Genomics and Prognosis Analysis of Epithelial-Mesenchymal Transition in Glioma

Predicting immune status and gene mutations in stomach adenocarcinoma patients based on inflammatory response-related prognostic features

BACKGROUND

Stomach adenocarcinoma (STAD) is an aggressive malignant tumor. Herein, we characterized the prognosis based on inflammatory response-related features and evaluated their potential impact on survival and immune status of STAD patients.

METHODS

Inflammation-related genes obtained from public databases were used to analyze the inflammatory response scores of STAD samples. The differentially expressed genes (DEGs) between STAD and adjacent gastric tissue were then analyzed using the "limma" package. Genes associated with STAD prognosis were obtained from the intersection of inflammation-related genes and DEGs. The key genes screened by last absolute shrinkage and selection operator (LASSO) Cox and stepwise regression analyses were used to construct prognostic models and nomograms. The tumor immune dysfunction exclusion (TIDE) algorithm was used to assess potential differences in immunotherapy response between high- and low-risk groups and to explore gene mutation signatures using the R software maftools package. In addition, GSEA was used to predict pathway characteristics between different subgroups. Finally, scratch and transwell assays were performed to explore the role of SERPINE1 in STAD cells.

RESULTS

We found that a high-inflammatory group was associated with poor prognosis in STAD patients. 14 inflammation-related DEGs out of 126 DEGs were identified to be associated with the prognosis of STAD patients, and the prognostic models and nomograms constructed from the subsequently identified key genes (SLC7A1, CD82, SERPINE1 ROS1 and SLC7A2) demonstrated a good predictive performance in terms of prognosis of STAD. Patients in the STAD high-risk group had higher StromalScore and TIDE scores. It was also found that patients in the STAD low-risk group may have a higher mutation burden. Enrichment analysis revealed significant enrichment of epithelial-mesenchymal transition, angiogenesis and KRAS pathways in the high-risk group. In-vitro experiments showed that down-regulation of SERPINE1 attenuated the migratory and invasive abilities of AGS cells.

CONCLUSION

This study provides new insights into prognostic prediction and immunotherapy for STAD from the perspective of the inflammatory response.

Molecular subtypes based on immunologic and epithelial-mesenchymal transition gene sets reveal tumor immune microenvironment characteristics and implications for immunotherapy of patients with glioma

The tumor immune microenvironment (TIME) significantly influences cancer progression and treatment. This study sought to uncover novel TIME-related glioma biomarkers to advance antitumor immunotherapies by integrating data from sequencing of bulk RNA as well as scRNA. Immunologic and epithelial-mesenchymal transition (EMT) characteristics were used to classify glioma patients into two immune subtypes (ISs) and two EMT subtypes (ESs). Patients in IS1 and ES1, characterized by high immune infiltration and low stemness scores, exhibited poor clinical outcomes and limited responsiveness to immunotherapy. A new risk signature was developed using 16 genes and validated in independent glioma cohorts. Among these, HAVCR2, IL18, LAGLS9, and PTPN6 emerged as hub genes, with IL18 identified as a potential independent indicator. The upregulation of IL18 in high-grade gliomas and U-251 MG cells aligned with bioinformatics analysis. These insights deepen the understanding of TIME-related mechanisms in glioma and highlight potential therapeutic targets, offering a theoretical foundation for effective antitumor immunotherapies in glioma.

AC099850.3 promotes HBV-HCC cell proliferation and invasion through regulating CD276: a novel strategy for sorafenib and immune checkpoint combination therapy

BACKGROUND

This study investigates the molecular mechanisms of CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and sorafenib (SF) for improving hepatitis B virus-related hepatocellular carcinoma (HBV-HCC).

METHODS

A dataset of 44 HBV-HCC patients and their survival information was selected from the TCGA database. Immune genes related to survival status were identified using the ImmPort database and WGCNA analysis. A prognostic risk model was constructed and analyzed using Lasso regression. Differential analysis was performed to screen key genes, and their significance and predictive accuracy for HBV-HCC were validated using Kaplan-Meier survival curves, ROC analysis, CIBERSORT analysis, and correlation analysis. The correlation between AC099850.3 and the gene expression matrix was calculated, followed by GO and KEGG enrichment analysis using AC099850.3 and its co-expressed genes. HepG2.2.15 cells were selected for in vitro validation, and lentivirus interference, cell cycle determination, CCK-8 experiments, colony formation assays, Transwell experiments, scratch experiments, and flow cytometry were performed to investigate the effects of key genes on HepG2.2.15 cells. A subcutaneous transplanted tumor model in mice was constructed to verify the inhibitory effect of key genes on HBV-HCC tumors. Subsequently, pH-triggered drug release NPs (CC@AC&SF@PP) were prepared, and their therapeutic effects on HBV-HCC in situ tumor mice were studied.

RESULTS

A prognostic risk model (AC012313.9, MIR210HG, AC099850.3, AL645933.2, C6orf223, GDF10) was constructed through bioinformatics analysis, showing good sensitivity and specificity in diagnostic prediction. AC099850.3 was identified as a key gene, and enrichment analysis revealed its impact on cell cycle pathways. In vitro cell experiments demonstrated that AC099850.3 promotes HepG2.2.15 cell proliferation and invasion by regulating immune checkpoint CD276 expression and cell cycle progression. In vivo, subcutaneously transplanted tumor experiments showed that AC099850.3 promotes the growth of HBV-HCC tumors in nude mice. Furthermore, pH-triggered drug release NPs (CC@AC&SF@PP) loaded with AC099850.3 siRNA and SF were successfully prepared and delivered to the in situ HBV-HCC, enhancing the effectiveness of combined therapy for HBV-HCC.

CONCLUSIONS

AC099850.3 accelerates the cell cycle progression and promotes the occurrence and development of HBV-HCC by upregulating immune checkpoint CD276 expression. CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and SF improve the effectiveness of combined therapy for HBV-HCC.

Epigenetic remodeling to improve the efficacy of immunotherapy in human glioblastoma: pre-clinical evidence for development of new immunotherapy approaches

BACKGROUND

Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor, that is refractory to standard treatment and to immunotherapy with immune-checkpoint inhibitors (ICI). Noteworthy, melanoma brain metastases (MM-BM), that share the same niche as GBM, frequently respond to current ICI therapies. Epigenetic modifications regulate GBM cellular proliferation, invasion, and prognosis and may negatively regulate the cross-talk between malignant cells and immune cells in the tumor milieu, likely contributing to limit the efficacy of ICI therapy of GBM. Thus, manipulating the tumor epigenome can be considered a therapeutic opportunity in GBM.

METHODS

Microarray transcriptional and methylation profiles, followed by gene set enrichment and IPA analyses, were performed to study the differences in the constitutive expression profiles of GBM vs MM-BM cells, compared to the extracranial MM cells and to investigate the modulatory effects of the DNA hypomethylating agent (DHA) guadecitabine among the different tumor cells. The prognostic relevance of DHA-modulated genes was tested by Cox analysis in a TCGA GBM patients' cohort.

RESULTS

The most striking differences between GBM and MM-BM cells were found to be the enrichment of biological processes associated with tumor growth, invasion, and extravasation with the inhibition of MHC class II antigen processing/presentation in GBM cells. Treatment with guadecitabine reduced these biological differences, shaping GBM cells towards a more immunogenic phenotype. Indeed, in GBM cells, promoter hypomethylation by guadecitabine led to the up-regulation of genes mainly associated with activation, proliferation, and migration of T and B cells and with MHC class II antigen processing/presentation. Among DHA-modulated genes in GBM, 7.6% showed a significant prognostic relevance. Moreover, a large set of immune-related upstream-regulators (URs) were commonly modulated by DHA in GBM, MM-BM, and MM cells: DHA-activated URs enriched for biological processes mainly involved in the regulation of cytokines and chemokines production, inflammatory response, and in Type I/II/III IFN-mediated signaling; conversely, DHA-inhibited URs were involved in metabolic and proliferative pathways.

CONCLUSIONS

Epigenetic remodeling by guadecitabine represents a promising strategy to increase the efficacy of cancer immunotherapy of GBM, supporting the rationale to develop new epigenetic-based immunotherapeutic approaches for the treatment of this still highly deadly disease.

