Journal of Gene Medicine最新文献

Background

Ovarian cancer is one of the most common cancers in women. Profiles changes of microRNAs (miRNAs) are closely linked to malignant tumors. In the present study, we investigated expression of miR-451a in high-grade serous ovarian cancer (HGSOC). We also investigated the potential pathological roles and the likely mechanism of miR-451a in the development of HGSOC using animal models and cell lines.

Methods

Using bioinformatics techniques and a real-time PCR, we analyzed differently expressed miRNAs in HGSOC compared to normal tissue. MTT (i.e. 3-[4, 5-dimethyl thiazol-2-yl]-2,5-diphenyl tetrazolium bromide), EDU (i.e. 5-ethynyl-2′-deoxyuridine) and transwell assays were performed to investigate the effect of miR-451a on the proliferation and migration of HGSOC SKOV-3 cells. A dual luciferase reporter assay was performed to verify the targeting relationship of miR-451 and RAB5A (one of the Rab GTPase proteins that regulates endocytosis and vesicle transport). Also, we analyzed levels of the RAB5A mRNA and protein by real-time PCR, western blotting and immunohistochemistry assays in HGSOC cells and tissues. Finally, we performed in vivo experiments using HGSOC mice.

Results

miR-451a was substantially upregulated in HGSOC and associated with favorable clinical characteristics. miR-451a knockdown significantly increased growth and metastasis of HGSOC cell line SKOV-3 through Ras/Raf/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) signaling. In addition, RAB5A, an early endosome marker, was shown to be a direct target of miR-451a. Moreover, RAB5A is correlated with unfavorable clinical features and shows independent prognostic significance in HGSOC.

Conclusions

We found that the miR-451a/RAB5A axis is associated with tumorigenesis and progression through the Ras/Raf/MEK/ERK pathway, providing prognostic indicators and therapeutic targets for patients with HGSOC.

Background

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

Methods

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

Results

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC₅₀ values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

Conclusions

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Background

Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA).

Methods

The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the “Seurat” R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package “ConsensusClusterPlus”. The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the “glmnet” R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The “maftools” R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups.

Results

One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis.

Conclusion

Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.

Background

Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood.

Methods

We analyzed the differential expression of pyroptosis-related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC-related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

Results

We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters.

Conclusions

PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.

Background

Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women.

Methods

The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios.

Results

High PRS was significantly associated with increased RA risk in individuals aged 60–70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%–42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%–64.9%) for those in the top 5% of the PRS distribution.

Conclusions

The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.

Background

Autophagy plays an important role in immunity and inflammation. The present study aimed to explore the prognostic significance of autophagy-related genes (ARGs) in endometrial cancer (EC) using bioinformatics.

Methods

The list of ARGs was obtained from the Human Autophagy Database. The differentially expressed ARGs (DEARGs) between the EC and normal endometrial tissue samples were screened from The Cancer Genome Atlas database. Cox regression analysis was performed on the DEARGs to screen the prognostic ARGs and construct risk signatures for overall survival (OS) and progression-free survival (PFS). The hub ARGs were identified from a protein–protein interaction network, and CDKN2A was obtained from the intersection of prognostic ARGs and hub ARGs. The association of CDKN2A expression with clinical characteristics and immune infiltration were analyzed. Finally, the role of CDKN2A in autophagy was confirmed in EC cell lines.

Results

CDKN2A, PTK6 and DLC1 were used to establish risk signatures for predicting the survival of EC patients. Receiver operating characteristic curve analysis indicated that the risk signatures can accurately predict both OS and PFS. CDKN2A was the only hub prognostic ARG, and showed significant association with the age, survival status, grade, histological type, body mass index and FIGO (i.e. International Federation of Gynecology and Obstetrics) stage (p < 0.05). Furthermore, CDKN2A expression was also correlated with the infiltration of immune cells, indicating that CDKN2A might play a critical role in regulating the immune microenvironment and immune responses in EC. In addition, silencing of CDKN2A gene promoted autophagy in the HEC-1A cell line and upregulated the expression levels of autophagy-related proteins.

Conclusions

CDKN2A is a prognostic factor and therapeutic target in EC, and is likely associated with the tumor immune landscape and autophagy.

