Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

Identification of a functional CircRNA-miRNA-mRNA network and inhibitory effect of Hsa_circ_0001681 on gliomas

OBJECTIVE

Gliomas pose a significant global health challenge due to high rates of morbidity and mortality. Recent research has indicated that circular RNAs (circRNAs) may play a crucial role in gliomas. However, the specific impacts of circRNAs on gliomas development is poorly understood. Therefore, the present study aimed to explore the roles of circRNAs in gliomas by analyzing their interactions with microRNAs (miRNAs) and messenger RNAs (mRNAs).

METHODS

Datasets were extracted from the Gene Expression Omnibus (GEO) database to investigate differentially expressed circRNAs in gliomas. Using the Circular RNA Interactome, we predicted interactions between the identified circRNAs and 125 target miRNAs, focusing on 15 key miRNAs selected by intersection analysis. The miRNet database was applied to predict 2635 target mRNAs, constructing a comprehensive circRNA-miRNA-mRNA network, while functional enrichment analyses were conducted to determine the roles of this network.

RESULTS

Four circRNAs with significant differential expression in glioma samples were identified. The constructed network indicated the substantial involvement of transcriptional regulation and cancer-related pathways. Notably, hsa_circ_0001681 was highlighted as a key circRNA, which was further validated through Sanger sequencing and quantitative reverse transcription PCR (qRT-PCR). Functional assays, including cellular assays and animal xenograft experiments, demonstrated that hsa_circ_0001681 inhibits glioma carcinogenesis in vitro and in vivo.

CONCLUSION

Our investigation highlights the significant role of the circRNA-miRNA-mRNA network in the pathophysiology of gliomas, and supports the potential of hsa_circ_0001681 as a diagnostic and therapeutic biomarker. These findings present new opportunities for understanding the molecular mechanisms underlying glioma and developing targeted treatments.

Suppression of hepatocellular carcinoma progression by long noncoding RNA apolipoprotein C1 pseudogene via the regulation of the microRNA-106b-PTEN axis

Background

Numerous researches have reported that long noncoding RNAs (lncRNAs) participate in tumor development and progression. LncRNA apolipoprotein C-I pseudogene 1 (APOC1P1), a pseudogene located in 19q13.2 between apolipoprotein C-I and apolipoprotein C-IV, is involved in a variety of diseases. However, the role of lncRNA APOC1P1 in hepatocellular carcinoma (HCC) remains unknown.

Methods

Quantitative polymerase chain reaction (qPCR) was performed to examine the expression of APOC1P1, miR-106b, and PTEN (phosphatase and TENsin homolog deleted on chromosome 10) in HCC tissues, adjacent normal tissues, and specific cell lines (LO2, Bel-7407, HCCLM3, MHCC-97H, Hep G2, and Huh-7). Upregulation of APOC1P1 and downregulation of miR-106b were conducted via application of vector transfection and microRNA (miRNA) inhibitor. Bioinformatics analysis and luciferase reporter assay were used to verify the binding sites of APOC1P1, miR-106b, and PTEN. Cell proliferation and invasion were determined with Cell Counting Kit-8 (CCK-8) and Transwell experiments. Subcellular location analysis was used to determine the distribution of APOC1P1 in cells, and Western blotting was used to detect the expression of PTEN.

Results

It was found that the expressions of APOC1P1 and PTEN were downregulated, while that of miR-106b was upregulated in HCC tissues and cells. Subcellular location analysis showed that APOC1P1 was localized in cytoplasm and competitively bound to miR-106b. APOC1P1 overexpression and miR-106b inhibition suppressed HCC cell proliferation and invasion. qPCR indicated the negative correlation between APOC1P1 expression and miR-106b expression in HCC tissues and a positive correlation between APOC1P1 and PTEN.

Conclusions

Our findings suggested that the lncRNA APOC1P1 inhibits HCC progression by competitively binding to miR-106b, leading to elevated PTEN expression, inhibiting cell proliferation and invasion in HCC cells. These results provide new insights into the diagnosis and therapy of HCC.

Development and validation of a genomic nomogram based on a ceRNA network for comprehensive analysis of obstructive sleep apnea

Objectives: Some ceRNA associated with lncRNA have been considered as possible diagnostic and therapeutic biomarkers for obstructive sleep apnea (OSA). We intend to identify the potential hub genes for the development of OSA, which will provide a foundation for the study of the molecular mechanism underlying OSA and for the diagnosis and treatment of OSA.

