Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer.

BACKGROUND: The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction. METHODS: Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed. RESULTS: Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets. CONCLUSIONS: The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.

Mining Prognostic Biomarkers of Thyroid Cancer Patients Based on the Immune-Related Genes and Development of a Reliable Prognostic Risk Model.

Purpose: Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). Methods: Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. Results: Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. Conclusion: Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.

Development of a novel five-gene immune-related risk model for the prognosis evaluation of prostate adenocarcinoma patients.

The interaction between the immune cells and the host immune system with the tumor cells is significantly associated with the initiation and progression of prostate adenocarcinoma (PRAD), whereas the application of immune-related genes (IRGs) for the prognosis evaluation of PRAD patients is still lacking. In this study, we aimed to identify IRGs with prognostic values and to develop a clinically effective risk model. Wilcoxon rank-sum test and univariate Cox analysis were applied to identify the differentially expressed immune-related genes (DEIRGs) related to the survival of PRAD patients. The Least absolute shrinkage and selection operator (LASSO) analysis was performed to identify the independent prognostic DEIRGs and to establish an immune risk score prognostic model. The reliability and veracity of the prognostic model were validated in PRAD patients from the internal cohort (The Cancer Genome Atlas, TCGA dataset) and the external cohort (International Cancer Genome Consortium, ICGC dataset), respectively. Six of the 193 identified DEIRGs were survival-associated in PRAD patients. Five prognostic DEIRGs (SLPI, NOX1, DES, BIRC5 and AMH) were selected to construct the immune-related prognostic model with optimal robustness. In the 2 independent cohorts we chose, PRAD patients could be effectively stratified according to our risk model. Patients with high risk scores had worse survival. Clinical correlation analysis proved that the risk score was associated with advanced clinicopathologic features. Multivariate analysis indicated that the risk model was an independent prognostic indicator. We also established a nomogram based on the risk score model for clinical application. Additionally, the risk score model was correlated with immune cell infiltration and reflected the status of the immune microenvironment. The prognostic value of the five immune-related genes used in the prognostic model was also validated. Our immune-related prognostic model was an effective tool that could not only serve as a predictor for prognosis, but also provide potential prognostic and therapeutic molecular biomarkers for optimizing personalized therapies in clinical practice.

A Novel Autophagy-Related Prognostic Risk Model and a Nomogram for Survival Prediction of Oral Cancer Patients.

BACKGROUND: This study is aimed at constructing a risk signature to predict survival outcomes of ORCA patients. METHODS: We identified differentially expressed autophagy-related genes (DEARGs) based on the RNA sequencing data in the TCGA database; then, four independent survival-related ARGs were identified to construct an autophagy-associated signature for survival prediction of ORCA patients. The validity and robustness of the prognostic model were validated by clinicopathological data and survival data. Subsequently, four independent prognostic DEARGs that composed the model were evaluated individually. RESULTS: The expressions of 232 autophagy-related genes (ARGs) in 127 ORCA and 13 control tissues were compared, and 36 DEARGs were filtered out. We performed functional enrichment analysis and constructed protein-protein interaction network for 36 DEARGs. Univariate and multivariate Cox regression analyses were adopted for searching prognostic ARGs, and an autophagy-associated signature for ORCA patients was constructed. Eventually, 4 desirable independent survival-related ARGs (WDR45, MAPK9, VEGFA, and ATIC) were confirmed and comprised the prognostic model. We made use of multiple ways to verify the accuracy of the novel autophagy-related signature for survival evaluation, such as receiver-operator characteristic curve, Kaplan-Meier plotter, and clinicopathological correlational analyses. Four independent prognostic DEARGs that formed the model were also associated with the prognosis of ORCA patients. CONCLUSIONS: The autophagy-related risk model can evaluate OS for ORCA patients independently since it is accurate and stable. Four prognostic ARGs that composed the model can be studied deeply for target treatment.

Exome sequencing and RNA analysis identify two novel CPLANE1 variants causing Joubert syndrome.

