Erianin inhibits the progression of triple-negative breast cancer by suppressing SRC-mediated cholesterol metabolism.

Triple-negative breast cancer (TNBC) is highly malignant and lacks effective biotherapeutic targets. The development of efficient anticancer drugs with low toxicity and few side effects is a hotspot in TNBC treatment research. Although erianin is known to have potent antitumor activity, its regulatory mechanism and target in TNBC have not been fully elucidated, hampering further drug development. This study showed that erianin can significantly inhibit TNBC cell proliferation and migration, promote cell apoptosis, and inhibit the growth of transplanted tumors in mice. Mechanistically, through network pharmacology analysis, molecular docking and cellular thermal shift assays, we preliminarily identified SRC as the cellular target of erianin. Erianin potently inhibited the expression of SRC, which mediated the anticancer effect of erianin in TNBC. Moreover, erianin can downregulate the expression of genes related to cholesterol synthesis and uptake by targeting SRC, interfering with cholesterol levels in TNBC, thereby inhibiting the progression of TNBC in vivo and in vitro. Taken together, our results suggest that erianin may inhibit the progression of TNBC by suppressing SRC-mediated cholesterol metabolism, and erianin has the great potential to be an effective treatment for TNBC patients.

Cervical cancer subtype identification and model building based on lipid metabolism and post-infection microenvironment immune landscape.

Background: As the second most common gynecological cancer, cervical cancer (CC) seriously threatens women's health. The poor prognosis of CC is closely related to the post-infection microenvironment (PIM). This study investigated how lipid metabolism-related genes (LMRGs) affect CC PIM and their role in diagnosing CC. Methods: We analyzed lipid metabolism scores in the CC single-cell landscape by AUCell. The differentiation trajectory of epithelial cells to cancer cells was revealed using LMRGs and Monocle2. Consensus clustering was used to identify novel subgroups using the LMRGs. Multiple immune assessment methods were used to evaluate the immune landscape of the subgroups. Prognostic genes were determined by the LASSO and multivariate Cox regression analysis. Finally, we perform molecular docking of prognostic genes to explore potential therapeutic agents. Results: We revealed the differentiation trajectory of epithelial cells to cancer cells in CC by LMRGs. The higher LMRGs expression cluster had higher survival rates and immune infiltration expression. Functional enrichment showed that two clusters were mainly involved in immune response regulation. A novel LMR signature (LMR.sig) was constructed to predict clinical outcomes in CC. The expression of prognostic genes was correlated with the PIM immune landscape. Small molecular compounds with the best binding effect to prognostic genes were obtained by molecular docking, which may be used as new targeted therapeutic drugs. Conclusion: We found that the subtype with better prognosis could regulate the expression of some critical genes through more frequent lipid metabolic reprogramming, thus affecting the maturation and migration of dendritic cells (DCs) and the expression of M1 macrophages, reshaping the immunosuppressive environment of PIM in CC patients. LMRGs are closely related to the PIM immune landscape and can accurately predict tumor prognosis. These results further our understanding of the underlying mechanisms of LMRGs in CC.

Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study.