Retinol dehydrogenase 10 promotes epithelial-mesenchymal transition in spinal cord gliomas via PI3K/AKT pathway

Background: Spinal cord glioma (SCG), a rare subset of central nervous system (CNS) glioma, represents a complex challenge in neuro-oncology. There has been research showing that Retinol Dehydrogenase 10 (RDH10) may be a tumor promoting factor in brain glioma, but the biological effects of RDH10 remain undefined in SCG. Methods: We performed gene set enrichment analysis (GSEA) and unsupervised clustering analysis to investigate the roles of EMT (epithelial-mesenchymal transition) in glioma. DEG (differently expressed gene) screening and correlation analysis were conducted to filter the candidate genes which were closely associated with EMT process in SCG. Enrichment analysis and GSVA (Gene Set Variation Analysis) were conducted to investigate the potential mechanism of RDH10 for SCG. Trans-well and healing assay were performed to explore the role of RDH10 in the invasion of SCG. Western blotting was performed to evaluate the levels of markers in PI3K-AKT and EMT pathway. In vivo tests were conducted to verify the role of RDH10 in EMT process. Results: Bioinformatic analysis demonstrated the EMT pathway was associated with dismal prognosis of glioma. Further analysis demonstrated that RDH10 showed the strongest correlation with the EMT process. Retinol Dehydrogenase 10 expression was significantly increased in SCG tissues, correlating with advanced tumor grade and unfavorable prognosis. Functional analysis indicated that decreasing RDH10 levels impeded the invasive and migratory abilities of SCG cells, whereas increasing RDH10 levels augmented them. Enrichment analysis and western blot revealed that RDH10 regulated EMT process of SCG by PI3K-AKT pathway. We observed that the enhanced invasion ability and increased EMT-related protein induced by RDH10 overexpression can be suppressed by PI3K-AKT pathway inhibitor (LY294002). Conclusion: Our research found that RDH10 was an effective biomarker associated with tumor grade and prognosis of SCG. RDH10 could regulate EMT process of SCG through PI3K-AKT pathway.

A novel FGFR1 inhibitor CYY292 suppresses tumor progression, invasion, and metastasis of glioblastoma by inhibiting the Akt/GSK3β/snail signaling axis

Glioblastoma (GBM) is a malignant brain tumor that grows quickly, spreads widely, and is resistant to treatment. Fibroblast growth factor receptor (FGFR)1 is a receptor tyrosine kinase that regulates cellular processes, including proliferation, survival, migration, and differentiation. FGFR1 was predominantly expressed in GBM tissues, and FGFR1 expression was negatively correlated with overall survival. We rationally designed a novel small molecule CYY292, which exhibited a strong affinity for the FGFR1 protein in GBM cell lines in vitro. CYY292 also exerted an effect on the conserved Ser777 residue of FGFR1. CYY292 dose-dependently inhibited cell proliferation, epithelial-mesenchymal transition, stemness, invasion, and migration in vitro by specifically targeting the FGFR1/AKT/Snail pathways in GBM cells, and this effect was prevented by pharmacological inhibitors and critical gene knockdown. In vivo experiments revealed that CYY292 inhibited U87MG tumor growth more effectively than AZD4547. CYY292 also efficiently reduced GBM cell proliferation and increased survival in orthotopic GBM models. This study further elucidates the function of FGFR1 in the GBM and reveals the effect of CYY292, which targets FGFR1, on downstream signaling pathways directly reducing GBM cell growth, invasion, and metastasis and thus impairing the recruitment, activation, and function of immune cells.

Has-miR-300-GADD45B promotes melanoma growth via cell cycle

Response to oncogenic factors like UV, GADD45 family in skin participates in scavenging ROS, DNA repair and cell cycle control. Because of this, the previous study of the chronic UVB injury model has found that hsa-miR-300 can conduct intercellular transport by exosomes and target regulation of GADD45B. Whether the hsa-miR-300-GADD45B still regulates tumor development by cell cycle pathway is unclear. Through transcriptomic analysis of primary (n=39) and metastatic (n=102) melanoma, it was confirmed that in metastatic samples, some of the 97 down-regulated genes participate in maintaining skin homeostasis while 42 up-regulated genes were enriched in cancer-related functions. Furthermore, CDKN1A, CDKN2A, CXCR4 and RAD51 in the melanoma pathway, were also differentially expressed between normal skin and melanoma. CDKN1A and CDKN2A were also found to be involved in TP53-dependent cell cycle regulation. In conclusion, it was speculated that CDKN1A, CDKN2A, TP53, GADD45B and hsa-miR-300 may have regulatory relationships. It was demonstrated that there is a bidirectional regulation between hsa-miR-300 and TP53. In addition, miR-300 can regulate CDKN1A by GADD45B/TP53 and promote melanoma growth by accelerating the cell cycle transition from G1/S to G2 phase.

miR-4780 Derived from N2-Like Neutrophil Exosome Aggravates Epithelial-Mesenchymal Transition and Angiogenesis in Colorectal Cancer

Despite significant advances in diagnostic methods and treatment strategies, the prognosis for patients with advanced colon cancer remains poor, and mortality rates are often high due to metastasis. Increasing evidence showed that it is of significant importance to investigate how the tumor microenvironment participates in the development of colorectal cancer (CRC). In this manuscript, neutrophils were sequentially stimulated with all-trans retinoic acid and transforming growth factor-β in turn to induce the neutrophil polarization. Differentially expressed miRNA in neutrophil exosomes have been sequenced by microarray profile, and the effect of N2-like neutrophil-derived exosomal miR-4780 on epithelial-mesenchymal transition (EMT) and angiogenesis was investigated. In our results, we found that neutrophils were enriched in CRC tumor tissue and that CD11b expression correlated with tumor site and serous membrane invasion. At the same time, we demonstrated that internalization of N2 exosomes exacerbated the viability, migration, and invasion of CRC cell lines and inhibited apoptosis. To further investigate the molecular mechanism, we analyzed the miRNA expression profile in the N2-like neutrophils, which led to the selection of hsa-miR-4780 for the subsequent experiment. The overexpression of miR-4780 from N2-like neutrophil-derived exosomes exacerbated EMT and angiogenesis. Moreover, miR-4780 can regulate its target gene SOX11 to effect EMT and angiogenesis in CRC cell lines. CRC with liver metastasis model also validated that aberrant expression of miR-4780 in N2-like neutrophil exosomes exacerbated tumor metastasis and development of tumor via EMT and angiogenesis. In conclusion, our current findings reveal an important mechanism by which mR-4780 from N2-like neutrophil exosomes exacerbates tumor metastasis and progression via EMT and angiogenesis.

Screening of renal clear cell carcinoma prognostic marker genes based on TCGA and GTEx chip data and construction of transcription factor-related regulatory networks

This study aimed to identify prognostic marker genes for renal clear cell carcinoma (RCCC) and construct a regulatory network of transcription factors and prognostic marker genes. Three hundred eighty-six genes were significantly differentially expressed in RCCC, with functional enrichment analysis suggesting a relationship between these genes and kidney function and development. Cox and Lasso regression analyses revealed 10 prognostic marker genes (RNASET2, MSC, DPEP1, FGF1, ATP1A1, CLDN10, PLG, SLC44A1, PCSK1N, and LGI4) that accurately predicted RCCC patient prognosis. Upstream transcription factors of these genes were also identified, and in vitro experiments suggested that ATP1A1 may play a key role in RCCC patient prognosis. The findings of this study provide important insights into the molecular mechanisms of RCCC and may have implications for personalized treatment strategies.

TMEM2 induces epithelial-mesenchymal transition and promotes resistance to temozolomide in GBM cells

Glioblastoma multiforme (GBM) is the most common intracranial malignant tumor and is notorious for its poor prognosis. An important element in the short overall survival of GBM patients is the lack of understanding the pathogenesis and progression of tumor and deficiency biomarkers that can be used for early diagnosis and therapeutic sensitivity monitoring. Studies have shown that transmembrane protein 2 (TMEM2) is participated in tumorigenesis of various human tumors, including rectal and breast cancers. Although Qiuyi Jiang et al. have reported that TMEM2 combined with IDH1/2 and 1p19q can predict the survival time of glioma patients based on bioinformatics, its expression and biological role of glioma remain unclear. In our study, we investigated the effect of TMEM2 expression level on glioma malignancy in public datasets and an independent internal dataset. We revealed TEMM2 expression was higher in GBM tissues than in non-tumor brain tissues (NBT). Moreover, the increase in TMEM2 expression level was closely related to tumor malignancy. The survival analysis showed that TMEM2 high expression reduces survival time in all glioma patients, including GBM and LGG patients. Subsequent experiments demonstrated that knockdown TMEM2 inhibited proliferation of GBM cells. In addition, we analyzed TMEM2 mRNA levels in different GBM subtypes, and demonstrated that TMEM2 expression was upregulated in mesenchymal subtype. Meanwhile, bioinformatics analysis and transwell assay indicated that knockdown TMEM2 suppressed epithelial-mesenchymal transition (EMT) in GBM. Importantly, Kaplan-Meier analysis demonstrated that TMEM2 high expression reduced the treatment response to TMZ in GBM patients. Knockdown of TMEM2 alone did not reduce apoptosis GBM cells, but significant apoptotic cells were observed in the group treated with additional TMZ. These studies may contribute to improving the accuracy of early diagnosis and evaluating the effectiveness of TMZ treatment in GBM patients.