BACKGROUND: Joubert syndrome (JS) is a genetically heterogeneous disorder; its genetic etiology involves more than 35 genes, and a limited number of studies have investigated the pathogenic mechanism of variants in patients with JS. RNA splicing analysis is critical to determine the functional significance for noncanonical splicing variants. METHODS: Whole exome sequencing was performed to screen the causative gene variants in a JS family. Sanger sequencing was used to verify the variants. cDNA PCR products were analyzed and functional experiments were performed to determine the pathogenicity of the variants. RESULTS: The clinical phenotypes and CPLANE1 variants in the JS patient were analyzed and proved consistent. We identified two novel heterozygous variants of CPLANE1 in the proband first, including c.4459del (frameshift variant) and c.7534-14G > A (intronic variant). We analyzed the pathogenic consequences of the 2 variants and classified the c.4459del as likely pathogenic according to the ACMG/AMP guidelines; however, the pathogenic significance of c.7534-14G > A was uncertain. Furthermore, we performed RNA splicing analysis and revealed that the noncanonical splicing variant (c.7534-14G > A) caused aberrant exon 37 skipping. It produced an aberrant transcript that was predicted to encode a C-terminal truncated protein. CONCLUSIONS: The genetic variation spectrum of JS caused by CPLANE1 was updated. Two novel variants further deepened our insight into the disease's molecular mechanism and confirmed the significance of diagnostic whole-exome sequencing.

Classification and Interpretation for 11 FBN1 Variants Responsible for Marfan Syndrome and Pre-implantation Genetic Testing (PGT) for Two Families Successfully Blocked Transmission of the Pathogenic Mutations.

Background: The lifespan of Marfan Syndrome (MFS) patients is shortened, especially in patients without early diagnostics, preventive treatment, and elective surgery. Clinically, MFS diagnosis is mainly dependent on phenotypes, but for children, sporadic cases, or suspicious MFS patients, molecular genetic testing, and mainly FBN1 mutation screening, plays a significant role in the diagnosis of MFS. PGT-M gives couples that had a family history of monogenic disorders the opportunity to avoid the occurrence of MFS. Methods: In this study, 11 families with MFS were recruited and complete clinical features were collected. Variants were classified and interpreted through pedigree analysis according to guidelines. Two families chose to undergo PGT-M; 16 blastocysts were biopsied and amplified. Haplotype analysis was performed to deduce the embryo's genotype by using single nucleotide polymorphisms (SNPs) identified in each sample. Results: We identified 11 potential disease-causing FBN1 variants, six of which are novel. All variants were assessed with prediction tools to assess mutation pathogenicity, population databases to evaluate population allele frequency, literature databases to identify whether the variant had been reported in MFS patients, and multiple sequence alignment to carry out conservative analysis. Finally, nine variants were classified as likely pathogenic/pathogenic variants. Among 11 variants, eight variants were missense, and seven of them were located in the Ca-binding EGF-like motifs, moreover, half of them substituted conserved Cysteine residues. We also identified a splice site variant, a frameshift variant, and a synonymous variant. There are two variants that are de novo variants. PGT-M helped two MFS families give birth to a healthy baby not carrying the FBN1 mutation. Conclusions: In the present study, the FBN1 mutation spectrum was enriched, and may help further elucidate the pathogenesis, benefiting clinical diagnosis and management of MFS. We make use of a reliable PGT-M method for the successful birth of healthy babies to two MFS families.

Expanding the Scope of Non-invasive Prenatal Testing to Detect Fetal Chromosomal Copy Number Variations.

Non-invasive prenatal testing (NIPT) for common fetal trisomies is effective. However, the usefulness of cell-free DNA testing to detect other chromosomal abnormalities is poorly understood. We analyzed the positive rate at different read depths in next-generation sequencing (NGS) and identified a strategy for fetal copy number variant (CNV) detection in NIPT. Pregnant women who underwent NIPT by NGS at read depths of 4-6 M and fetuses with suspected CNVs were analyzed by amniocentesis and chromosomal microarray analysis (CMA). These fetus samples were re-sequenced at a read depth of 25 M and the positive detection rate was determined. With the increase in read depth, the positive CNV detection rate increased. The positive CNV detection rates at 25 M with small fragments were higher by NGS than by karyotype analysis. Increasing read depth in NGS improves the positive CNV detection rate while lowering the false positive detection rate. NIPT by NGS may be an accurate method of fetal chromosome analysis and reduce the rate of birth defects.

Construction autophagy-related prognostic risk signature combined with clinicopathological validation analysis for survival prediction of kidney renal papillary cell carcinoma patients.