BACKGROUND: Allergic diseases typically refer to a heterogeneous group of conditions primarily caused by the activation of mast cells or eosinophils, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma. Asthma, AR, and AD collectively affect approximately one-fifth of the global population, imposing a significant economic burden on society. Despite the availability of drugs to treat allergic diseases, they have been shown to be insufficient in controlling relapses and halting disease progression. Therefore, new drug targets are needed to prevent the onset of allergic diseases. METHOD: We employed a Mendelian randomization approach to identify potential drug targets for the treatment of allergic diseases. Leveraging 1798 genetic instruments for 1537 plasma proteins from the latest reported Genome-Wide Association Studies (GWAS), we analyzed the GWAS summary statistics of Ferreira MA et al. (nCase = 180,129, nControl = 180,709) using the Mendelian randomization method. Furthermore, we validated our findings in the GWAS data from the FinnGen and UK Biobank cohorts. Subsequently, we conducted sensitivity tests through reverse causal analysis, Bayesian colocalization analysis, and phenotype scanning. Additionally, we performed protein-protein interaction analysis to determine the interaction between causal proteins. Finally, based on the potential protein targets, we conducted molecular docking to identify potential drugs for the treatment of allergic diseases. RESULTS: At Bonferroni significance (p < 3.25 × 10-5), the Mendelian randomization analysis revealed 11 significantly associated protein-allergic disease pairs. Among these, the increased levels of TNFAIP3, ERBB3, TLR1, and IL1RL2 proteins were associated with a reduced risk of allergic diseases, with corresponding odds ratios of 0.82 (0.76-0.88), 0.74 (0.66-0.82), 0.49 (0.45-0.55), and 0.81 (0.75-0.87), respectively. Conversely, increased levels of IL6R, IL1R1, ITPKA, IL1RL1, KYNU, LAYN, and LRP11 proteins were linked to an elevated risk of allergic diseases, with corresponding odds ratios of 1.04 (1.03-1.05), 1.25 (1.18-1.34), 1.48 (1.25-1.75), 1.14 (1.11-1.18), 1.09 (1.05-1.12), 1.96 (1.56-2.47), and 1.05 (1.03-1.07), respectively. Bayesian colocalization analysis suggested that LAYN (coloc.abf-PPH4 = 0.819) and TNFAIP3 (coloc.abf-PPH4 = 0.930) share the same variant associated with allergic diseases. CONCLUSIONS: Our study demonstrates a causal association between the expression levels of TNFAIP3 and LAYN and the risk of allergic diseases, suggesting them as potential drug targets for these conditions, warranting further clinical investigation.

Single-cell and bulk RNA-sequencing analysis to predict the role and clinical value of CD36 in lung squamous cell carcinoma.

The majority of patients with lung squamous cell carcinoma are diagnosed at an advanced stage, which poses a challenge to the efficacy of chemotherapy. Therefore, the search for an early biomarker needs to be addressed. CD36 is a scavenger receptor expressed in various cell types. It has been reported that it is closely related to the occurrence and development of many kinds of tumours. However, its role in lung squamous cell carcinoma has not been reported. Our research aims to reveal the role of CD36 in lung squamous cell carcinoma by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. We used bioinformatics methods to explore the potential carcinogenicity of CD36 by analysing the data from the cancer genome map (TCGA), gene expression comprehensive map (GEO), human protein map (HPA) comparative toxicology genomics database (CTD) and other resources. Our study dissected the relationship between CD36 and prognosis and gene correlation, functional analysis, mutation of different tumours, infiltration of immune cells and exploring the interaction between CD36 and chemicals. The results showed that the expression of CD36 was heterogeneous. Compared with normal patients, the expression was low in lung squamous cell carcinoma. In addition, CD36 showed early diagnostic value in four kinds of tumours (LUSC, BLCA, BRCA and KIRC) and was positively or negatively correlated with the prognosis of different tumours. The relationship between CD36 and the tumour immune microenvironment was revealed by immunoinfiltration analysis, and many drugs that might target CD36 were identified by the comparative toxicological genomics database (CTD). In summary, through pancancer analysis, we found and verified for the first time that CD36 may play a role in the detection of lung squamous cell carcinoma. In addition, it has high specificity and sensitivity in detecting cancer. Therefore, CD36 can be used as an auxiliary index for early tumour diagnosis and a prognostic marker for lung squamous cell carcinoma.