GAS5 attenuates the malignant progression of glioma stem-like cells by promoting E-cadherin

It has been widely reported that glioma stem-like cells (GSCs) serve a crucial role in the malignant progression of glioma. In particular, recent studies have reported that long non-coding RNAs (lncRNAs) are closely associated with glioma development. However, the underlying molecular regulatory mechanistic role of GSCs remains poorly understood. The present study established two highly malignant glioma stem-like cell lines from clinical surgical specimens. In these, it was found that the lncRNA growth arrest-specific 5 (GAS5) expression was downregulated in GSCs and high-grade glioma tissues, compared with normal human astrocyte cells (NHAs) and normal brain tissues, respectively, which also showed a positive correlation with patient survival. Functional assays revealed that knocking down GAS5 expression promoted the proliferation, invasion, migration, stemness, and tumorigenicity of GSGs, while suppressing their apoptosis. Mechanistically, GAS5 directly sponged miR-23a, which in turn functioned as an oncogene by inhibiting E-cadherin, through the assays of reverse transcription-quantitative PCR (RT-qPCR) and luciferase reports. In addition, rescue experiments demonstrated that GAS5 could promote the expression and function of E-cadherin in a miR-23a-dependent manner. Collectively, these data suggest that GAS5 functions as a suppressor in GSCs by targeting the miR-23a/E-cadherin axis, which may be a promising therapeutic target against glioma.

Identification of a robust scoring system based on metabolic genes followed by in-depth validation of ATP1A3 in glioma

AIMS

In the past few decades, the prognosis of glioma patients has not significantly improved. Therefore, to provide more precise medical services for glioma patients, it is urgent to identify more clinically meaningful subtypes, establish more robust clinical prediction models, and find more effective therapeutic targets.

MATERIALS AND METHODS

Four distinct metabolic-associated subtypes were identified by the NMF algorithm based on metabolic genes (MEGs). A robust scoring system was constructed based on the differentially expressed genes (DEGs) screened from the four metabolic-associated subtypes with the LASSO regression algorithm and multivariate Cox regression analysis. Further analysis of scoring systems was done by different R packages. In addition, the ATP1A3 gene was screened and bioinformatics analysis of it was conducted on several public websites. GSEA software was utilized to search hallmark signaling pathways closely related to ATP1A3. Cytological experiments were used to investigate the role of ATP1A3 in the malignant progression of glioblastoma (GBM) cells.

KEY FINDINGS

Four metabolic-associated subtypes with significantly different clinicopathological characteristics were identified, and a robust scoring system with outstanding clinical application value was established. In addition, a tumor suppressor gene ATP1A3 was found, which is expected to be a potential therapeutic target for glioma.

SIGNIFICANCE

This study is of great significance in the diagnosis, prognosis, and prediction of the response to immune checkpoint blockers (ICBs) for glioma patients. More importantly, this study found a potential therapeutic target for glioma.

Investigating the change in gene expression profile of blood mononuclear cells post-laparoscopic sleeve gastrectomy in Chinese obese patients

BACKGROUND

Laparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms.

METHODS

Peripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m² in the Department of General Surgery of Kunming First People's Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-Seq data were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases.

RESULTS

We identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P.

CONCLUSION

This study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.

Construction and validation of a novel signature based on epithelial-mesenchymal transition-related genes to predict prognosis and immunotherapy response in hepatocellular carcinoma by comprehensive analysis of the tumor microenvironment

Immunotherapy has yielded encouraging results in the treatment of advanced hepatocellular carcinoma (HCC). However, the relationship between epithelial-mesenchymal transition (EMT) and immunotherapy for HCC has not been adequately explained. In this study, we comprehensively analyzed a bulk RNA sequence dataset of 365 HCC patients in The Cancer Genome Atlas (TCGA) dataset. Subsequently, we constructed a prognostic signature based on 6 EMT-related genes and divided 365 HCC patients into high- and low-risk groups. The predictive efficacy of the signature was well validated in different clinical subgroups and in two independent external datasets. We further explored the relationship between prognostic signature and immunotherapy response in terms of immune cell infiltration, somatic mutations, tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint-associated gene expression, single-nucleotide variants (SNV) neoantigens, cancer testicular antigens (CTA) scores, and tumor immune dysfunction and exclusion (TIDE) scores. We validated the predictive efficacy of prognostic signature for immunotherapy response using external independent immunotherapy data. Real-time quantitative polymerase chain reaction (qRT-PCR) was used to validate EMT-related gene overexpression in HCC tissue samples. Prognostic signature was an independent risk factor affecting the prognosis of HCC patients and has shown superiority in predicting patient survival compared to other clinical factors. Compared with the low-risk group, the proportion of Activated_CD4_T_cell, Type_2_T_helper_cel, and macrophages were higher in the tumor microenvironment of HCC patients in the high-risk group, while the Activated_CD8_T_cell and CD56bright_natural_killer_cell proportions were lower. The prognostic signature was positively correlated with TMB scores, MSI scores, SNV neoantigens scores, expression levels of immune checkpoint-related genes, and TIDE scores, and patients in the high-risk group were more suitable for immunotherapy. qRT-PCR confirms overexpression of 6 EMT-related genes in HCC tissues for the construction of prognostic signature. Our novel prognostic signature can effectively predict the prognosis and immunotherapy response of HCC patients. In the future, it will be an effective tool for physicians to screen suitable immunotherapy populations and improve response rates and overall survival (OS).

The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance

The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.

Comprehensive analysis of the prognostic signature and tumor microenvironment infiltration characteristics of cuproptosis-related lncRNAs for patients with colon adenocarcinoma

Background

Cuproptosis, a newly described method of regulatory cell death (RCD), may be a viable new therapy option for cancers. Long noncoding RNAs (lncRNAs) have been confirmed to be correlated with epigenetic controllers and regulate histone protein modification or DNA methylation during gene transcription. The roles of cuproptosis-related lncRNAs (CRLs) in Colon adenocarcinoma (COAD), however, remain unknown.

Methods

COAD transcriptome data was obtained from the TCGA database. Thirteen genes associated to cuproptosis were identified in published papers. Following that, correlation analysis was used to identify CRLs. The cuproptosis associated prognostic signature was built and evaluated using Lasso regression and COX regression analysis. A prognostic signature comprising six CRLs was established and the expression patterns of these CRLs were analyzed by qRT-PCR. To assess the clinical utility of prognostic signature, we performed tumor microenvironment (TME) analysis, mutation analysis, nomogram generation, and medication sensitivity analysis.

Results

We identified 49 prognosis-related CRLs in COAD and constructed a prognostic signature consisting of six CRLs. Each patient can be calculated for a risk score and the calculation formula is: Risk score =TNFRSF10A-AS1 * (-0.2449) + AC006449.3 * 1.407 + AC093382.1 *1.812 + AC099850.3 * (-0.0899) + ZEB1-AS1 * 0.4332 + NIFK-AS1 * 0.3956. Six CRLs expressions were investigated by qRT-PCR in three colorectal cancer cell lines. In three cohorts, COAD patients were identified with different risk groups, with the high-risk group having a worse prognosis than the low-risk group. Furthermore, there were differences in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups. We also identified certain drugs that were more sensitive to the high-risk group: Paclitaxel, Vinblastine, Sunitinib and Elescloml.

Conclusions

Our findings may be used to further investigate RCD, comprehension of the prognosis and tumor microenvironment infiltration characteristics in COAD.

Machine learning predicting mortality in sarcoidosis patients admitted for acute heart failure

Background

Sarcoidosis with cardiac involvement, although rare, has a worse prognosis than sarcoidosis involving other organ systems.

Objective

We used a large dataset to train machine learning models to predict in-hospital mortality among sarcoidosis patients admitted with heart failure (HF).