BACKGROUND: Little data is available on prognostic biomarkers and effective treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP) patients, to find potential prognostic biomarkers and new targets was an urgent mission for KIRP therapy. METHODS: The differentially expressed autophagy-related genes (DEARGs) were screened out according to the RNA sequencing data in The Cancer Genome Atlas database, then identified survival-related DEARGs to establish a prognostic model for survival predicting of KIRP patients. Then we verified the robustness and validity of the prognostic risk model through clinicopathological data. At last, we evaluate the prognostic value of genes that formed the prognostic risk model individually. RESULTS: We analyzed the expression of 232 autophagy-related genes (ARGs) in 289 KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as DEARGs. The functional and pathway enrichment analysis was done and protein-protein interaction network was constructed for all DEARGs. To sift candidate DEARGs associated with KIRP patients' survival and create an autophagy-related risk prognostic model, univariate and multivariate Cox regression analysis were did separately. Eventually 3 desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were picked out and used for construct the autophagy-related risk model. The accuracy of the prognostic risk model for survival prediction was assessed by Kaplan-Meier plotter, receiver-operator characteristic curve, and clinicopathological correlational analyses. The prognostic value of above 3 genes was verified individually by survival analysis and expression analysis on mRNA and protein level. CONCLUSIONS: The autophagy-related prognostic model is accurate and applicable, it can predict OS independently for KIRP patients. Three independent prognostic DEARGs can benefit for facilitate personalized target treatment too.

Construction autophagy-related prognostic risk signature to facilitate survival prediction, individual treatment and biomarker excavation of epithelial ovarian cancer patients.

BACKGROUND: Existing clinical methods for prognosis evaluating for Epithelial Ovarian Cancer (EOC) patients had defects of invasive, unsystematic and subjective and little data are available for individualizing treatment, therefore, to identify potential prognostic markers and new therapeutic targets for EOC is urgently required. RESULTS: Expression of 232 autophagy-related genes (ARGs) in 354 EOC and 56 human ovarian surface epithelial specimens from 7 independent laboratories were analyzed, 31 mRNAs were identified as DEARGs. We did functional and pathway enrichment analysis and constructed protein-protein interaction network for all DEARGs. To screen out candidate DEARGs related to EOC patients' survival and construct an autophagy-related prognostic risk signature, univariate and multivariate Cox proportional hazards models were established separately. Finally, 5 optimal independent prognostic DEARGs (PEX3, DNAJB9, RB1, HSP90AB1 and CXCR4) were confirmed and the autophagy-related risk model was established by the 5 prognostic DEARGs. The accuracy and robustness of the prognostic risk model for survival prediction were evaluated and verified by analyzing the correlation between EOC patients' survival status, clinicopathological features and risk scores. CONCLUSIONS: The autophagy-related prognostic risk model can be independently used to predict overall survival in EOC patients, it can also potentially assist in individualizing treatment and biomarker development.

Establishment and validation of an autophagy-related prognostic signature for survival predicting in cutaneous melanoma.

Existing staging system for prognosis evaluating for Skin Cutaneous Melanoma (SKCM) patients had defects of subjective, inaccuracy and inconsistently, therefore, to identify specific and applicable prognostic markers and promote personalized therapeutic interventions is urgently required. This study aims to build a robust autophagy-related genes (ARGs) signature for prognosis monitoring of SKCM patients. We determined 26 ARGs as differentially expressed autophagy-related genes (DEARGs) from 103 SKCM and 23 normal skin samples in GSE15605 and GSE3189 datasets. Optimal prognostic DEARGs composed the risk model were screened and verified in 458 SKCM patients in TCGA cohort as the training cohort and 209 patients in GSE65904 as the test cohort. Finally, 4 optimal independent prognostic DEARGs (CAPNS1, DAPK2, PARP1 and PTK6) were filtered out in the training cohort to establish the risk model. A prognostic nomogram was established for quantitative survival prediction. The risk model grouped high-risk SKCM cancer patients exhibited significantly shorter survival times in both training and test cohorts. The area under the ROC curve for risk score model was 0.788 and 0.627 in the training and test cohorts indicated the risk model was relatively accurate for prognosis monitoring. Clinical correlation analysis exhibited that risk score was an independent predictor for prognosis significantly associated with T/N classification. The prognostic value of the 4 risk genes formed the risk model was also validated respectively. We identified a novel autophagy-related signature for prognosis monitoring. It has the potential to be an independent prognostic indicator and can benefit targeted therapy.

