The Construction and Analysis of ceRNA Network and Patterns of Immune Infiltration in Colon Adenocarcinoma Metastasis

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

A novel disulfidptosis-related gene signature predicts overall survival of glioblastoma patients

Aim: The aim of this study was to investigate the prognostic relevance of disulfidptosis-related genes in glioblastoma using bioinformatic analysis in The Cancer Genome Atlas Program-Glioblastoma (TCGA-GBM) database and develop a gene signature model for predicting patient prognosis. Methods: We conducted a bioinformatic analysis using the TCGA-GBM database and employed weighted co-expression network analysis to identify disulfidptosis-related genes. Subsequently, we developed a predictive gene signature model based on these genes to stratify glioblastoma patients into high and low-risk groups. Results: Patients categorized into the high-risk group based on the disulfidptosis-related gene signature exhibited a significantly reduced survival rate in comparison to those in the low-risk group. Functional analysis also revealed notable differences in the immune status between the two risk groups. Conclusion: In conclusion, a new disulfidptosis-related gene signature can be utilised to predict prognosis in GBM.

Identification of a prognosis-related gene signature and ceRNA regulatory networks in lung adenocarcinoma

The ceRNA network, consisting of both noncoding RNA and protein-coding RNA, governs the occurrence, progression, metastasis, and infiltration of lung adenocarcinoma. Signatures comprising multiple genes can effectively determine survival stratification and prognosis of patients with lung adenocarcinoma. To explore the mechanisms of lung adenocarcinoma progression and identify potential biological targets, we carried out systematic bioinformatics analyses of the genetic profiles of lung adenocarcinoma, such as weighted gene co-expression network analysis (WGCNA), differential expression (DE) assessment, univariate and multivariate Cox proportional hazard regression models, ceRNA modulatory networks generated using the ENCORI and miRcode databases, nomogram models, ROC curve assessment, and Kaplan-Meier survival curve analysis. The ceRNA network encompassed 37 nodes, comprising 12 mRNAs, 22 lncRNAs, and three miRNAs. Simultaneously, we performed integration analysis using the 12 genes from the ceRNA network. Our findings revealed that the signature established by these 12 genes serves as an adverse element in lung adenocarcinoma, contributing to unfavorable patient prognosis. To ensure the credibility of our results, we used in vitro experiments for further verification. In conclusion, our study delved into the potential mechanisms underlying lung adenocarcinoma via the ceRNA regulatory network, specifically focusing on the PIF1 and has-miR-125a-5p axis. Additionally, a signature comprising 12 genes was identified as a biomarker related to the prognosis of lung adenocarcinoma.

Integrated bioinformatics and validation reveal SOX12 as potential biomarker in colon adenocarcinoma based on an immune infiltration-related ceRNA network

PURPOSE

The primary objective of this study was to construct competing endogenous RNA (ceRNA) networks and evaluate the prognostic significance of tumor-infiltrating immune cells (TIICs) and key biomarkers within the ceRNA networks in colon adenocarcinoma (COAD) patients.

METHODS

Comprehensive bioinformatics tools were used to screen differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs) related to COAD, leading to the creation of ceRNA networks. The CIBERSORT technique was employed to assess the significance of TIICs in COAD, and an immune-related prognosis prediction model was subsequently developed. Co-expression analyses were conducted to determine the relationship between key genes in ceRNA networks and immunologically significant TIICs. The study also utilized 5 GEO datasets and web-based databases to externally validate the findings.

RESULTS

The study revealed a statistically significant relationship between key hub genes and immune cells, as determined through co-expression analysis. Two hub regulators (SOX12 and H19) demonstrated significant prognostic value in the ceRNA-related prognostic model, and their elevated expression levels were verified across multiple CRC cell lines. Additionally, the knockdown of SOX12 led to a suppression of proliferation, migration, and invasion in colon cancer cells.