Method

Utilizing the National Inpatient Sample, we identified 4659 patients hospitalized with a primary diagnosis of HF. In this cohort, we identified patients with a secondary diagnosis of sarcoidosis using International Statistical Classification of Disease, Tenth Revision (ICD-10) codes. Patients were separated into a training group and a testing group in a 7:3 ratio. Least absolute shrinkage and selection operator regression was used to select variables to prevent model overfitting or underfitting. For machine learning models, logistic regression, random forest, and XGBoosting were applied in the training group. Parameters in each of the models were tuned using the GridSearchCV function. After training, all models were further validated in the testing group. Models were then evaluated using the area under curve (AUC) score, sensitivity, and specificity.

Results

A total of 2.3% of sarcoidosis patients died in HF admission. Our machine learning model analysis found the RF model to have the highest AUC score and sensitivity. Feature analysis found that comorbid arrhythmias and fluid electrolyte disorders were the strongest factors in predicting in-hospital mortality.

Conclusion

Machine learning methods can be useful in identifying predictors of in-hospital mortality in a given dataset.

Correlation of Matrisome-Associatted Gene Expressions with LOX Family Members in Astrocytomas Stratified by IDH Mutation Status

Tumor cell infiltrative ability into surrounding brain tissue is a characteristic of diffusely infiltrative astrocytoma and is strongly associated with extracellular matrix (ECM) stiffness. Collagens are the most abundant ECM scaffolding proteins and contribute to matrix organization and stiffness. LOX family members, copper-dependent amine oxidases, participate in the collagen and elastin crosslinking that determine ECM tensile strength. Common IDH mutations in lower-grade gliomas (LGG) impact prognosis and have been associated with ECM stiffness. We analyzed the expression levels of LOX family members and matrisome-associated genes in astrocytoma stratified by malignancy grade and IDH mutation status. A progressive increase in expression of all five LOX family members according to malignancy grade was found. LOX, LOXL1, and LOXL3 expression correlated with matrisome gene expressions. LOXL1 correlations were detected in LGG with IDH mutation (IDH^mut), LOXL3 correlations in LGG with IDH wild type (IDH^wt) and strong LOX correlations in glioblastoma (GBM) were found. These increasing correlations may explain the increment of ECM stiffness and tumor aggressiveness from LGG-IDH^mut and LGG-IDH^wt through to GBM. The expression of the mechanosensitive transcription factor, β-catenin, also increased with malignancy grade and was correlated with LOXL1 and LOXL3 expression, suggesting involvement of this factor in the outside–in signaling pathway.

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery

BACKGROUND

Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS.

METHODS

The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters.

RESULTS

A total of 1,205 patients were included and divided into three clusters. Cluster 1 (n = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 (n = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97-4.46)]. Cluster 3 (n = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9-8.5)].

CONCLUSION

Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

The prognostic value of immune-related genes AZGP1, SLCO5A1, and CTF1 in Uveal melanoma

Objective

Uveal melanoma (UM) is an aggressive malignancy with a poor prognosis and no available effective treatment. Therefore, exploring a potential prognostic marker for UM could provide new possibilities for early detection, recurrence, and treatment.

Methods

In this study, we used “ConsensusClusterPlus” to classify patients with UM into subgroups, screened for significant differences in immune prognostic factors between subgroups, selected three genes using LASSO (Least absolute shrinkage and selection operator) regression to construct a risk model, and performed tumor immune cell infiltration analysis on the risk model. infiltration analysis, and then verified the heterogeneous role of the 3 core genes in other cancers by pan-cancer analysis and validate its expression by RT-qPCR in normal and tumor cells.

Results

We consistently categorized 80 UM patients into two subgroups after the immunogenetic set, where the UM1 subgroup had a better prognosis than the UM2 subgroup, and used 3 immune-related genes AZGP1, SLCO5A1, and CTF1 to derive risk scores as independent prognostic markers and predictors of UM clinicopathological features. We found significant differences in overall survival (OS) between low- and high-risk groups, and prognostic models were negatively correlated with B cell and myeloid dendritic cell and positively correlated with CD8+ T cell AZGP1 and CTF1 were significantly upregulated in UM cells compared with normal UM cells.

Conclusion

Immunogens are significantly associated with the prognosis of UM, and further classification based on genetic characteristics may help to develop immunotherapeutic strategies and provide new approaches to develop customized treatment strategies for patients.

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

The clinical significance, immune infiltration, and tumor mutational burden of angiogenesis-associated lncRNAs in kidney renal clear cell carcinoma

Background

Poor prognosis of kidney renal clear cell carcinoma (KIRC) is often related to angiogenesis. The lncRNAs that regulate angiogenesis could also affect the prognosis of KIRC. It is meaningful for us to use lncRNAs related to angiogenesis to construct a generic, individualized prognostic signature for patients with KIRC.

Methods

We identified eight angiogenesis-associated genes (AAGs) by differential expression analysis and univariate Cox regression from The Cancer Genome Atlas dataset, including 537 KIRC samples and 72 normal samples. In total, 23 prognostic lncRNAs were screened out after Pearson correlation analysis and univariate Cox regression analysis. Then, we performed least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to establish a four-AAG-related lncRNA prognostic signature.

Results

The risk score was calculated for each KIRC patients by using a four-AAG-related lncRNA prognostic signature. We divided the KIRC patients into high- and low-risk groups by the median of the risk score. It was confirmed that the AAG-related lncRNA prognostic signature has good prognostic value for KIRC patients by time-dependent receiver operating characteristic and Kaplan–Meier survival analysis. We identified 3,399 differentially expressed genes between the high- and low-risk groups and performed their functional enrichment analyses. The AAG-related lncRNA prognostic signature was an independent prognostic predictor for KIRC patients and was used to perform a combined nomogram. We reevaluated them in terms of survival, clinic characteristics, tumor-infiltrating immune cells and tumor mutation burden.

Conclusion

Our research indicates that the AAG-related lncRNA prognostic signature is a promising and potential independent prognostic indicator for KIRC patients. Then, it could offer new insights into the prognosis assessment and potential treatment strategies of KIRC patients.

Identification of Prognostic and Tumor Microenvironment by Shelterin Complex-Related Signatures in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the oral cavity. Shelterin complex gene (SG) has an important role in regulating telomere structure and length. SG is considered promising as a novel prognostic marker for cancer and a potential target for tumor therapy. However, SGs have not been systematically studied in OSCC. We analyzed SGs based on public data from OSCC patients and showed that SGs are closely associated with the prognosis of OSCC patients. Two different subtypes of SGs were identified in the TCGA and GEO cohorts, and LASSO regression analysis was used to further construct an SGs-related prognostic model. Randomized cohorts and different clinical subgroups validated the model's accuracy. The assessment of clinical characteristics, tumor mutational burden (TMB), and tumor microenvironment (TME) between high- and low-risk scores groups showed lower TMB, more abundant immune cell infiltration, and better prognosis in the low-risk group. According to the IPS analysis, patients in the low-risk group were more responsive to immunotherapy. This study establishes a foundation for research on SG and confirms that risk scores can predict prognosis and guide clinical treatment in OSCC patients.

Identification of a novel lipid metabolism-related gene signature within the tumour immune microenvironment for breast cancer

Background

Systemic factors can strongly affect how tumour cells behave, grow, and communicate with other cells in breast cancer. Lipid metabolic reprogramming is a systemic process that tumour cells undergo; however, the formation and dynamics of lipids associated with the tumour immune microenvironment (TIME) remain unclear. The investigation of the sophisticated bidirectional crosstalk of tumour cells with cancer metabolism, gene expression, and TIME could have the potential to identify novel biomarkers for diagnosis, prognosis, and immunotherapy. This study aimed to construct a prognostic signature to detect the bicrosstalk between the lipid metabolic system and the TIME of breast cancer.

Methods

To detect the expression of LRGs and execute GO/KEGG analysis, the R program was chosen. Considering the clinical information and pathological features, a prognostic gene signature was constructed by LASSO Cox regression analysis. TMB, MSI, and immune infiltration analyses were performed, and consensus cluster analysis of LRGs was also performed.

Results

These 16 lipid metabolism-related genes (LRGs) were mainly involved in the process of lipid metabolism and fatty acid binding in breast cancer. Prognosis analysis identified the prognostic value of FABP7(Fatty acid binding protein 7) and NDUFAB1(NADH:ubiquinone oxidoreductase subunit AB1) in breast cancer patients. The prognostic gene signature constructed with FABP7 and NDUFAB1 was significantly related to immune cell infiltration and could predict the overall survival rate with above average correctness of breast cancer patients. FABP7 and NDUFAB1 were proven to have relevance in immune cell infiltration and tumour mutation burden (TMB). Consensus cluster analysis identified that the upregulated mRNAs were mostly related to the oncogenesis process, while the downregulated mRNAs were associated with immune-related signalling pathways.