Stemness signature and targeted therapeutic drugs identification for Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and carries the worst prognosis, characterized by the lack of progesterone, estrogen, and HER2 gene expression. This study aimed to analyze cancer stemness-related gene signature to determine patients' risk stratification and prognosis feature with TNBC. Here one-class logistic regression (OCLR) algorithm was applied to compute the stemness index of TNBC patients. Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort. We diagnosed the expression level of prognostic genes signature in the tumor immune microenvironment, analyzed the TNBC scRNA-seq GSE176078 dataset, and further validated the expression level of prognostic genes using the HPA database. Finally, the small molecular compounds targeted at the anti-tumor effect of predictive genes were screened by molecular docking; this novel stemness-based prognostic genes signature study could facilitate the prognosis of patients with TNBC and thus provide a feasible therapeutic target for TNBC.

Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer.

Transcription factors (TFs) have been shown to play a key role in the occurrence and development of tumors, including triple-negative breast cancer (TNBC), with a worse prognosis. Machine learning is widely used for establishing prediction models and screening key tumor drivers. Current studies lack TF integration in TNBC, so targeted research on TF prognostic models and targeted drugs is beneficial to improve clinical translational application. The purpose of this study was to use the Least Absolute Shrinkage and Selection Operator to build a prognostic TFs model after cohort normalization based on housekeeping gene expression levels. Potential targeted drugs were then screened on the basis of molecular docking, and a multi-drug combination strategy was used for both in vivo and in vitro experimental studies. The machine learning model of TFs built by E2F8, FOXM1, and MYBL2 has broad applicability, with an AUC value of up to 0.877 at one year. As a high-risk clinical factor, its abnormal disorder may lead to upregulation of the activity of pathways related to cell proliferation. This model can also be used to predict the adverse effects of immunotherapy in patients with TNBC. Molecular docking was used to screen three drugs that target TFs: Trichostatin A (TSA), Doxorubicin (DOX), and Calcitriol. In vitro and in vivo experiments showed that TSA + DOX was able to effectively reduce DOX dosage, and TSA + DOX + Calcitriol may be able to effectively reduce the toxic side effects of DOX on the heart. In conclusion, the machine learning model based on three TFs provides new biomarkers for clinical and prognostic diagnosis of TNBC, and the combination targeted drug strategy offers a novel research perspective for TNBC treatment.

Widely targeted metabolomics reveals the antioxidant and anticancer activities of different colors of Dianthus caryophyllus.

Carnation is edible flower that has potent antioxidant properties and is used in traditional Chinese medicinal system and food industry. The phytochemicals responsible for these various proprieties, however, are not fully understood. Thus, in order to recognize metabolite diversity and variability in carnation flowers of different colors and to discover key metabolites that contribute to the differences in antioxidant and anticancer activities, widely targeted LC-MS/MS-based metabolomics analysis was conducted on purple, green, yellow, and white carnation flowers. We identified and chemically categorized 932 metabolites. Metabolic compounds varied significantly with flower color. Several flavonoids, organic acids, phenolic acids, and nucleotides and their derivatives were found to be specific differential metabolites in purple flowers. A total of 128 key differential metabolites were screened. The purple flowers were found to have the highest antioxidant and anticancer activities compared to the other colored flowers. Correlation analysis revealed that the 6-hydroxykaempferol-3,6-O-diglucoside, 6-hydroxykaempferol-7-O-glucoside, quercetin-3-O-sophoroside, and 2'-deoxyguanosine were found to be the major constituents of the antioxidant and anticancer activities. 2'-Deoxyguanosine has effective antiproliferative activity against A549 and U2OS cells for the first report. At the same time, the combination of 2'-deoxyguanosine with 6-hydroxykaempferol-3, 6-O-diglucoside, or quercetin-3-O-sophoroside have also been found to increase the antitumor activity of 2'-deoxyguanosine. These discoveries enrich information on the phytochemical composition of carnation of different colors and provide resources for the overall use and improvement of carnation flowers quality.