CONCLUSION

Through the construction of ceRNA networks and evaluation of TIICs, the study successfully established two risk score models and nomograms. These models serve as valuable tools for understanding the molecular processes and predicting the prognosis of COAD patients. Further validation of hub regulators SOX12 and H19 substantiates their potential role as key biomarkers in COAD.

The diagnostic potential of a circRNA-miRNA network in non-small cell lung cancer

Increasing studies demonstrate the significant contributions of circRNA-related competitive endogenous RNA (ceRNA) regulatory networks to tumorigenesis and cancer progression. Here, we aimed to construct a non-small cell lung cancer (NSCLC)-specific circRNA-miRNA network and evaluate its diagnostic potential in NSCLC. MiRNA deep sequencing was performed to screen differentially-expressed serum miRNAs in NSCLC. Four bioinformatics databases (TargetScan, miRanda, starBase, and RNAhybrid) were used to analyze the integrated circRNA-miRNA interaction network. The circRNA-miRNA network, including hsa-miR-4482-3p, hsa-miR-146a-3p, hsa_circ_0008167 and hsa_circ_0003317 was constructed based on their interactions and preliminary testing in NSCLC cells. The relative levels of the selected non-coding RNAs (ncRNAs) were quantified using quantitative real-time polymerase chain reaction (qRT-PCR) in the healthy, pneumonia, benign lung tumor and NSCLC cohorts. The diagnostic power of the circRNA-miRNA network was evaluated using receiver operating characteristic (ROC) analyses. The serum levels of hsa-miR-4482-3p, hsa-miR-146a-3p, hsa_circ_0008167, and hsa_circ_0003317 were dysregulated in NSCLC. The combination of the four ncRNAs showed the highest diagnostic value to discriminate between benign lung tumors and NSCLC. Additionally, the upregulated levels of hsa_circ_0008167 were correlated to more aggressive features of NSCLC, such as lymph node metastasis, distant metastasis, and higher stage. Furthermore, the combination of hsa_circ_0008167 + hsa-miR-4482-3p, and hsa_circ_0008167 + hsa-miR-4482-3p + hsa-miR-146a-3p had the greatest diagnostic power to differentiate between lymph node +/- metastases and higher/lower stages, respectively, compared to circRNAs or miRNAs alone, and traditional tumor markers. In conclusion, we identified a specific circRNA-miRNA network with higher sensitivity and specificity to diagnose NSCLC, thereby providing a new strategy for further development of ceRNA-related tumor markers in other cancers. KEY MESSAGES: Serum miR-4482-3p, miR-146a-3p, circ_0008167 and circ_0003317 are dysregulated in NSCLC. Higher levels of serum circ_0008167 are associated with more malignant NSCLC. Multiple combinations of circRNAs and miRNAs show higher value to diagnose NSCLC.

Epigenetic Regulation of miR-25 and Lnc107153 on Expression of Seasonal Estrus Key Gene CHGA in Sheep

Pituitary pars tuberalis (PT) plays an important role as the transmission center in the seasonal reproduction of animals. It helps convert external photoperiod signals into intrinsic seasonal reproduction signals. In sheep PT, specific expression patterns of several genes (including short photoperiod-induced gene CHGA and long photoperiod genes EYA3 and TSHβ) under different photoperiods are crucial characteristics during this signal transduction. Recent studies have revealed the role of epigenetics in regulating the expression of seasonal reproductive key genes. Therefore, we explored whether microRNAs and LncRNAs regulated the expressions of the above key genes. Firstly, the expression of miR-25 and CHGA showed a significant negative correlation in sheep PT. Results of the dual luciferase reporter assay and miR-25 overexpression indicated that miR-25 could inhibit the expression of CHGA by specifically binding to its 3'UTR region in pituitary cells. Then, expression negative correlation and dual luciferase reporter analyses were used to screen and identify the candidate LncRNA (Lnc107153) targeted by miR-25. Finally, the results of fluorescence in situ hybridization and Lnc107153 overexpression suggested that Lnc107153 and miR-25 were involved in the epigenetic regulation of CHGA expression. However, the expressions of EYA3 and TSHβ were not regulated by miRNAs. These results will provide new insights into the epigenetic regulatory network of key genes in sheep seasonal reproduction.