Conclusion

A comprehensive analysis was performed to evaluate the lipid metabolic system and identified a signature constructed by two prognostic genes for immunotherapies in breast cancer. The results also revealed evidence of vulnerabilities in the interplay between the lipid metabolic system and the TIME in breast cancer. Further data with clinical studies and experiments are warranted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-022-01651-9.

Epithelial-Mesenchymal Transition-Based Gene Signature and Distinct Molecular Subtypes for Predicting Clinical Outcomes in Breast Cancer

Purpose

Regulation of inducers and transcription factor families influence epithelial–mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression.

Methods

Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an independent prognostic indicator was assessed. A nomogram was constructed and validated by calibration analysis and decision curve analysis (DCA).

Results

Five molecular subtypes were characterized based on EMT genes. Patients in Cluster 2 exhibited an activated immune state and a better prognosis. An 11-EMT gene-signature was built to predict breast cancer prognosis. After validation, the signature showed independence and robustness in predicting clinical outcomes of patients. A nomogram combining the RiskScore and pTNM_stage accurately predicted 1-, 2-, 3-, and 5-year survival chance. In comparison with published model, the current model showed a higher area under the curve (AUC).

Conclusion

We characterized five breast cancer subtypes with distinct clinical outcomes and immune status. The study developed an 11-EMT gene-signature as an independent prognostic factor for predicting clinical outcomes of breast cancer.

NLRP6-Dependent Pyroptosis-Related lncRNAs Predict the Prognosis of Hepatocellular Carcinoma

Pyroptosis, a novel pro-inflammatory type of programmed cell death, is involved in the tumorigenesis of various cancers. Recent findings have implicated long non-coding RNAs (lncRNAs) in the serial steps of cancer development. However, the expression and prognostic signatures of pyroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remain largely unknown. Therefore, a pyroptosis-related lncRNA prognostic model was constructed for HCC. Thirty-four pyroptosis-related genes were obtained from previous reviews, and gene expression data were collected from The Cancer Genome Atlas (TCGA) database. Spearman's correlation test was used to identify potential pyroptosis-related lncRNAs. Cox and LASSO regression analyses were used to construct a prognostic model. Subsequently, receiver operating characteristic (ROC) curves were constructed to assess the model's predictive ability for the overall survival (OS) of HCC patients. CytoHubba was used to screen out the potential hub gene, whose expression was verified using clinical samples from HCC patients. Finally, nine pyroptosis-related differentially expressed lncRNAs in HCC were identified, and a prognostic model with four pyroptosis-related lncRNAs was constructed with an area under the ROC curve (AUC) of approximately 0.734. Single-sample gene set enrichment analysis and TCGA revealed different immune infiltration and immune checkpoints between the two risk groups. Moreover, these lncRNAs are closely related to the pyroptosis-related gene, NLRP6, which may be considered a hub gene. NLRP6 was lower-expressed in HCC samples, and patients with lower expression of NLRP6 had the longer OS. In conclusion, NLRP6-dependent pyroptosis-related lncRNAs play important roles in tumor immunity and may be potential predictors and therapeutic targets for HCC.

Subcellular progression of mesenchymal transition identified by two discrete synchronous cell lines derived from the same glioblastoma

Glioblastomas (GBM) exhibit intratumoral heterogeneity of various oncogenic evolutional processes. We have successfully isolated and established two distinct cancer cell lines with different morphological and biological characteristics that were derived from the same tissue sample of a GBM. When we compared their genomic and transcriptomic characteristics, each cell line harbored distinct mutation clusters while sharing core driver mutations. Transcriptomic analysis revealed that one cell line was undergoing a mesenchymal transition process, unlike the other cell line. Furthermore, we could identify four tumor samples containing our cell line-like clusters from the publicly available single-cell RNA-seq data, and in a set of paired longitudinal GBM samples, we could confirm three pairs where the recurrent sample was enriched in the genes specific to our cell line undergoing mesenchymal transition. The present study provides direct evidence and a valuable source for investigating the ongoing process of subcellular mesenchymal transition in GBM, which has prognostic and therapeutic implications.

Pyroptosis-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with UCEC

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease globally, and there is no unified prognostic signature at present. In our study, two clusters were identified. Cluster 1 showed better prognosis and higher infiltration level, such as tumor microenvironment (TME), tumor mutation burden (TMB), and immune checkpoint genes expression. Gene set enrichment analysis (GSEA) indicated that some tumor-related pathways and immune-associated pathways were exposed. What is more, six pyroptosis-related long noncoding RNAs (lncRNAs) (PRLs) were applied to establish a prognostic signature through multiple Cox regression analysis. In both training and testing sets, patients with higher risk score had poorer survival than patients with low risk. The area under the curve (AUC) of receiver operating characteristic (ROC) curves performed that the survival probability was better in people with lower risk score. Mechanism analysis revealed that high risk score was correlated with reduced immune infiltration and T cells exhaustion, matching the definition of an "immune-desert" phenotype. Patients with lower risk score were characterized by higher immune checkpoint gene expression and TMB and have a sensitive response to immunotherapy and chemotherapy compared with patients with high risk score. The signature has accurate prediction ability of UCEC and is a promising therapeutic target to improve the effect of immunotherapy.

Tumor Heterogeneity and Molecular Characteristics of Glioblastoma Revealed by Single-Cell RNA-Seq Data Analysis

Glioblastoma multiforme (GBM) is the most common infiltrating lethal tumor of the brain. Tumor heterogeneity and the precise characterization of GBM remain challenging, and the disease-specific and effective biomarkers are not available at present. To understand GBM heterogeneity and the disease prognosis mechanism, we carried out a single-cell transcriptome data analysis of 3389 cells from four primary IDH-WT (isocitrate dehydrogenase wild type) glioblastoma patients and compared the characteristic features of the tumor and periphery cells. We observed that the marker gene expression profiles of different cell types and the copy number variations (CNVs) are heterogeneous in the GBM samples. Further, we have identified 94 differentially expressed genes (DEGs) between tumor and periphery cells. We constructed a tissue-specific co-expression network and protein-protein interaction network for the DEGs and identified several hub genes, including CX3CR1, GAPDH, FN1, PDGFRA, HTRA1, ANXA2 THBS1, GFAP, PTN, TNC, and VIM. The DEGs were significantly enriched with proliferation and migration pathways related to glioblastoma. Additionally, we were able to identify the differentiation state of microglia and changes in the transcriptome in the presence of glioblastoma that might support tumor growth. This study provides insights into GBM heterogeneity and suggests novel potential disease-specific biomarkers which could help to identify the therapeutic targets in GBM.

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Glucose metabolic reprogramming and immune imbalance play important roles in the progression of cancers. The purpose of this study is to develop a glycolysis-related prognostic signature for endometrial cancer (EC) and analyze its relationship with immune function. The mRNA expression profiling of the glycolysis-related genes and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA). We identified a glycolysis-related gene prognostic signature for predicting the prognosis of EC by using The Least Absolute Shrinkage and Selection Operator (LASSO) regression and found the patients in the high-risk group had worse survival prognosis. Multivariate Cox regression analysis showed that the gene signature was an independent prognostic factor for EC. The ROC curve confirmed the accuracy of the prognostic signature (AUC = 0.730). Then, we constructed a nomogram to predict the 1–5 years survival rate of EC patients. The association between the gene signature and immune function was analyzed based on the “ESTIMATE” and “CIBERSORT” algorithm, which showed the immune and ESTIMATE scores of patients in the high-risk group were lower, while the low immune and ESTIMATE scores were associated with a worse prognosis of patients. The imbalance of immune cells was also found in the high-risk group. Further, the protein of CDK1, a gene in the signature, was found to be closely related to prognosis of EC and inhibition of CDK1 could inhibit migration and promote apoptosis of EC cells. This study reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC.

Identification of a Nomogram with an Autophagy-Related Risk Signature for Survival Prediction in Patients with Glioma

Background

Glioma is a common type of tumor in the central nervous system characterized by high morbidity and mortality. Autophagy plays vital roles in the development and progression of glioma, and is involved in both normal physiological and various pathophysiological progresses.

Patients and Methods

A total of 531 autophagy-related genes (ARGs) were obtained and 1738 glioma patients were collected from three public databases. We performed least absolute shrinkage and selection operator regression to identify the optimal prognosis-related genes and constructed an autophagy-related risk signature. The performance of the signature was validated by receiver operating characteristic analysis, survival analysis, clinic correlation analysis, and Cox regression. A nomogram model was established by using multivariate Cox regression analysis. Schoenfeld’s global and individual test were used to estimate time-varying covariance for the assumption of the Cox proportional hazard regression analysis. The R programming language was used as the main data analysis and visualizing tool.