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most widely distributed tumor in the world, and its immunotherapy is not practical. Neutrophil is one of a tumor's most abundant immune cell groups. This research aimed to investigate the complex communication network in the immune microenvironment (TIME) of NSCLC tumors to clarify the interaction between immune cells and tumors and establish a prognostic risk model that can predict immune response and prognosis of patients by analyzing the characteristics of Neutrophil differentiation. Integrated Single-cell RNA sequencing (scRNA-seq) data from NSCLC samples and Bulk RNA-seq were used for analysis. Twenty-eight main cell clusters were identified, and their interactions were clarified. Next, four subsets of Neutrophils with different differentiation states were found, closely related to immune regulation and metabolic pathways. Based on the ratio of four housekeeping genes (ACTB, GAPDH, TFRC, TUBB), six Neutrophil differentiation-related genes (NDRGs) prognostic risk models, including MS4A7, CXCR2, CSRNP1, RETN, CD177, and LUCAT1, were constructed by Elastic Net and Multivariate Cox regression, and patients' total survival time and immunotherapy response were successfully predicted and validated in three large cohorts. Finally, the causes of the unfavorable prognosis of NSCLC caused by six prognostic genes were explored, and the small molecular compounds targeted at the anti-tumor effect of prognostic genes were screened. This study clarifies the TIME regulation network in NSCLC and emphasizes the critical role of NDRGs in predicting the prognosis of patients with NSCLC and their potential response to immunotherapy, thus providing a promising therapeutic target for NSCLC.

Bioinformatics Analysis and Structure of Gastric Cancer Prognosis Model Based on Lipid Metabolism and Immune Microenvironment.

OBJECTIVES: The reprogramming of lipid metabolism is a new trait of cancers. However, the role of lipid metabolism in the tumor immune microenvironment (TIME) and the prognosis of gastric cancer remains unclear. METHODS: Consensus clustering was applied to identify novel subgroups. ESTIMATE, TIMER, and MCPcounter algorithms were used to determine the TIME of the subgroups. The underlying mechanisms were elucidated using functional analysis. The prognostic model was established using the LASSO algorithm and multivariate Cox regression analysis. RESULTS: Three molecular subgroups with significantly different survival were identified. The subgroup with relatively low lipid metabolic expression had a lower immune score and immune cells. The differentially expressed genes (DEGs) were concentrated in immune biological processes and cell migration via GO and KEGG analyses. GSEA analysis showed that the subgroups were mainly enriched in arachidonic acid metabolism. Gastric cancer survival can be predicted using risk models based on lipid metabolism genes. CONCLUSIONS: The TIME of gastric cancer patients is related to the expression of lipid metabolism genes and could be used to predict cancer prognosis accurately.

The Value of the Stemness Index in Ovarian Cancer Prognosis.

Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-ß signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC.

Clinical significance and mechanism of long noncoding RNA HAGLROS in triple negative breast cancer.

PURPOSE: Triple Negative Breast Cancer (TNBC) is the malignant tumor with the fastest progression rate in breast cancer. LncRNAs are widely involved in various biological characteristics of tumor. The purpose of this study was to mine LncRNAs that can be used to diagnose and evaluate the prognosis of TNBC. METHODS: Base on TCGA dataset, we used three R language packages to analyze the differentially expressed (DE) lncRNAs in TNBC. Survival analysis and ROC curve analysis were conducted to estimate the potential diagnostic and prognostic value of LncRNAs for TNBC. Furthermore, CCK-8 and Transwell assays were used to assess the effects of LncRNA on MDA-MB-231 cells proliferation and migration. Additionally, targets mRNAs of candidate LncRNA were predicted by co-expression analysis and multiple target gene prediction databases, then KEGG pathway and GO analysis were conducted using DAVID online tool. RESULTS: 6165 DERNAs and 1258 DElncRNAs were obtained. 40 LncRNAs were significantly correlated with the survival time of TNBC patients. Among them, HAGLROS has the highest HR value. ROC curve analysis also showed that HAGLROS had high sensitivity and specificity. Further in vitro experiments showed that downregulation of HAGLROS inhibited the proliferation and migration of MDA-MB-231 cells. Moreover, by conducting bioinformatics analysis, we found that these target genes of HAGLROS were involved in regulating five signaling pathways. Mechanistic investigations demonstrated that HAGLROS might regulate the expression of PAX5 through miR-330-5p, the effects of miR-330-5p in MDA-MB-231 cells were also analyzed. CONCLUSION: Our results showed that HAGLROS was significantly overexpressed in TNBC, and high HAGLROS expression predicted poor overall survival. Downregulation of HAGLROS could inhibite the proliferation and migration of MDA-MB-231 cell by regulating PAX5 expression through miR-330-5p.