LncRNA and its role in gastric cancer immunotherapy

Gastric cancer (GC) is a potential dominant disease in tumor immunotherapy checkpoint inhibitors, and adoptive cell therapy have brought great hope to GC patients. However, only some patients with GC can benefit from immunotherapy, and some patients develop drug resistance. More and more studies have shown that long non-coding RNAs (lncRNAs) may be important in GC immunotherapy's prognosis and drug resistance. Here, we summarize the differential expression of lncRNAs in GC and their impact on the curative effect of GC immunotherapy, discuss potential mechanisms of activity in GC immunotherapy resistance regulated by lncRNAs. This paper reviews the differential expression of lncRNA in GC and its effect on immunotherapy efficacy in GC. In terms of genomic stability, inhibitory immune checkpoint molecular expression, the cross-talk between lncRNA and immune-related characteristics of GC was summarized, including tumor mutation burden (TMB), microsatellite instability (MSI), and Programmed death 1 (PD-1). At the same time, this paper reviewed the mechanism of tumor-induced antigen presentation and upregulation of immunosuppressive factors, as well as the association between Fas system and lncRNA, immune microenvironment (TIME) and lncRNA, and summarized the functional role of lncRNA in tumor immune evasion and immunotherapy resistance.

Comprehensive bioinformatics analysis and molecular validation of lncRNAs-mediated ceRNAs network in schizophrenia

AIMS

The present study aimed to investigate how Schizophrenia (SCZ)-specific long non-coding RNAs (lncRNAs) served as competing endogenous RNAs (ceRNAs) to modulate the biological functions and pathways involved in the pathogenesis of SCZ.

MAIN METHODS

Microarray dataset (GSE54913) was obtained from Gene Expression Omnibus (GEO) database. Differently expressed (DE) lncRNAs and mRNAs were identified by "limma" package. The binding miRNAs of lncRNAs and target mRNAs of shared miRNAs were predicted by miRcode, miRDB, miRTarbase and targetscan databases. Following the ceRNAs theory, interaction network was established and visualized with the cytoscape. Functional enrichment analysis uncovered the concentrated functions and signaling pathways that may be associated with SCZ progression. Protein-protein interaction (PPI) analysis was utilized to determine hub genes. Quantitative real-time PCR (qRT-PCR) and receiver operating characteristic curve (ROC) were performed to evaluate the expression and diagnostic value of ceRNAs members, respectively.

KEY FINDINGS

DElncRNAs and DEmRNAs were initially screened from GSE54913 to construct the SCZ-related ceRNAs network with 42 nodes and 53 edges. Functional enrichment analysis revealed that ceRNAs members appeared to be highly correlated with transcription factor activation, cell replication and tumor-related pathways. Once validated, a significant ceRNAs subnetwork was proposed as being implicated in the pathogenesis of SCZ. ROC analysis indicated that SCZ-related ceRNAs members may be sensitive diagnostic biomarkers for SCZ.

SIGNIFICANCE

The significant SCZ-related ceRNAs subnetworks (lncRNA-C2orf48A/hsa-miR-20b-5p,-17-5p/KIF23, FOXJ2) may represent promising predictive and diagnostic biomarkers and provide novel insights into the mechanism by which lncRNAs act as microRNA sponges and contribute to the pathogenesis of SCZ.

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Background: N⁶ methyladenosine (m⁶A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m⁶A regulators and m⁶A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.

Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m⁶A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m⁶A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.

Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.