Methods: We collected plasma samples from OSA patients and healthy controls for the detection of ceRNA using a chip. Based on the differential expression of lncRNA, we identified the target genes of miRNA that bind to lncRNAs. We then constructed lncRNA-related ceRNA networks, performed functional enrichment analysis and protein-protein interaction analysis, and performed internal and external validation of the expression levels of stable hub genes. Then, we conducted LASSO regression analysis on the stable hub genes, selected relatively significant genes to construct a simple and easy-to-use nomogram, validated the nomogram, and constructed the core ceRNA sub-network of key genes.

Results: We successfully identified 282 DElncRNAs and 380 DEmRNAs through differential analysis, and we constructed an OSA-related ceRNA network consisting of 292 miRNA-lncRNAs and 41 miRNA-mRNAs. Through PPI and hub gene selection, we obtained 7 additional robust hub genes, CCND2, WT1, E2F2, IRF1, BAZ2A, LAMC1, and DAB2. Using LASSO regression analysis, we created a nomogram with four predictors (CCND2, WT1, E2F2, and IRF1), and its area under the curve (AUC) is 1. Finally, we constructed a core ceRNA sub-network composed of 74 miRNA-lncRNA and 7 miRNA-mRNA nodes.

Conclusion: Our study provides a new foundation for elucidating the molecular mechanism of lncRNA in OSA and for diagnosing and treating OSA.

Identification of a hypoxia-related gene prognostic signature in colorectal cancer based on bulk and single-cell RNA-seq

Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 (HK1) and glucose-6-phosphate isomerase (GPI) gene expression in natural killer (NK) and CD8⁺ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature's six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature's six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells.

Relationship between M6A methylation regulator and prognosis in patients with hepatocellular carcinoma after transcatheter arterial chemoembolization

Background

Patients with mid-stage HCC (hepatocellular carcinoma) may benefit from transcatheter arterial chemoembolization (TACE). However, patient efficacy varies widely, and the detailed assessment index is unknown. The most general methylation alteration in mRNA (Messenger RNA), N6-methyladenosine (m6A), is controlled by the m6A regulator, which is associated with the emergence of tumors. To include the molecular causes of cancer, competition with ceRNA (endogenous RNA) networks is crucial. However, the exact processes they contribute to TACE HCC remain uncertain. The purpose of this study was tantamount to investigating the possible function of ceRNA networks and m6A regulators in patients with TACE HCC.

Methods

Genes Associated with m6A were discovered using the TACE GEO (Gene Expression Omnibus) dataset. An additional estimate of M6A-associated DEGs (differentially expressed genes) was used to create a predictive response model, which is required. LncRNA-miRNA and miRNA-mRNA interactions were then predicted, the regulatory ceRNA network was set up using Cytoscape software, and target genes were identified using GEPIA online analysis. The connection between immunological checkpoints, immune cell marker genes, and target genes for immune cells was also examined.

Results

The detection of 4 m6A-associated DEGs, the development and evaluation of 2 Machine learning models, and the development of risk models that accurately predicted the response rate of specific patients. Additionally, we obtained two miRNAs (micro RNAs)and six lncRNAs (Long non-coding RNAs), forming an 8-pair ceRNA network, and the target gene LRPPRC deletion of one copy number and gene expression was highly correlated with the amount of Tregs immune cells. LRPPRC was related positively with NRP1, IRF5, and ITGAM and negatively with CCR7 and CD8B among immune cell marker genes. We also discovered that LRPPRC correlates positively with immune checkpoint CD274 cells.

Conclusion

The response of HCC patients to TACE therapy may be predicted using a model based on four gene expression data. We also developed a ceRNA network for TACE HCC related to m6A, which offered suggestions for more research into its molecular processes and possible prognostic indicators.