ln RNA LINC01234 promotes triple-negative breast cancer progression through regulating the miR-429/SYNJ1 axis.

Emerging evidence has illustrated that long noncoding RNA 01234 (LINC01234) has played a pivotal role in the development and progression of human cancer. The regulatory role and underlying mechanisms of LINC01234 in triple-negative breast cancer (TNBC) remains unknown. In this study, we analyzed the expression level of LINC01234 in several breast cancer cell lines. CCK-8, EdU, flow cytometry analysis, wound healing assay, and transwell assay were carried out to investigate the effect of LINC01234 on tumor proliferation, apoptosis, and migration. Bioinformatic analysis and luciferase reporter assays were performed to confirm the molecular binding. We found that LINC01234 was dramatically upregulated in breast cancer cell lines, especially in TNBC. The loss and gain-of functional experiments revealed that LINC01234 significantly promoted proliferation, migration, and suppressed cell apoptosis of MDA-MB-231 cells in vitro and inhibited tumorigenesis in vivo. Mechanistic investigations demonstrated that LINC01234 might act as a competing endogenous RNA (ceRNA) for miR-429 to regulate the SYNJ1 expression. The effects of miR-429 and SYNJ1 in MDA-MB-231 cells were also analyzed. Our results revealed that the novel LINC01234/miR-429/SYNJ1 axis played a critical role in progression of TNBC cell line MDA-MB-231, and it may serve as a therapeutic target for TNBC.

A novel three-long noncoding RNA risk score system for the prognostic prediction of triple-negative breast cancer.

Background: Triple-negative breast cancer (TNBC) is characterized by fast tumor increase, rapid recurrence and natural metastasis. We aimed to identify a genetic signature for predicting the prognosis of TNBC. Materials & methods: We conducted a weighted correlation network analysis of datasets from the Gene Expression Omnibus. Multivariate Cox regression was used to construct a risk score model. Results: The multi-factor risk scoring model was meaningfully associated with the prognosis of patients with TBNC. The predictive power of the model was demonstrated by the time-dependent receiver operating characteristic curve and Kaplan-Meier curve, and verified using a validation set. Conclusion: We established a long noncoding RNA-based model for the prognostic prediction of TNBC.

Potential of RASSF1A promoter methylation as a biomarker for colorectal cancer: Meta-analysis and TCGA analysis.

The RAS association domain family protein 1A (RASSF1A) is a tumor suppressor in colorectal cancer (CRC), and is often inactived by hypermethylation. Therefore, we evaluated the association between RASSF1A hypermethylation and the risk and prognosis in CRC. We identified literature through searching PubMed and China National Knowledge Infrastructure databases, and then validated and supplemented the meta-analysis with TCGA analysis. Twenty-three studies involving 2886 subjects of CRC were examined. The meta-analysis showed that RASSF1A promoter methylation inferred high CRC risk (odds ratio, 6.53, 95% confidence interval 3.88-11.01, P < .001) and poor overall survival (hazard ratio 2.85, 95% CI 1.88-4.31, P < .001). The TCGA analysis suggested that effect of RASSF1A promotor methylation was affected by tumor localization (colon vs. rectum). RASSF1A promoter methylation was a predictor of high risk (OR 2.38, 95%CI 1.02-5.6, P = .046) and poor disease free survival(HR 2.25, 95%CI 1.27-3.99, P = .006)in colon adenocarcinoma, but the association was statistically insignificant in rectum adenocarcinoma(HR 1.58, 95% CI 0.69-3.59, P = .28). These results suggested RASSF1A hypermethylation is a risk and a potential prognostic biomarker in CRC.

miR-148b-3p, miR-190b, and miR-429 Regulate Cell Progression and Act as Potential Biomarkers for Breast Cancer.