Conclusion: This m6Ascore risk model based on m⁶A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

Competing Endogenous RNA (ceRNA) Networks and Splicing Switches in Cervical Cancer: HPV Oncogenesis, Clinical Significance and Therapeutic Opportunities

Cervical cancer (CC) is the primary cause of female cancer fatalities in low-middle-income countries (LMICs). Persistent infections from the human papillomavirus (HPV) can result in cervical cancer. However, numerous different factors influence the development and progression of cervical cancer. Transcriptomic knowledge of the mechanisms with which HPV causes cervical cancer pathogenesis is growing. Nonetheless, there is an existing gap hindering the development of therapeutic approaches and the improvement of patient outcomes. Alternative splicing allows for the production of numerous RNA transcripts and protein isoforms from a single gene, increasing the transcriptome and protein diversity in eukaryotes. Cancer cells exhibit astounding transcriptome modifications by expressing cancer-specific splicing isoforms. High-risk HPV uses cellular alternative splicing events to produce viral and host splice variants and proteins that drive cancer progression or contribute to distinct cancer hallmarks. Understanding how viruses utilize alternative splicing to drive pathogenesis and tumorigenesis is essential. Although research into the role of miRNAs in tumorigenesis is advancing, the function of other non-coding RNAs, including lncRNA and circRNA, has been understudied. Through their interaction with mRNA, non-coding RNAs form a network of competing endogenous RNAs (ceRNAs), which regulate gene expression and promote cervical cancer development and advancement. The dysregulated expression of non-coding RNAs is an understudied and tangled process that promotes cervical cancer development. This review will present the role of aberrant alternative splicing and immunosuppression events in HPV-mediated cervical tumorigenesis, and ceRNA network regulation in cervical cancer pathogenesis will also be discussed. Furthermore, the therapeutic potential of splicing disruptor drugs in cervical cancer will be deliberated.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

Correlation between Genes of the ceRNA Network and Tumor-Infiltrating Immune Cells and Their Biomarker Screening in Kidney Renal Clear Cell Carcinoma

This study aimed to using bioinformatics tools, qPCR, and the immunohistochemical analysis to find out factors related to the early diagnosis and prognosis of kidney renal clear cell carcinoma (KIRC). The expression profiles of lncRNA, miRNA, and mRNA of KIRC were downloaded from The Cancer Genome Atlas database. A ceRNA regulatory network was constructed based on the interaction between these three differentially expressed genes. The CIBERSORT deconvolution algorithm was used to analyze the differential distribution of 22 types of immune cells. The Kaplan–Meier survival and Cox analyses were used to screen genes of the ceRNA network and also immune cell subtypes related to the clinical and prognostic prediction of KIRC. Co-expression regulatory relationships were found among LINC01426, LINC00894, CCNA2, L1 cell adhesion molecule (L1CAM), and T follicular helper cells, which served as potential biomarkers. The results of quantitative reverse transcriptase-polymerase chain reaction showed that LINC01426 was upregulated while L1CAM was downregulated in KIRC, but no difference was found in the expression levels of LINC00894 and CCNA2 in cancer and adjacent samples. The immunohistochemical analysis showed that T follicular helper cells were more concentrated in core tissues and metastases of KIRC. In a word, co-expression relationships were found among LINC01426, L1CAM, and T follicular helper cells, and they may serve as biomarkers for early diagnosis and prognostic evaluation of KIRC.

Establishment of a circular RNA regulatory stemness-related gene pair signature for predicting prognosis and therapeutic response in colorectal cancer

Background

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract with a poor prognosis. Cancer stem cells (CSCs) affect disease outcomes and treatment responses in CRC. We developed a circular RNA (circRNA) regulatory stemness-related gene pair (CRSRGP) signature to predict CRC patient prognosis and treatment effects.