Comprehensive preimplantation genetic testing by massively parallel sequencing.

STUDY QUESTION: Can whole genome sequencing (WGS) offer a relatively cost-effective approach for embryonic genome-wide haplotyping and preimplantation genetic testing (PGT) for monogenic disorders (PGT-M), aneuploidy (PGT-A) and structural rearrangements (PGT-SR)? SUMMARY ANSWER: Reliable genome-wide haplotyping, PGT-M, PGT-A and PGT-SR could be performed by WGS with 10× depth of parental and 4× depth of embryonic sequencing data. WHAT IS KNOWN ALREADY: Reduced representation genome sequencing with a genome-wide next-generation sequencing haplarithmisis-based solution has been verified as a generic approach for automated haplotyping and comprehensive PGT. Several low-depth massively parallel sequencing (MPS)-based methods for haplotyping and comprehensive PGT have been developed. However, an additional family member, such as a sibling, or a proband, is required for PGT-M haplotyping using low-depth MPS methods. STUDY DESIGN, SIZE, DURATION: In this study, 10 families that had undergone traditional IVF-PGT and 53 embryos, including 13 embryos from two PGT-SR families and 40 embryos from eight PGT-M families, were included to evaluate a WGS-based method. There were 24 blastomeres and 29 blastocysts in total. All embryos were used for PGT-A. Karyomapping validated the WGS results. Clinical outcomes of the 10 families were evaluated. PARTICIPANTS/MATERIALS, SETTING, METHODS: A blastomere or a few trophectoderm cells from the blastocyst were biopsied, and multiple displacement amplification (MDA) was performed. MDA DNA and bulk DNA of family members were used for library construction. Libraries were sequenced, and data analysis, including haplotype inheritance deduction for PGT-M and PGT-SR and read-count analysis for PGT-A, was performed using an in-house pipeline. Haplotyping with a proband and parent-only haplotyping without additional family members were performed to assess the WGS methodology. Concordance analysis between the WGS results and traditional PGT methods was performed. MAIN RESULTS AND THE ROLE OF CHANCE: For the 40 PGT-M and 53 PGT-A embryos, 100% concordance between the WGS and single-nucleotide polymorphism (SNP)-array results was observed, regardless of whether additional family members or a proband was included for PGT-M haplotyping. For the 13 embryos from the two PGT-SR families, the embryonic balanced translocation was detected and 100% concordance between WGS and MicroSeq with PCR-seq was demonstrated. LIMITATIONS, REASONS FOR CAUTION: The number of samples in this study was limited. In some cases, the reference embryo for PGT-M or PGT-SR parent-only haplotyping was not available owing to failed direct genotyping. WIDER IMPLICATIONS OF THE FINDINGS: WGS-based PGT-A, PGT-M and PGT-SR offered a comprehensive PGT approach for haplotyping without the requirement for additional family members. It provided an improved complementary method to PGT methodologies, such as low-depth MPS- and SNP array-based methods. STUDY FUNDING/COMPETING INTEREST(S): This research was supported by the research grant from the National Key R&D Program of China (2018YFC0910201 and 2018YFC1004900), the Guangdong province science and technology project of China (2019B020226001), the Shenzhen Birth Defect Screening Project Lab (JZF No. [2016] 750) and the Shenzhen Municipal Government of China (JCYJ20170412152854656). This work was also supported by the National Natural Science Foundation of China (81771638, 81901495 and 81971344), the National Key R&D Program of China (2018YFC1004901 and 2016YFC0905103), the Shanghai Sailing Program (18YF1424800), the Shanghai Municipal Commission of Science and Technology Program (15411964000) and the Shanghai 'Rising Stars of Medical Talent' Youth Development Program Clinical Laboratory Practitioners Program (201972). The authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Preimplantation Genetic Testing for a Chinese Family With X-Linked Lymphoproliferative Syndrome Type 1.