Results

An overall survival-related risk signature consisting of 15 ARGs was constructed and significantly stratified glioma patients into high- and low-risk groups (P < 0.0001). The area under the ROC curve of 1-, 3-, 5-year survival was 0.890, 0.923, and 0.889, respectively. Univariate and multivariate Cox analyses indicated that the risk signature was a satisfactory independent prognostic factor. Moreover, a nomogram model integrating risk signature with clinical information for predicting survival rates of patients with glioma was constructed (C-index=0.861±0.024).

Conclusion

This study constructed a novel and reliable ARG-related risk signature, which was verified as a satisfactory prognostic marker. The nomogram model could provide a reference for individually predicting the prognosis for each patient with glioma and promoting the selection of optimal treatment.

Machine Learning Analysis of Immune Cells for Diagnosis and Prognosis of Cutaneous Melanoma

Tumor infiltration, known to associate with various cancer initiations and progressions, is a promising therapeutic target for aggressive cutaneous melanoma. Then, the relative infiltration of 24 kinds of immune cells in melanoma was assessed by a single sample gene set enrichment analysis (ssGSEA) program from a public database. The multiple machine learning algorithms were applied to evaluate the efficiency of immune cells in diagnosing and predicting the prognosis of melanoma. In comparison with the expression of immune cell in tumor and normal control, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC = 0.965, RF AUC = 0.99, SVM AUC = 0.963, LASSO AUC = 0.964, and NNET AUC = 0.989). These diagnostic models were also validated in three outside datasets and suggested over 90% AUC to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker that could estimate the prognosis of melanoma. This biomarker was also further validated in internal and external datasets. Following that, we created a nomogram with a composition of risk score and clinical parameters, which had high accuracies in predicting survival over three and five years. The nomogram's decision curve revealed a bigger net benefit than the tumor stage. Furthermore, a risk score system was used to categorize melanoma patients into high- and low-risk subgroups. The high-risk group has a significantly lower life expectancy than the low-risk subgroup. Finally, we observed that complement, epithelial-mesenchymal transition, and inflammatory response were significantly activated in the high-risk group. Therefore, the findings provide new insights for understanding the tumor infiltration relevant to clinical applications as a diagnostic or prognostic biomarker for melanoma.

Pan-Cancer Analysis Shows Enrichment of Macrophages, Overexpression of Checkpoint Molecules, Inhibitory Cytokines, and Immune Exhaustion Signatures in EMT-High Tumors

Epithelial-mesenchymal transition (EMT) is a highly dynamic process that occurs under normal circumstances; however, EMT is also known to play a central role in tumor progression and metastasis. Furthermore, role of tumor immune microenvironment (TIME) in shaping anticancer immunity and inducing the EMT is also well recognized. Understanding the key features of EMT is critical for the development of effective therapeutic interventions. Given the central role of EMT in immune escape and cancer progression and treatment, we have carried out a pan-cancer TIME analysis of The Cancer Genome Atlas (TCGA) dataset in context to EMT. We have analyzed infiltration of various immune cells, expression of multiple checkpoint molecules and cytokines, and inflammatory and immune exhaustion gene signatures in 22 cancer types from TCGA dataset. A total of 16 cancer types showed a significantly increased (p < 0.001) infiltration of macrophages in EMT-high tumors (mesenchymal samples). Furthermore, out of the 17 checkpoint molecules we analyzed, 11 showed a significant overexpression (p < 0.001) in EMT-high samples of at least 10 cancer types. Analysis of cytokines showed significant enrichment of immunosuppressive cytokines-TGFB1 and IL10-in the EMT-high group of almost all cancer types. Analysis of various gene signatures showed enrichment of inflammation, exhausted CD8+ T cells, and activated stroma signatures in EMT-high tumors. In summary, our pan-cancer EMT analysis of TCGA dataset shows that the TIME of EMT-high tumors is highly immunosuppressive compared to the EMT-low (epithelial) tumors. The distinctive features of EMT-high tumors are as follows: (i) the enrichment of tumor-associated macrophages, (ii) overexpression of immune checkpoint molecules, (iii) upregulation of immune inhibitory cytokines TGFB1 and IL10, and (iv) enrichment of inflammatory and exhausted CD8+ T-cell signatures. Our study shows that TIMEs of different EMT groups differ significantly, and this would pave the way for future studies analyzing and targeting the TIME regulators for anticancer immunotherapy.

The ALDH Family Contributes to Immunocyte Infiltration, Proliferation and Epithelial-Mesenchymal Transformation in Glioma

Gliomas are malignant tumors that originate from the central nervous system. The aldehyde dehydrogenase family has been documented to affect cancer progression; however, its role in gliomas remains largely unexplored. Bulk RNA-seq analysis and single-cell RNA-Seq analysis were performed to explore the role of the aldehyde dehydrogenases family in gliomas. Training cohort contained The Cancer Genome Atlas data, while data from Chinese Glioma Genome Atlas and Gene Expression Omnibus were set as validation cohorts. Our scoring system based on the aldehyde dehydrogenases family suggested that high-scoring samples were associated with worse survival outcomes. The enrichment score of pathways were calculated by AUCell to substantiate the biofunction prediction results that the aldehyde dehydrogenases family affected glioma progression by modulating tumor cell proliferation, migration, and immune landscape. Tumor immune landscape was mapped from high-scoring samples. Moreover, ALDH3B1 and ALDH16A1, two main contributors of the scoring system, could affect glioblastoma cell proliferation and migration by inducing cell-cycle arrest and the epithelial-mesenchymal transition. Taken together, the aldehyde dehydrogenases family could play a significant role in the tumor immune landscape and could be used to predict patient prognosis. ALDH3B1 and ALDH16A1 could influence tumor cell proliferation and migration.

CD70 as an actionable immunotherapeutic target in recurrent glioblastoma and its microenvironment

PURPOSE

Glioblastoma (GBM) patients suffer from a dismal prognosis, with standard of care therapy inevitably leading to therapy-resistant recurrent tumors. The presence of cancer stem cells (CSCs) drives the extensive heterogeneity seen in GBM, prompting the need for novel therapies specifically targeting this subset of tumor-driving cells. Here, we identify CD70 as a potential therapeutic target for recurrent GBM CSCs.

EXPERIMENTAL DESIGN

In the current study, we identified the relevance and functional influence of CD70 on primary and recurrent GBM cells, and further define its function using established stem cell assays. We use CD70 knockdown studies, subsequent RNAseq pathway analysis, and in vivo xenotransplantation to validate CD70's role in GBM. Next, we developed and tested an anti-CD70 chimeric antigen receptor (CAR)-T therapy, which we validated in vitro and in vivo using our established preclinical model of human GBM. Lastly, we explored the importance of CD70 in the tumor immune microenvironment (TIME) by assessing the presence of its receptor, CD27, in immune infiltrates derived from freshly resected GBM tumor samples.

RESULTS

CD70 expression is elevated in recurrent GBM and CD70 knockdown reduces tumorigenicity in vitro and in vivo. CD70 CAR-T therapy significantly improves prognosis in vivo. We also found CD27 to be present on the cell surface of multiple relevant GBM TIME cell populations, notably putative M1 macrophages and CD4 T cells.

CONCLUSION

CD70 plays a key role in recurrent GBM cell aggressiveness and maintenance. Immunotherapeutic targeting of CD70 significantly improves survival in animal models and the CD70/CD27 axis may be a viable polytherapeutic avenue to co-target both GBM and its TIME.

HMGA1 stimulates MYH9-dependent ubiquitination of GSK-3β via PI3K/Akt/c-Jun signaling to promote malignant progression and chemoresistance in gliomas

Myosin heavy chain 9 (MYH9) plays an essential role in human diseases, including multiple cancers; however, little is known about its role in gliomas. In the present study, we revealed that HMGA1 and MYH9 were upregulated in gliomas and their expression correlated with WHO grade, and HMGA1 promoted the acquisition of malignant phenotypes and chemoresistance of glioma cells by regulating the expression of MYH9 through c-Jun-mediated transcription. Moreover, MYH9 interacted with GSK-3β to inhibit the expression of GSK-3β protein by promoting its ubiquitination; the downregulation of GSK-3β subsequently promoted the nuclear translocation of β-catenin, enhancing growth, invasion, migration, and temozolomide resistance in glioma cells. Expression levels of HMGA1 and MYH9 were significantly correlated with patient survival and should be considered as independent prognostic factors. Our findings provide new insights into the role of HMGA1 and MYH9 in gliomagenesis and suggest the potential application of HMGA1 and MYH9 in cancer therapy in the future.