Methods

The circRNA, miRNA, and mRNA expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CRSRGPs were established based on stemness-related genes in the competing endogenous RNA (ceRNA) network. A CRSRGP signature was generated using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis of TCGA training set. The prognosis was predicted by generating a nomogram integrating the CRSRGP signature and clinicopathologic features. The model was validated in an external validation set (GSE17536). The antitumor drug sensitivity and immunotherapy responses of CRC patients in the high-risk group (HRG) and low-risk group (LRG) were evaluated by the pRRophetic algorithm and immune checkpoint analysis.

Results

We established an 18-CRSRGP signature to predict the prognosis and treatment responses of CRC patients. In the training and external validation sets, risk scores were used to categorize CRC patients into the HRG and LRG. The Kaplan–Meier analysis showed a poor prognosis for patients in the HRG and that subgroups with different clinical characteristics had significantly different prognoses. A multivariate Cox analysis revealed that the CRSRGP signature was an independent prognostic factor. The nomogram integrating clinical features and the CRSRGP signature efficiently predicted CRC patient prognosis, outperformed the current TNM staging system, and had improved practical clinical value. Anticancer drug sensitivity predictions revealed that the tumors of patients in the HRG were more sensitive to pazopanib, sunitinib, gemcitabine, lapatinib, and cyclopamine. Analysis of immune checkpoint markers demonstrated that patients in the HRG were more likely to benefit from immunotherapy.

Conclusion

An efficient, reliable tool for evaluating CRC patient prognosis and treatment response was established based on the 18-CRSRGP signature and nomogram.

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

Gastrointestinal cancers (GICs) are high-incidence malignant tumors that seriously threaten human health around the world. Their complexity and heterogeneity make the classic staging system insufficient to guide patient management. Recently, competing endogenous RNA (ceRNA) interactions that closely link the function of protein-coding RNAs with that of non-coding RNAs, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), has emerged as a novel molecular mechanism influencing miRNA-mediated gene regulation. Especially, ceRNA networks have proven to be powerful tools for deciphering cancer mechanisms and predicting therapeutic responses at the system level. Moreover, abnormal gene expression is one of the critical breaking events that disturb the stability of ceRNA network, highlighting the role of molecular biomarkers in optimizing cancer management and treatment. Therefore, developing prognostic signatures based on cancer-specific ceRNA network is of great significance for predicting clinical outcome or chemotherapy benefits of GIC patients. We herein introduce the current frontiers of ceRNA crosstalk in relation to their pathological implications and translational potentials in GICs, review the current researches on the prognostic signatures based on lncRNA or circRNA-mediated ceRNA networks in GICs, and highlight the translational implications of ceRNA signatures for GICs management. Furthermore, we summarize the computational approaches for establishing ceRNA network-based prognostic signatures, providing important clues for deciphering GIC biomarkers.

Construction of a Novel MYC-Associated ceRNA Regulatory Network to Identify Prognostic Biomarkers in Colon Adenocarcinoma

Colorectal cancer (CRC) includes colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Competitive endogenous RNA (ceRNA) is crucial for cancer pathogenesis. Abnormal expression of MYC is generally associated with a poor colon adenocarcinoma prognosis. The present study aimed to identify a novel MYC-associated ceRNA regulatory network and identify potential prognostic markers associated with COAD. We obtained the transcriptome sequencing profiles of 462 COAD cases from the TCGA database and analyzed differentially expressed genes (DEGs) in MYC high expression (MYChigh) and MYC low expression (Myclow) tumors. We identified an important lncRNA, LINC00114, which effectively predicts overall survival and plays a protective role in COAD. Moreover, the LINC00114/miR-216a-5p axis was identified as a clinical prognostic model. The predicted target genes of the LINC00114/miR-216a-5p axis include uromodulin Like 1 (UMODL1) and oncoprotein induced transcript 3 (OIT3), which are closely related to the survival and prognosis of COAD patients. In summary, we constructed a novel ceRNA regulatory network and identified potential biomarkers for the targeted therapy and prognosis of COAD.