BACKGROUND: X-linked lymphoproliferative disease (XLP) is a rare primary immunodeficiency disorder. We performed experiments based on two strategies of preimplantation genetic testing (PGT) for a family with XLP caused by a mutation in SH2D1A (c.191G > A). METHODS: First, a single-cell polymerase chain reaction (PCR) protocol was established using single lymphocytes. A nested PCR experiment was performed with direct sequencing after whole genome amplification of single cells to assess the accuracy of the genetic diagnosis. Embryos obtained after intracytoplasmic sperm injection were biopsied on day 3 and detected using the established single-cell PCR protocol. In the second PGT cycle, targeted next generation sequencing (NGS) was performed and the single nucleotide polymorphism (SNP) markers flanking SH2D1A were selected to determine the disease-carrying haplotype phase in each embryo. RESULT: In the first PGT cycle, six embryos were biopsied. Discounting an embryo from a single failed PCR experiment, five embryos were identified, including three unaffected and two hemizygous. After PCR, one normal embryo was transferred when it was developing into an early blastocyst. Although the ultrasound images indicated a viable singleton pregnancy, the implantation was on the cesarean scar. Therefore, an artificial abortion was performed. In the haplotyping cycle, six embryos were identified to have inherited a haplotype without pathogenic mutations. After the embryo implantation process failed twice, a successful singleton pregnancy was established, and subsequently, a healthy female child was born. CONCLUSION: Targeted NGS with haplotyping analysis circumvents the laborious process of multiplex PCR and is more likely to ensure diagnostic accuracy. However, when a genetic recombination occurs close to the site of mutation, confirmed identification using selected SNP markers can be challenging.

Interactive Verification Analysis of Multiple Sequencing Data for Identifying Potential Biomarker of Lung Adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) comprises around 40% of all lung cancers, and in about 70% of patients, it has spread locally or systemically when first detected leading to a worse prognosis. METHODS: We filtered out differentially expressed genes (DEGs) based on the RNA sequencing data in the Gene Expression Omnibus database and verified and deeply analyzed screened DEGs using a combined bioinformatics approach. RESULTS: Expressions of 11,143 genes in 694 nontumor lung tissues and LUAD cases from 8 independent laboratories were analyzed; 188 mRNAs were identified as differentially expressed genes (DEGs). A PPI network constructed with 188 DEGs screened out 8 hub DEGs (CDH5, PECAM1, VWF, CLDN5, COL1A1, MMP9, SPP1, and IL6) which highly interconnected with other nodes. The expression levels of 8 hub genes in LUAD and control were assessed in the Oncomine database, and the results were consistent. The survival curves of 8 hub genes showed that their expressions are significantly related to the prognosis of lung cancer and LUAD patients except for IL6. Since the expression of IL6 is nonspecific and highly sensitive, we choose the other 7 hub genes we had verified to do the next analysis. Mutual exclusivity or cooccurrence analysis of 7 hub genes identified a tendency towards cooccurrence between CDH5, PECAM1, and VWF in LUAD. The coexpression profiles of CDH5 in LUAD were identified, and we found that PECAM1 and VWF coexpressed with CDH5. Immunohistochemistry and RT-PCR analysis showed that higher levels of CDH5, PECAM1, and VWF were expressed in normal lung tissues but a low or undetectable level was found in LUAD tissues. CONCLUSIONS: Taken together, we speculate that CDH5, PECAM1, and VWF played an important role in LUAD.

Bioinformatics analysis of gene expression profile of serous ovarian carcinomas to screen key genes and pathways.