Background

The induction of cardiomyocyte (CM) proliferation is a promising approach for cardiac regeneration following myocardial injury. MicroRNAs (miRNAs) have been reported to regulate CM proliferation. In particular, miR-431 expression decreases during cardiac development, according to Gene Expression Omnibus (GEO) microarray data. However, whether miR-431 regulates CM proliferation has not been thoroughly investigated.

Methods

We used integrated bioinformatics analysis of GEO datasets to identify the most significantly differentially expressed miRNAs. Real-time quantitative PCR and fluorescence in situ hybridization were performed to determine the miRNA expression patterns in hearts. Gain- and loss-of-function assays were conducted to detect the role of miRNA in CM proliferation. Additionally, we detected whether miR-431 affected CM proliferation in a myocardial infarction model. The TargetScan, miRDB and miRWalk online databases were used to predict the potential target genes of miRNAs. Luciferase reporter assays were used to study miRNA interactions with the targeting mRNA.

Results

First, we found a significant reduction in miR-431 levels during cardiac development. Then, by overexpression and inhibition of miR-431, we demonstrated that miR-431 promotes CM proliferation in vitro and in vivo, as determined by immunofluorescence assays of 5-ethynyl-2'-deoxyuridine (EdU), pH3, Aurora B and CM count, whereas miR-431 inhibition suppresses CM proliferation. Then, we found that miR-431 improved cardiac function post-myocardial infarction. In addition, we identified FBXO32 as a direct target gene of miR-431, with FBXO32 mRNA and protein expression being suppressed by miR-431. FBXO32 inhibited CM proliferation. Overexpression of FBXO32 blocks the enhanced effect of miR-431 on CM proliferation, suggesting that FBXO32 is a functional target of miR-431 during CM proliferation.

Conclusion

In summary, miR-431 promotes CM proliferation by targeting FBXO32, providing a potential molecular target for preventing myocardial injury.

Background

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers.

Methods

ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation.

Results

WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS.

Conclusions

ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

Background

A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets.

Method

The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule-related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner.

Results

This study also assessed the relationship between costimulating molecules and immunotherapy response. The high-risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response.

Conclusion

A costimulatory molecule-based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.

Background

The aim of this study was to determine the effect of human urine-derived stem cells (HUSCs) for the treatment of spinal cord injury (SCI) and investigate associated the molecular network mechanism by using bioinformatics combined with experimental validation.

Methods

After the contusive SCI model was established, the HUSC-expressed specific antigen marker was implanted into the injury site immediately, and the Basso, Beattie and Bresnahan locomotor rating scale (BBB scale) was utilized to evaluate motor function so as to determine the effect of HUSCs for the neural repair after SCI. Then, the geneCards database was used to collect related gene targets for both HUSCs and SCI, and cross genes were merged with the findings of PubMed screen. Subsequently, protein–protein interaction (PPI) network, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment, as well as core network construction, were performed using Cytoscape software. Lastly, real-time quantitative polymerase chain reaction (PCR) and immunofluorescence were employed to validate the mRNA expression and localization of 10 hub genes, and two of the most important, designated as cadherin 1 (CDH1) and integrin subunit beta 1 (ITGB1), were identified successfully.

Results

The immunophenotypes of HUSCs were marked by CD90+ and CD44+ but not CD45, and flow cytometry confirmed their character. The expression rates of CD90, CD73, CD44 and CD105 in HUSCs were 99.49, 99.77, 99.82 and 99.51%, respectively, while the expression rates of CD43, CD45, CD11b and HLA-DR were 0.08, 0.30, 1.34 and 0.02%, respectively. After SCI, all rats appeared to have severe motor dysfunction, but the BBB score was increased in HUSC-transplanted rats compared with control rats at 28 days. By using bioinformatics, we obtained 6668 targets for SCI and 1095 targets for HUSCs and identified a total of 645 cross targets between HUSCs and SCI. Based on the PPI and Cytoscape analysis, CD44, ACTB, FN1, ITGB1, HSPA8, CDH1, ALB, HSP90AA1 and GAPDH were identified as possible therapeutic targets. Enrichment analysis revealed that the involved signal pathways included complement and coagulation cascades, lysosome, systemic lupus erythematosus, etc. Lastly, quantificational real-time (qRT)-PCR confirmed the mRNA differential expression of CDH1/ITGB1 after HUSC therapy, and glial fibrillary acidic protein (GFAP) immunofluorescence staining showed that the astrocyte proliferation at the injured site could be reduced significantly after HUSC treatment.

Conclusions