the mRNA expression landscape and clinical data of samples from the Cancer Genome Atlas (TCGA). Univariate Cox analysis and robust likelihood-based survival analysis were conducted to identify key mRNAs associated with BRCA. Furthermore, risk scores based on multivariate Cox analysis divided the training set into high-risk and low-risk groups. ROC analysis determined the optimal cut-off point for patient classification of risk levels. The prognostic model was additionally validated in the testing set and complete dataset. Finally, we plotted the survival curves for the mRNAs used in our model. RESULTS: We obtained the original expression data of 13,617 mRNAs from a total of 1088 samples. After comprehensive survival analysis, the four-mRNA (ACSL1, OTUD3, PKD1L2, and WISP1) prognosis risk assessment model was constructed. Furthermore, the area under cure (AUC) was 0.834, indicating that the model was meaningful and reasonable. In each dataset, analysis based on the four-mRNA signature risk score indicated that the survival status of the group with high risk score was worse than that of the group with low risk scores. Patients with strong mRNA expression of OTUD3, PKD1L2, and WISP1 tended to have good prognosis, whereas patients with high ACSL1 expression tended to have poor prognosis. CONCLUSION: In summary, we constructed a four-mRNA prognosis risk assessment model for BRCA. The newly developed model offers more possibilities for assessing prognosis and guiding the selection of better treatment strategies for BRCA.

Identifying the Antiproliferative Effect of Astragalus Polysaccharides on Breast Cancer: Coupling Network Pharmacology With Targetable Screening From the Cancer Genome Atlas.

Background: Astragalus polysaccharides (APS), natural plant compounds, have recently emerged as a promising strategy for cancer treatment, but little is known concerning their effects on breast cancer (BC) tumorigenesis. Methods: We obtained breast cancer genetic data from The Cancer Genome Atlas (TCGA) database, network pharmacology to further clarify its biological properties. Survival analysis and molecular docking techniques were implemented for the final screening to obtain key target information. Our experiments focused on the detection of intervention effects of APS on BC cells (MCF-7 and MDA-MB-231), and quantitative RT-PCR (qRT-PCR) was used to assess the expression of key targets. Results: A total of 1,439 differentially expressed genes (DEGs) were identified by TCGA and used to build disease networks. Module analysis, gene ontology and pathway analysis revealed characteristic of the DEGs network. Topological properties were used to identify key targets, survival analysis and molecular docking finally found that the targets of APS regulation of BC cells may be CCNB1, CDC6, and p53. Through cell viability, migration and invasion assays, we found that APS interferes with the development of breast cancer in MCF7 and MDA-MB-231 cells in a dose-dependent manner. Furthermore, qRT-PCR verification suggested that the expression of CCNB1 and CDC6 in breast cancer cells was significantly downregulated in response to APS, while expression of the tumor suppressor gene P53 was significantly increased. Conclusion: Results of this study suggest therapeutic potential for APS in BC treatment, possibly through interventions with CCNB1, CDC6, and P53. Furthermore, these findings illustrate the feasibility of using network pharmacology to connect large-scale target data as a way to discover the mechanism of natural products interfering with disease.

MicroRNAs associated with lung squamous cell carcinoma: New prognostic biomarkers and therapeutic targets.

BACKGROUND: With the development in research, the importance of microRNAs (miRNAs) in the occurrence and metastasis, and prognosis of lung squamous cell carcinoma (LUSC) have received extensive attention. This study aims to identify new biomarkers and pivotal genes associated with LUSC prognosis through bioinformatics. MATERIALS AND METHODS: We downloaded miRNA and messenger RNA samples related to LUSC from The Cancer Genome Atlas (TCGA) database. Following initial data screening and preprocessing, we utilized the R platform and a range of analysis tools (miRDB, TargetScanHuman7.2, DAVID, and Cytoscape_v3.5.1) to analyze the TCGA data and identify highly specific and sensitive biomarkers. RESULTS: We finally identified 12 miRNAs and six central genes closely related to the overall survival of patients with LUSC. We found that high expression of 7 miRNAs (miR-301b, miR-383, miR-512, miR-515, miR-525, miR-577, and miR-5682) and low expression of five miRNAs (miR-448, miR-486, miR-4732,miR-4732, miR-516, and miR-1911) can significantly improve the overall survival rate of patients with LUSC. The six central genes DLGAP5, CENPA, CHEK1, LRRK2, CALB1, and TOP2A are directly or indirectly involved in the formation and metastasis of LUSC and could, therefore, be considered as target genes for drug therapy. CONCLUSION: With the development in research, the importance of miRNAs in the occurrence and metastasis and prognosis of LUSC have received extensive attention. This study aims to identify new biomarkers and pivotal genes associated with LUSC prognosis through bioinformatics.