PURPOSE: Breast cancer is the most frequently diagnosed malignancy in women worldwide. MicroRNAs (miRNAs) are thought to serve as potential biomarkers in various cancers, including breast cancer. METHODS: We evaluated the miRNA expression profiles in 1,083 breast cancer samples and 104 normal breast tissues from The Cancer Genome Atlas database. We used the edgeR package of R software to analyze the differentially expressed miRNAs in normal and cancer tissues, and screened survival-related miRNAs by Kaplan-Meier analysis. A receiver operating characteristic curve was generated to evaluate the accuracy of these miRNAs as molecular markers for breast cancer diagnosis. Furthermore, the functional role of these miRNAs was verified using cell experiments. Targets of candidate miRNAs were predicted using 9 online databases, and Gene Ontology (GO) functional annotation and pathway analyses were conducted using Database for Annotation, Visualization and Integrated Discovery online tool. RESULTS: A total of 68 miRNAs showed significantly different expression patterns between the groups (p < 0.001), and 13 of these miRNAs were significantly associated with poor survival (p < 0.05). Three miRNAs with high specificity and sensitivity, namely, miR-148b-3p, miR-190b, and miR-429, were selected. In vitro experiments showed that the overexpression of these 3 miRNAs significantly promoted the proliferation and migration of MDA-MB-468 and T47D cells and reduced the apoptosis of T47D cells. GO and pathway enrichment analyses revealed that the targets of these dysregulated miRNAs were involved in many critical cancer-related biological processes and pathways. CONCLUSION: The miR-148b-3p, miR-190b, and miR-429 may serve as potential diagnostic and prognostic markers for breast cancer. This study demonstrated the roles of these 3 miRNAs in the initiation and progression of breast cancer.

Associations Between TP53 and MDM2 Polymorphisms and the Follicle-Stimulating Hormone/Luteinizing Hormone Ratio in Infertile Women.

AIMS: This is a follow-up study based on the results of our previous article, to further explore the effect of the TP53 codon 72 (rs1042522) and MDM2 SNP309 (rs2279744) polymorphisms on basal follicle-stimulating hormone (FSH)/luteinizing hormone (LH) ratios in infertility women. MATERIALS AND METHODS: The distribution of two genetic polymorphisms (rs1042522 and rs2279744) and basal FSH/LH ratios were tested and analyzed in 1051 in vitro fertilization (IVF) patients at a university-affiliated hospital. RESULTS: The TP53 codon 72 polymorphism had a significant association with the FSH/LH ratio (group I: FSH/LH <2.3 and group II: FSH/LH ≥2.3) (C/C vs. G/G: odds ratio [OR] = 1.69, 95% confidence interval [CI]: 1.07-2.65, p = 0.02; G/C vs. G/G: OR = 1.86, 95% CI: 1.25-2.77, p = 0.002). In a stratification analysis, C allele carriers and the C/C genotype showed a strong association with positive clinical pregnancy outcomes after IVF compared with G allele carriers and the G/G genotype in the recessive, dominant, and allelic genetic models in group I (C/C vs. G/G: OR = 1.84, 95% CI: 1.25-2.69, p = 0.01; C/C vs. G carrier: OR = 1.52, 95% CI: 1.12-2.07, p = 0.01; C carrier vs. G/G: OR = 1.46, 95% CI: 1.07-2.01, p = 0.02; C allele vs. G allele: OR = 1.34, 95% CI: 1.11-1.62, p = 0.003), no significant associations by stratification were observed for group II. No associations were found between MDM2 SNP309 and either of two groups. CONCLUSION: The TP53 codon 72 polymorphism is associated with FSH/LH ratios, suggesting that it is a potential predictive genetic marker of IVF outcome in patients younger than 35 years of age with baseline FSH levels below 10 IU/L and who have an FSH/LH ratio <2.3.