BACKGROUND: Serous ovarian carcinomas (SCA) are the most common and most aggressive ovarian carcinoma subtype which etiology remains unclear. To investigate the prospective role of mRNAs in the tumorigenesis and progression of SCA, the aberrantly expressed mRNAs were calculated based on the NCBI-GEO RNA-seq data. RESULTS: Of 21,755 genes with 89 SCA and SBOT cases from 3 independent laboratories, 59 mRNAs were identified as differentially expressed genes (DEGs) (|log2Fold Change| > 1.585, also |FoldChange| > 3 and adjusted P < 0.05) by DESeq R. There were 26 up-regulated DEGs and 33 down-regulated DEGs screened. The hierarchical clustering analysis, functional analysis and pathway enrichment analysis were performed on all DEGs and found that Polo-like kinase (PLK) signaling events are important. PPI network constructed with different filtration conditions screened out 4 common hub genes (KIF11, CDC20, PBK and TOP2A). Mutual exclusivity or co-occurrence analysis of 4 hub genes identified a tendency towards co-occurrence between KIF11 and CDC20 or TOP2A in SCA (p < 0.05). To analyze further the potential role of KIF11 in SCA, the co-expression profiles of KIF11 in SCA were identified and we found that CDC20 co-expressed with KIF11 also is DEG that we screened out before. To verify our previous results in this paper, we assessed the expression levels of 4 hub DEGs (all up-regulated) and 4 down-regulated DEGs in Oncomine database. And the results were consistent with previous conclusions obtained from GEO series. The survival curves showed that KIF11, CDC20 and TOP2A expression are significantly related to prognosis of SCA patients. CONCLUSIONS: From all the above results, we speculate that KIF11, CDC20 and TOP2A played an important role in SCA.

RNA-sequencing and microarray data mining revealing: the aberrantly expressed mRNAs were related with a poor outcome in the triple negative breast cancer patients.

BACKGROUND: Triple negative breast cancer (TNBC) account for about 20% of breast carcinomas and the American society of clinical oncology guidelines does not specify approaches for TNBC patients since lack of specific driver molecules and targeted drugs. METHODS: We filtered out the aberrantly expressed mRNAs on the basis of RNA-seq data deposited in the Gene Expression Omnibus database, and verified and deeply analyzed screened differentially expressed genes (DEGs) using a combined bioinformatics approach. RESULTS: Of 21,755 genes with 472 TNBC cases from 3 independent laboratories, 159 mRNAs were identified as DEGs. To verify our results, we assessed the expression levels of top 8 DEGs in Oncomine database. The hierarchical clustering analysis, functional and pathway enrichment analysis were carried out for all DEGs. The results reveal that N-acetyltransferase 1 (NAT1) is most obvious of expression change's gene. Protein-protein interaction (PPI) network construction of 159 DEGs selected 3 hub genes: desmoglein 3 (DSG3), family with sequence similarity 83 member D (FAM83D) and GATA binding protein 3 (GATA3). For further analysis of the potential role of NAT1 in TNBC, the co-expression profiles of NAT1 in BC were made out, and we found that there are 5 genes [GATA3, trefoil factor 3 (TFF3), forkhead box A1 (FOXA1), signal peptide, CUB domain and EGF like domain containing 2 (SCUBE2), G protein-coupled receptor 160 (GPR160)] which co-expressed with NAT1 also were DEGs that we screened out before. Co-occurrence analysis confirmed that same as DEGs, GATA3 and SCUBE2 co-expressed with NAT1, and had a tendency towards a co-occurrence with NAT1 in TNBC. The survival curves showed that NAT1, GATA3 and SCUBE2 expression are significantly related with prognosis. CONCLUSIONS: From all above results, we speculate that NAT1, GATA3 and SCUBE2 play a vital role in TNBC.

Identification of significant biomarkers and pathways associated with gastric carcinogenesis by whole genome-wide expression profiling analysis.

The incidence of gastric cancer (GC) is extremely high in East Asia. GC is also one of the most common and lethal forms of cancer from a global perspective. However, to date, we have not been able to determine one or several genes as biomarkers in the diagnosis of GC and have also been unable to identify the genes which are important in the therapy of GC. In this study, we analyzed all genome-wide expression profiling arrays uploaded onto the Gene Expression Omnibus (GEO) database to filtrate the differentially expressed genes (DEGs) between normal stomach tissues and GC tissues. GSE13911, GSE19826 and GSE79973 were based on the GPL570 platform, and GSE29272 was based on the GPL96 platform. We screened out the DEGs from the two platforms and by selecting the intersection of these two platforms, we identified the common DEGs in the sequencing data from different laboratories. Finally, we obtained 3 upregulated and 34 downregulated DEGs in GC from 384 samples. As the number of downregulated DEGs was greater than that of the upregulated DEGs, functional analysis and pathway enrichment analysis were performed on the downregulated DEGs. Through our analysis, we identified the most significant genes associated with GC, such as secreted phosphoprotein 1 (SPP1), sulfatase 1 (SULF1), thrombospondin 2 (THBS2), ATPase H+/K+ transporting beta subunit (ATP4B), gastric intrinsic factor (GIF) and gastrokine 1 (GKN1). The prognostic power of these genes was corroborated in the Oncomine database and by Kaplan-Meier plotter (KM-plotter) analysis. Moreover, gastric acid secretion, collecting duct acid secretion, nitrogen metabolism and drug metabolism were significantly related to GC. Thus, these genes and pathways may be potential targets for improving the diagnosis and clinical effects in patients with GC.