Computational Characterizing Necroptosis Reveals Implications for Immune Infiltration and Immunotherapy of Hepatocellular Carcinoma

Necroptosis is a programmed form of necrotic cell death in regulating cancer ontogenesis, progression, and tumor microenvironment (TME) and could drive tumor-infiltrating cells to release pro-inflammatory cytokines, incurring strong immune responses. Nowadays, there are few identified biomarkers applied in clinical immunotherapy, and it is increasingly recognized that high levels of tumor necroptosis could enhance the response to immunotherapy. However, comprehensive characterization of necroptosis associated with TME and immunotherapy in Hepatocellular carcinoma (HCC) remains unexplored. Here, we computationally characterized necroptosis landscape in HCC samples from TCGA and ICGA cohorts and stratified them into two necroptosis clusters (A or B) with significantly different characteristics in clinical prognosis, immune cell function, and TME-landscapes. Additionally, to further evaluate the necroptosis levels of each sample, we established a novel necroptosis-related gene score (NRGscore). We further investigated the TME, tumor mutational burden (TMB), clinical response to immunotherapy, and chemotherapeutic drug sensitivity of HCC subgroups stratified by the necroptosis landscapes. The NRGscore is robust and highly predictive of HCC clinical outcomes. Further analysis indicated that the high NRGscore group resembles the immune-inflamed phenotype while the low score group is analogous to the immune-exclusion or metabolism phenotype. Additionally, the high NRGscore group is more sensitive to immune checkpoint blockade-based immunotherapy, which was further validated using an external HCC cohort, metastatic melanoma cohort, and advanced urothelial cancer cohort. Besides, the NRGscore was demonstrated as a potential biomarker for chemotherapy, wherein the high NRGscore patients with more tumor stem cell composition could be more sensitive to Cisplatin, Doxorubicin, Paclitaxel-based chemotherapy, and Sorafenib therapy. Collectively, a comprehensive characterization of the necroptosis in HCC suggested its implications for predicting immune infiltration and response to immunotherapy of HCC, providing promising strategies for treatment.

Identification of a Ferroptosis-Related Long Noncoding RNA Prognostic Signature and Its Predictive Ability to Immunotherapy in Hepatocellular Carcinoma

Background: Immune checkpoint blockers (ICBs) are increasingly being used to treat patients with advanced hepatocellular carcinoma (HCC), but only a third of these patients are sensitive to ICBs. Emerging evidence suggests that ferroptosis could be a novel target for antitumor treatment, and combined treatment with ferroptosis inducers might enhance sensitivity to immunotherapy. However, there is a lack of information on the crosstalk between ferroptosis-related lncRNAs and anti-tumor immunity. Therefore, we aim to explore prognostic value of ferroptosis-related lncRNAs and clarify potential role in ICBs of HCC.

Methods: We obtained mRNA and lncRNA expression data from two independent cohorts (TCGA and GEO database). Univariate Cox, the least absolute shrinkage and selection operator (Lasso) algorithm and multivariate Cox analysis were used to construct a lncRNA signature, which was evaluated using the area under the receiver operating characteristic curve (AUC) and Kaplan–Meier curves. Tumor-infiltrating cell (TIC) profiling and the tumor immune dysfunction and exclusion (TIDE) algorithm were used to validate the signature model and immunotherapy. Finally, we adopted RT-PCR assay to evaluate the differential expression of lncRNAs in HCC tissues in our hospital.

Results: The ferroptosis-related lncRNA signature included five lncRNAs, most of which were positively correlated with clinical stage and grade. The signature could stratify patients into two risk groups, with the high-risk group associated with a shorter overall survival (OS, p < 0.05) in TCGA-LIHC and GSE76427. Besides, the AUCs of the 1-, 3-, and 5-years OS were 0.772, 0.707, and 0.666, respectively. Gene set enrichment analysis (GESA) of lncRNAs revealed enrichment of oncogenic and immune-related pathways. The TIC profiling indicated a close correlation between the signature and immune cells. Furthermore, the high-risk group had a better response to immunotherapy than low-risk group. RT-PCR demonstrated these five lncRNAs were upregulated in cancerous tissue than normal tissues.

Conclusions: The ferroptosis-related lncRNA signature could accurately predict the OS of HCC patients and may serve as an independent clinical factor for patients’ outcomes. Ferroptosis-related lncRNAs may remodel the tumor microenvironment (TME) and affect the anti-cancer ability of ICBs, and therefore, could potentially act as an indicator for the response to immunotherapy in HCC.

Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

Background

Accumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients.

Methods

Clinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints.

Results

A prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067-1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p < 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints.

Conclusion

The prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.