Loss of p21 promoted tumorigenesis in the background of telomere dysfunctions induced by TRF2 and Wrn deficiency.

Werner syndrome (WS) is a rare autosomal recessive progeria disease with genetic instability/cancer predisposition, thus a good model in understanding aging related carcinogenesis. Telomere dysfunction induced cellular senescence is essential in the manifestation of the WS phenotype. Our previous data has shown that p21 (encoded by Cdkn1a gene) could induce cellular senescence and suppress cellular growth of ALT (alternative lengthening of telomere) tumors derived from WS, suggested that p21 might play a key role in maintaining senescence of WS cells. To confirm the role of p21 in suppressing telomere dysfunction induced tumorigenesis, we overexpressed dominant negative protein TRF2ΔBΔM in p21-/- mouse embryonic fibroblasts (MEFs). To further stress the cell, we crossed Wrn-/- mice with p21-/- mice to obtained p21-/-Wrn-/- MEFs, and overexpressed TRF2ΔBΔM in these MEFs to induce telomere dysfunction similar to that in WS cells. Our data showed that, in the context of p21-/- TRF2ΔBΔM, loss of p21 function rescued cellular senescence, and induced p53 mutation, but did not induce tumorigenesis. However, in the set of p21-/-Wrn-/- TRF2ΔBΔM, loss of p21 function induced p53 mutation and tumorigenesis. To further verify the role of p21 in suppressing telomere dysfunction related tumorigenesis, we knocked down p21 in non-tumorigenic immortalized cells derived from WS MEFs (mTerc -/-Wrn-/- ), and found that loss of p21 could induce ALT tumorigenesis, which displayed typical smear pattern of telomere length and arc-shaped telomeric DNA. In another hand, recovering telomerase activity in these MEFs could also induce tumorigenesis without affecting p21 expression level. Together our data suggested that p21 controlled cell cycle regulation played an essential role in suppressing telomere dysfunction-related tumorigenesis. These data also suggested that the genetic context is essential in determining the role of p21 in cancer prevention. Therefore, targeting p21 in the treatment of human degenerative diseases would require a personalized genetic background screen.

Gain of function in the mouse model of a recurrent mutation p53N236S promotes the formation of double minute chromosomes and the oncogenic potential of p19ARF.

The mutation p53N236S (p53S) has been identified as one of the recurrent mutations in human cancers by TCGA database. Our in vitro data revealed the oncogenic gain of function of p53S. To understand the function of p53S in vivo, we generated the p53S knock-in mouse. The p53S/S mice manifested highly invasive lymphomas and metastatic sarcomas with dramatically increased double minute chromosomes. The survival curve, the incidence of tumors and the tumor spectrum of p53S/S mice is very similar to the p53R172H mouse model. The p53S/+ mice showed delayed onset of tumorigenesis and a high metastasis rate (40%) and low loss of heterozygosity rate (2/16). The activation of CDKN2A pathway in p53S/S MEF and tumors, and the accumulation of p19ARF protein in tumor tissues suggested p19ARF might contribute to the accumulation of mutant p53S protein in the tumor and promote tumorigenesis. The high expression of p19ARF correlated with mutant p53 accumulation and tumor progression, suggesting a dual role of p19ARF in tumor promotion or suppression that might depend on the p53 mutation status in tumor cells. The oncogenic gain of function of this recurrent mutation p53S prompts the reconsideration of p53 mutations function that occurs at a low frequency.