Expression of AE1/p16 promoted degradation of AE2 in gastric cancer cells.

BACKGROUND: Human anion exchanger 1 and 2 (AE1 and AE2) mediate the exchange of Cl(-)/HCO3 (-) across the plasma membrane and regulate intracellular pH (pHi). AE1 is specifically expressed on the surface of erythrocytes, while AE2 is widely expressed in most tissues, and is particularly abundant in parietal cells. Previous studies showed that an interaction between AE1 and p16 is a key event in gastric cancer (GC) progression, but the importance of AE2 in GC is unclear. METHODS: The relationship among AE1, AE2 and p16 in GC cells was characterized by molecular and cellular experiments. AE2 expression and pHi were measured after knockdown or forced expression of AE1 or p16 in GC cells. The effect of AE2 on GC growth and the correlation of AE2 expression with differentiation and prognosis of GC were also evaluated. The effect of gastrin on AE2 expression and GC growth was investigated in cellular experiments and mouse xenograft models. RESULTS: p16 binds to both AE1 and AE2 simultaneously. AE1 or p16 silencing elevated AE2 expression on the plasma membrane where it plays a role in pHi regulation and GC suppression. AE2 expression was decreased in GC tissue, and these decreased levels were correlated with poor differentiation and prognosis of GC. The low AE2 protein levels are due to rapid ubiquitin-mediated degradation that was facilitated in the presence of p16. Gastrin inhibited the growth of GC cells at least partially through up-regulation of AE2 expression. CONCLUSION: AE1/p16 expression promoted AE2 degradation in GC cells. Gastrin is a potential candidate drug for targeted therapies for AE1- and p16-positive GC.

PCAF acts as a gastric cancer suppressor through a novel PCAF-p16-CDK4 axis.

Gastric cancer (GC) is a leading cause of cancer-related death worldwide and the pathogenesis of GC remains largely unknown. Here, we demonstrate a novel mechanism by which P300/CBP associating factor (PCAF) acts as a tumor suppressor in GC cells. We showed that both PCAF mRNA and protein were downregulated in GC cells, and that this downregulation correlated with poor survival. Meanwhile, the interaction between human anion exchanger 1 (AE1) and p16 is a key event in GC development. We found that PCAF inhibited GC growth by interacting with AE1 and p16 to promote ubiquitin-mediated degradation of AE1 and p16 upregulation and translocation into the nucleus. Binding of nuclear p16 to CDK4 prevented the CDK4-Cyclin D1 interaction to inhibit GC proliferation. Furthermore, reduced PCAF levels in GC cells were associated with intracellular alkalinization and decreased immunity. Together these results suggest that PCAF acts as a GC suppressor through a novel PCAF-p16-CDK4 axis. The downregulation of PCAF expression in GC cells that follows intracellular alkalinization and decreased immune response, indicates that GC therapies should focus on restoring PCAF levels.

[Antibacterial mechanism of sulforaphane on Escherichia coli].

OBJECTIVE: To investigate the antibacterial mechanism of sulforaphaneon (SFN) on Escherichia coli. METHODS: To determine membrane penetrability, changes of SDS-PAGE protein spectra, soluble protein and alkaline phosphatase and reducing sugar were determined. Cellular nucleic acid synthesis was detected by 4, 6- diamidino-2-phenylindole (DAPI) staining assay. RESULTS: SFN affected the membrane permeability of Escherichia coli. Ions and small molecules could leak out of the cells. But it did not destroy the membrane integrity directly. After 16 hours of treatment with SFN, the total contents of intracellular and extracellular proteins decreased by 42.5% and 17.6%, respectively, while the quantity of DNA and RNA reduced by 34.8% and 48.5% respectively. CONCLUSION: SFN can affect cell membrane permeability, material and energy metabolism and inhibit the synthesis of nucleic acid and protein.