Development and Validation of an E2F-Related Gene Signature to Predict Prognosis of Patients With Lung Squamous Cell Carcinoma

Background

Lung squamous cell carcinoma (LUSC) generally correlates with poor clinical prognoses due to the lack of available prognostic biomarkers. This study is designed to identify a potential biomarker significant for the prognosis and treatment of LUSC, so as to provide a scientific basis for clinical treatment decisions.

Methods

Genomic changes in LUSC samples before and after radiation were firstly discussed to identify E2 factor (E2F) pathway of prognostic significance. A series of bioinformatics analyses and statistical methods were combined to construct a robust E2F-related prognostic gene signature. Furthermore, a decision tree and a nomogram were established according to the gene signature and multiple clinicopathological characteristics to improve risk stratification and quantify risk assessment for individual patients.

Results

In our investigated cohorts, the E2F-related gene signature we identified was capable of predicting clinical outcomes and therapeutic responses in LUSC patients, besides, discriminative to identify high-risk patients. Survival analysis suggested that the gene signature was independently prognostic for adverse overall survival of LUSC patients. The decision tree identified the strong discriminative performance of the gene signature in risk stractification for overall survival while the nomogram demonstrated a high accuracy.

Conclusion

The E2F-related gene signature may help distinguish high-risk patients so as to formulate personalized treatment strategy in LUSC patients.

Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis

Objective

Epithelial-mesenchymal transition (EMT) exerts a key function in cancer initiation and progression. Herein, we aimed to develop an EMT-based prognostic signature in gastric cancer.

Methods

The gene expression profiles of gastric cancer were obtained from TCGA dataset as a training set and GSE66229 and GSE84437 datasets as validation sets. By LASSO regression and Cox regression analyses, key prognostic EMT-related genes were screened for developing a risk score (RS) model. Potential small molecular compounds were predicted by the CMap database based on the RS model. GSEA was employed to explore signaling pathways associated with the RS. ESTIMATE and seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC) were applied to assess the RS and immune microenvironment.

Results

This study developed an EMT-related gene signature comprised of SERPINE1, PCOLCE2, MATN3, and DKK1. High-RS patients displayed poorer survival outcomes than those with low RS. ROC curves demonstrated the robustness of the model in predicting the prognosis. After external validation, the RS model was an independent risk factor for gastric cancer. Several compounds were predicted for gastric cancer treatment based on the RS model. ECM receptor interaction, focal adhesion, pathway in cancer, TGF-beta, and WNT pathways were distinctly activated in high-RS samples. Also, high RS was significantly associated with increased stromal and immune scores and increased infiltration of CD4+ T cell, CD8+ T cell, cancer-associated fibroblast, and macrophage in gastric cancer tissues.

Conclusion

Our findings suggested that the EMT-related gene model may robustly predict gastric cancer prognosis, which could improve the efficacy of personalized therapy.

HIF-1-regulated expression of calreticulin promotes breast tumorigenesis and progression through Wnt/β-catenin pathway activation

Calreticulin (CALR) is a multifunctional protein that participates in various cellular processes, which include calcium homeostasis, cell adhesion, protein folding, and cancer progression. However, the role of CALR in breast cancer (BC) is unclear. Here, we report that CALR is overexpressed in BC compared with normal tissue, and its expression is correlated with patient mortality and stemness indices. CALR expression was increased in mammosphere cultures, CD24^-CD44⁺ cells, and aldehyde dehydrogenase-expressing cells, which are enriched for breast cancer stem cells (BCSCs). Additionally, CALR knockdown led to BCSC depletion, which impaired tumor initiation and metastasis and enhanced chemosensitivity in vivo. Chromatin immunoprecipitation and reporter assays revealed that hypoxia-inducible factor 1 (HIF-1) directly activated CALR transcription in hypoxic BC cells. CALR expression was correlated with Wnt/β-catenin pathway activation, and an activator of Wnt/β-catenin signaling abrogated the inhibitory effect of CALR knockdown on mammosphere formation. Taken together, our results demonstrate that CALR facilitates BC progression by promoting the BCSC phenotype through Wnt/β-catenin signaling in an HIF-1-dependent manner and suggest that CALR may represent a target for BC therapy.

Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO

Pancreatic cancer is a highly malignant and metastatic tumor of the digestive system. Even after surgical removal of the tumor, most patients are still at risk of metastasis. Therefore, screening for metastatic biomarkers can identify precise therapeutic intervention targets. In this study, we analyzed 96 pancreatic cancer samples from The Cancer Genome Atlas (TCGA) without metastasis or with metastasis after R0 resection. We also retrieved data from metastatic pancreatic cancer cell lines from Gene Expression Omnibus (GEO), as well as collected sequencing data from our own cell lines, BxPC-3 and BxPC-3-M8. Finally, we analyzed the expression of metastasis-related genes in different datasets by the Limma and edgeR packages in R software, and enrichment analysis of differential gene expression was used to gain insight into the mechanism of pancreatic cancer metastasis. Our analysis identified six genes as risk factors for predicting metastatic status by LASSO regression, including zinc finger BED-Type Containing 2 (ZBED2), S100 calcium-binding protein A2 (S100A2), Jagged canonical Notch ligand 1 (JAG1), laminin subunit gamma 2 (LAMC2), transglutaminase 2 (TGM2), and the transcription factor hepatic leukemia factor (HLF). We used these six EMT-related genes to construct a risk-scoring model. The receiver operating characteristic (ROC) curve showed that the risk score could better predict the risk of metastasis. Univariate and multivariate Cox regression analyses revealed that the risk score was also an important predictor of pancreatic cancer. In conclusion, 6-mRNA expression is a potentially valuable method for predicting pancreatic cancer metastasis, assessing clinical outcomes, and facilitating future personalized treatment for patients with ductal adenocarcinoma of the pancreas (PDAC).

Long Non-Coding RNA SNHG6 Supports Glioma Progression Through Upregulation of Notch1, Sox2, and EMT

Gliomas, particularly the advanced grade glioblastomas, have poor 5-year survival rates and worse outcomes. lncRNAs and EMT have been extensively studied in gliomas but the disease progression remains poorly understood. SNHG6 has been shown to affect glioma cell proliferation but its effect on EMT of glioma cells along with its effect on disease progression is not known. We screened four glioma cell lines; H4, A172, U87MG, and SW088 and grouped them based on high vs. low SNHG6 expression. Transfections with SNHG6 specific siRNA resulted in induction of apoptosis of high SNHG6 expressing A172 and U87MG cells. This was accompanied by inhibition of EMT and downregulation of EMT-modulating factor Notch1, β-catenin activity and the cancer stem cell marker Sox2. The regulation was not found to be reciprocal as silencing of Notch1 and Sox2 failed to affect SNHG6 levels. The levels of SNHG6 and Notch1 were also found elevated in Grade IV glioma patients (n = 4) relative to Grade II glioma patients (n = 5). These results identify SNHG6 and Notch1 as valid targets for glioma therapy.

Caveolin-1, a Key Mediator Across Multiple Pathways in Glioblastoma and an Independent Negative Biomarker of Patient Survival

Glioblastoma (GB) remains an aggressive malignancy with an extremely poor prognosis. Discovering new candidate drug targets for GB remains an unmet medical need. Caveolin-1 (Cav-1) has been shown to act variously as both a tumour suppressor and tumour promoter in many cancers. The implications of Cav-1 expression in GB remains poorly understood. Using clinical and genomic databases we examined the relationship between tumour Cav-1 gene expression (including its spatial distribution) and clinical pathological parameters of the GB tumour and survival probability in a TCGA cohort (n=155) and CGGA cohort (n=220) of GB patients. High expression of Cav-1 represented a significant independent predictor of shortened survival (HR = 2.985, 5.1 vs 14.9 months) with a greater statistically significant impact in female patients and in the Proneural and Mesenchymal GB subtypes. High Cav-1 expression correlated with other factors associated with poor prognosis: IDH w/t status, high histological tumour grade and low KPS score. A total of 4879 differentially expressed genes (DEGs) in the GB tumour were found to correlate with Cav-1 expression (either positively or negatively). Pathway enrichment analysis highlighted an over-representation of these DEGs to certain biological pathways. Focusing on those that lie within a framework of epithelial to mesenchymal transition and tumour cell migration and invasion we identified 27 of these DEGs. We then examined the prognostic value of Cav-1 when used in combination with any of these 27 genes and identified a subset of combinations (with Cav-1) indicative of co-operative synergistic mechanisms of action. Overall, the work has confirmed Cav-1 can serve as an independent prognostic marker in GB, but also augment prognosis when used in combination with a panel of biomarkers or clinicopathologic parameters. Moreover, Cav-1 appears to be linked to many signalling entities within the GB tumour and as such this work begins to substantiate Cav-1 or its associated signalling partners as candidate target for GB new drug discovery.