Multi-omics analysis reveals the panoramic picture of necroptosis-related regulators in pan-cancer

Background: Unlike apoptosis, necroptosis is a tightly regulated form of programmed cell death (PCD) that occurs in a caspase-independent manner and is mainly triggered by receptor-interacting serine/threonine-protein kinases RIPK1 and RIPK3 and the RIPK3 substrate mixed-lineage kinase domain-like protein (MLKL). A growing body of evidence has documented that necroptosis, as a novel therapeutic strategy to overcome apoptosis resistance, has potential pro- or anti-tumoral effects in tumorigenesis, metastasis, and immunosurveillance. However, comprehensive multi-omics studies on regulators of necroptosis from a pan-cancer perspective are lacking.

Methods: In the present study, a pan-cancer multi-omics analysis of necroptosis-related regulators was performed by integrating over 10,000 multi-dimensional cancer genomic data across 33 cancer types from TCGA, 481 small-molecule drug response data from CTRP, and normal tissue data from GTEx. Pan-cancer pathway-level analyses of necroptosis were conducted by gene set variation analysis (GSVA), including differential expression, clinical relevance, immune cell infiltration, and regulation of cancer-related pathways.

Results: Genomic alterations and abnormal epigenetic modifications were associated with dysregulated gene expression levels of necroptosis-related regulators. Changes in the gene expression levels of necroptosis-related regulators significantly influenced cancer progression, intratumoral heterogeneity, alterations in the immunological condition, and regulation of cancer marker-related pathways. These changes, in turn, caused differences in potential drug sensitivity and the prognosis of patients.

Conclusion: Necroptosis-related regulators are expected to become novel biomarkers of prognosis and provide a fresh perspective on cancer diagnosis and treatment.

lncRNA USP30-AS1 sponges miR-765 and modulates the progression of colon cancer

Background

The incidence and mortality of colon cancer is increasing recently. It is necessary to identify effective biomarkers for the progression and prognosis of colon cancer. To assess the potential of lncRNA USP30-AS1 (USP30-AS1) in serving as the biomarker of colon cancer and unearth the underlying mechanism.

Methods

There were 123 colon cancer patients enrolled. The expression of USP30-AS1 was evaluated with PCR in tissue and cell samples. The clinical significance of USP30-AS1 was assessed with a series of statistical methods, while the CCK8 and Transwell assay were conducted to estimate its biological effect on the colon cancer cellular processes. In mechanism, the interaction of USP30-AS1 with miR-765 was evaluated with the dual-luciferase reporter assay.

Results

In colon cancer tissues, the USP30-AS1 downregulation and the miR-765 upregulation were observed, and there was a negative correlation between the USP30-AS1 expression level and the miR-765 expression level. The downregulation of USP30-AS1 related to the malignant progression and served as an adverse prognostic indicator of colon cancer. The overexpression of USP30-AS1 dramatically suppressed colon cancer cellular processes, which was alleviated by miR-765.

Conclusions

USP30-AS1 predicts the malignancy and prognosis of colon cancer patients. USP30-AS1 suppressed the progression of colon cancer through modulating miR-765.

Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression

BACKGROUND

Burn patients are prone to infection as well as immunosuppression, which is a significant cause of death. Currently, there is a lack of prognostic biomarkers for immunosuppression in burn patients. This study was conducted to identify immune-related genes that are prognosis biomarkers in post-burn immunosuppression and potential targets for immunotherapy.