The relationship between RASSF1A gene promoter methylation and the susceptibility and prognosis of melanoma: A meta-analysis and bioinformatics.

BACKGROUND: The function of the tumor suppressor gene RASSF1A in cancer cells has been detailed in many studies. However, due to the methylation of its promoter, the expression of RASSF1A is missing in most cancers. In the literature, we found that the conclusion regarding the relationship between RASSF1A gene promoter methylation and the susceptibility and prognosis of melanoma was not unified. This study adopts the use of a meta-analysis and bioinformatics to explore the relationship between RASSF1A gene promoter methylation and the susceptibility and prognosis of melanoma. METHODS: Data on melanoma susceptibility were downloaded from the PubMed, Cochrane Library, Web of Science and Google Scholar databases, which were analyzed via a meta-analysis. The effect sizes were estimated by measuring an odds ratio (OR) with a 95% confidence interval (CI). We also used a chi-squared-based Q test to examine the between-study heterogeneity, and used funnel plots to evaluate publication bias. The data on melanoma prognosis, which were analyzed by bioinformatics methods, were downloaded from The Cancer Genome Atlas (TCGA) project. The effect sizes were estimated by measuring the hazard ratios (HRs) with a 95% confidence interval (CI). RESULTS: Our meta-analysis included 10 articles. We found that RASSF1A gene promoter methylation was closely related to melanoma susceptibility (OR = 12.67, 95% CI: 6.16 ∼ 26.05, z = 6.90, P<0.0001 according to a fixed effects model and OR = 9.25, 95% CI: 4.37 ∼ 19.54, z = 5.82, P<0.0001 according to a random effects model). The results of the meta-analysis did not reveal any heterogeneity (tau2 = 0.00; H = 1 [1; 1.55]; I2 = 0% [0%; 58.6%], P = 0.5158) or publication bias (t = 0.87, P = 0.4073 by Egger's test; Z = 0.45, P = 0.6547 by Begg's test); therefore, we believe that the results of our meta-analysis were more reliable. To explore the relationship between RASSF1A gene methylation, the prognosis of melanoma and the clinical features of this cancer type, we used the melanoma DNA methylation data and clinical data from TCGA project. We found that RASSF1A gene promoter methylation and melanoma prognosis did not demonstrate any relationship (HR was 0.94 (95% CI = [0.69; 1.27], P = 0.694) with disease-free survival and 0.74 (95% CI = [0.53; 1.05], P = 0.106) for overall survival), and no significant difference was observed between RASSF1A gene promoter methylation and the clinical-pathological features of melanoma. CONCLUSIONS: In conclusion, the meta-analysis of the data in these articles provides strong evidence that the methylation status of the RASSF1A gene promoter was strongly related to melanoma susceptibility. Our bioinformatics analysis revealed no significant difference between RASSF1A gene promoter methylation and the prognosis and clinical-pathological features of melanoma.

Influence of TP53 Codon 72 Polymorphism Alone or in Combination with HDM2 SNP309 on Human Infertility and IVF Outcome.

To evaluate the association of the TP53 codon 72 (rs 1042522) alone or in combination with HDM2 SNP309 (rs 2279744) polymorphisms with human infertility and IVF outcome, we collected 1450 infertility women undergoing their first controlled ovarian stimulation for IVF treatment and 250 fertile controls in the case-control study. Frequencies, distribution, interaction of genes, and correlation with infertility and IVF outcome of clinical pregnancy were analyzed. We found a statistically significant association between TP53 codon 72 polymorphism and IVF outcome (52.10% vs. 47.40%, OR = 0.83, 95%CI:0.71-0.96, p = 0.01). No significant difference was shown between TP53 codon 72, HDM2 SNP309 polymorphisms, human infertility, and between the combination of two genes polymorphisms and the clinical pregnancy outcome of IVF. The data support C allele as a protective factor for IVF pregnancy outcome. Further researches should be focused on the mechanism of these associations.