Bioinformatic Analyses Identify a Prognostic Autophagy-Related Long Non-coding RNA Signature Associated With Immune Microenvironment in Diffuse Gliomas

Background

Autophagy and long non-coding RNA (lncRNA) play a critical role in tumor progression and microenvironment. However, the role of autophagy-related lncRNAs (ARLs) in glioma microenvironment remains unclear.

Methods

A total of 988 diffuse glioma samples were extracted from TCGA and CGGA databases. Consensus clustering was applied to reveal different subgroups of diffuse gliomas. Kaplan-Meier analysis was used to evaluate survival differences between groups. The infiltration of immune cells was estimated by ssGSEA, TIMER, and CIBERSORT algorithms. The construction of ARL signature was conducted using principal component analysis.

Results

Consensus clustering revealed two clusters of diffuse gliomas, in which cluster 1 was associated with poor prognosis and enriched with malignant subtypes of gliomas. Moreover, cluster 1 exhibited high apoptotic and immune characteristics, and it had a low purity and high infiltration of several immune cells. The constructed ARL signature showed a promising accuracy in predicting the prognosis of glioma patients. ARL score was significantly elevated in the malignant subtype of glioma and the high ARL score indicated a poor prognosis. Besides, the high ARL score notably indicated low tumor purity and high infiltration of macrophages and neutrophils.

Conclusion

Our study developed and validated a novel ARL signature for the classification of diffuse glioma, which was closely associated with glioma immune microenvironment and could serve as a promising prognostic biomarker for glioma patients.

The Acidic Brain-Glycolytic Switch in the Microenvironment of Malignant Glioma

Malignant glioma represents a fatal disease with a poor prognosis and development of resistance mechanisms against conventional therapeutic approaches. The distinct tumor zones of this heterogeneous neoplasm develop their own microenvironment, in which subpopulations of cancer cells communicate. Adaptation to hypoxia in the center of the expanding tumor mass leads to the glycolytic and angiogenic switch, accompanied by upregulation of different glycolytic enzymes, transporters, and other metabolites. These processes render the tumor microenvironment more acidic, remodel the extracellular matrix, and create energy gradients for the metabolic communication between different cancer cells in distinct tumor zones. Escape mechanisms from hypoxia-induced cell death and energy deprivation are the result. The functional consequences are more aggressive and malignant behavior with enhanced proliferation and survival, migration and invasiveness, and the induction of angiogenesis. In this review, we go from the biochemical principles of aerobic and anaerobic glycolysis over the glycolytic switch, regulated by the key transcription factor hypoxia-inducible factor (HIF)-1α, to other important metabolic players like the monocarboxylate transporters (MCTs)1 and 4. We discuss the metabolic symbiosis model via lactate shuttling in the acidic tumor microenvironment and highlight the functional consequences of the glycolytic switch on glioma malignancy. Furthermore, we illustrate regulation by micro ribonucleic acids (miRNAs) and the connection between isocitrate dehydrogenase (IDH) mutation status and glycolytic metabolism. Finally, we give an outlook about the diagnostic and therapeutic implications of the glycolytic switch and the relation to tumor immunity in malignant glioma.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

MCT4 Promotes Tumor Malignancy in F98 Glioma Cells

Monocarboxylate transporter 4 (MCT4, SLC16A3) is elevated under hypoxic conditions in many malignant tumors including gliomas. Moreover, MCT4 expression is associated with shorter overall survival. However, the functional consequences of MCT4 expression on the distinct hallmarks of cancer have not yet been explored at the cellular level. Here, we investigated the impact of MCT4 overexpression on proliferation, survival, cell death, migration, invasion, and angiogenesis in F98 glioma cells. Stable F98 glioma cell lines with MCT4 overexpression, normal expression, and knockdown were generated. Distinct hallmarks of cancer were examined using in silico analysis, various in vitro cell culture assays, and ex vivo organotypic rat brain slice culture model. Consistent with its function as lactate and proton exporter, MCT4 expression levels correlated inversely with extracellular pH and proportionally with extracellular lactate concentrations. Our results further indicate that MCT4 promotes proliferation and survival by altered cell cycle regulation and cell death mechanisms. Moreover, MCT4 overexpression enhances cell migration and invasiveness via reorganization of the actin cytoskeleton. Finally, MCT4 inhibition mitigates the induction of angiogenesis, suggesting that MCT4 also plays a crucial role in tumor-related angiogenesis. In summary, our data highlight MCT4/SLC16A3 as a key gene for distinct hallmarks of tumor malignancy in glioma cells.

A novel epithelial-mesenchymal transition molecular signature predicts the oncological outcomes in colorectal cancer

Epithelial‐mesenchymal transition (EMT), a biological process involving the transformation of epithelial cells into mesenchymal cells, promotes tumour initiation and metastasis. The aim of this study was to construct an EMT molecular signature for predicting colorectal cancer (CRC) prognosis and evaluate the efficacy of the model. The risk scoring system, constructed by log‐rank test and multivariate Cox regression analysis according to EMT‐related gene expression in CRC patients from TCGA database, demonstrated the highest correlation with prognosis compared with other parameters in CRC patients. The risk scores were significantly correlated with more lymph node metastasis, distal metastasis and advanced clinical stage of CRC. The model was further successfully validated in two independent external cohorts from GEO database. Furthermore, we developed a nomogram to integrate the EMT signature with the pathological stage of CRC, which was found to perform well in predicting the overall survival. Additionally, this risk scoring model was found to be associated with immune cell infiltration, implying a potential role of EMT involved in immunity regulation in tumour microenvironment. Taken together, our novel EMT molecular model may be useful in identifying high‐risk patients who need an intensive follow‐up and more aggressive therapy, finally contributing to more precise individualized therapeutic strategies.

Ultrasound-Targeted Microbubble Destruction-Mediated Downregulation of EZH2 Inhibits Stemness and Epithelial-Mesenchymal Transition of Liver Cancer Stem Cells

Background

Cancer cells could show the characteristics of cancer stem cells (CSCs) through epithelial-mesenchymal transition (EMT). EZH2 was associated with EMT. Ultrasound-targeted microbubble destruction (UTMD) could enhance gene transfection efficiency. Here, we explored the effect of UTMD-mediated shEZH2 on liver CSCs.

Methods

EZH2 expression in liver cancer and the overall survival of liver cancer patients were analyzed by bioinformatics. Liver CSCs (CD133⁺HuH7) were sorted by flow cytometry. After transfection of shEZH2 through UTMD (UTMD-shEZH2) or liposome (LIP-shEZH2), the viability, proliferation, sphere formation, migration, and invasion of CD133⁺HuH7 cells were detected by MTT, colony formation, tumor-sphere formation, wound healing, and transwell assays, respectively. A mice subcutaneous-xenotransplant tumor model was established by injecting CD133⁺HuH7 or CD133⁻HuH7 cells into the limbs of mice. Tumor weight and volume were documented. The expressions of EZH2, EMT-related factors, and STAT3/PI3K/AKT pathway-related factors in CD133⁺HuH7 cells or tumor tissues were detected by RT-qPCR, Western blot, or immunohistochemical.

Results

EZH2 was high-expressed in liver cancer, and the patients with high expression of EZH2 had a poor survival. CD133⁺ HuH7 cells had higher EZH2 expression, higher viability, and stronger sphere-forming and tumor-forming abilities than CD133⁻ HuH7 cells. ShEZH2 inhibited the viability, proliferation, sphere formation, migration, and invasion of CD133⁺ HuH7 cells, decreased the weight and volume of the xenotransplant tumor, inhibited the expressions of EZH2, Vimentin, N-Cadherin, Twist-1, p-STAT3, p-PI3K, and p-AKT, and increased E-Cadherin expression. UTMD-shEZH2 caused a stronger effect on CD133⁺ HuH7 cells than LIP-shEZH2.

Conclusion

UTMD-mediated shEZH2 inhibited the stemness and EMT of liver CSCs in vitro and in vivo through regulating the STAT3/PI3K/AKT pathway.