METHODS

We downloaded the gene expression profiles and clinical data of 213 burn patients and 79 healthy samples from the Gene Expression Omnibus (GEO) database. Immune infiltration analysis was used to identify the proportion of circulating immune cells. Functional enrichment analyses were carried out to identify immune-related genes that were used to build miRNA-mRNA networks to screen key genes. Next, we carried out correlation analysis between immune cells and key genes that were then used to construct logistic regression models in GSE77791 and were validated in GSE19743. Finally, we determined the expression of key genes in burn patients using quantitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS

A total of 745 differently expressed genes were screened out: 299 were up-regulated and 446 were down-regulated. The number of Th-cells (CD4+) decreased while neutrophils increased in burn patients. The enrichment analysis showed that down-regulated genes were enriched in the T-cell activation pathway, while up-regulated genes were enriched in neutrophil activation response in burn patients. We screened out key genes (NFATC2, RORA, and CAMK4) that could be regulated by miRNA. The expression of key genes was related to the proportion of Th-cells (CD4+) and survival, and was an excellent predictor of prognosis in burns with an area under the curve (AUC) value of 0.945. Finally, we determined that NFATC2, RORA, and CAMK4 were down-regulated in burn patients.

CONCLUSION

We found that NFATC2, RORA, and CAMK4 were likely prognostic biomarkers in post-burn immunosuppression and potential immunotherapeutic targets to convert Th-cell dysfunction.

Construction of a ceRNA network in hepatocellular carcinoma and comprehensive analysis of immune infiltration patterns

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of refractory malignant tumor with high fatality rate. Currently, immunotherapy and competitive endogenous RNA (ceRNA) are research hotspots in HCC, but the relationship between ceRNA and the immune microenvironment in HCC is unclear.

METHODS

Firstly, a differentially expressed circRNA-miRNA-mRNA network was constructed from the GEO database, and functional enrichment analysis was performed. Next, combine the TCGA database to construct a ceRNA prognosis-related subnetwork. Establish a risk prediction model based on the mRNA in the sub-network, and evaluate the impact of the model on the prognosis. Use clinical samples to verify the expression of genes in the model. Finally, we analyzed the distribution of tumor infiltrating immune cells (TIC) in HCC, and explored the correlation between mRNAs in the ceRNA sub-network and immune infiltration.

RESULTS

We used the HCC ceRNA network (including 12 circRNA, 5 miRNA, and 8 mRNA) as a starting point for the identification of target genes (PSMD10, ESR1 and PPARGC1A) in the ceRNA prognosis-related subnetwork to establish a risk prediction model and elucidated its important role in predicting the poor prognosis of HCC. The differences in mRNA expression verified by clinical samples are consistent with the database. In addition, we found that the mRNAs in the ceRNA prognosis subnetwork are closely related to different types of TICs and immune checkpoints.

CONCLUSIONS

This study is expected to serve as a reference for the study of mechanisms underlying liver cancer, the screening of prognostic markers and the evaluation of the immune response.

Revealing potential immunotherapy targets through analysis of a ceRNA network in human colon adenocarcinoma

BACKGROUND

Microsatellite instability-high (MSI-H) is a special type of human colon adenocarcinoma (COAD) that responds well to immunotherapy. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs), which are important members of competing endogenous RNAs (ceRNAs) networks, are involved in the tumorigenesis and development of MSI-H COAD. This study aimed to establish a ceRNA network for MSI in COAD to identify targets and prognostic markers that may explain the effects of immunotherapy.

METHODS

COAD sequencing data were extracted from The Cancer Genome Atlas (TCGA), after which differentially expressed miRNAs, lncRNAs, and mRNAs were determined according to microsatellite status. After building a network based on the ceRNA hypothesis, the relationships between microsatellite status and clinical features were explored. Biological processes in the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were analyzed for specific miRNAs, lncRNAs, and mRNAs. Survival analysis was used to identify potential biomarkers.

RESULTS

Based on the inclusion criteria, a total of 363 COAD samples were obtained from TCGA. Strict screening criteria were used to identify differentially expressed RNAs in the MSI-H and microsatellite-stable groups, with 82 miRNAs, 1,280 lncRNAs, and 2121 mRNAs obtained (fold change >2, false discovery rate <0.01). Based on the RNA interaction mechanism, a miRNA-lncRNA-mRNA network was constructed, through which a subnetwork composed of 5 miRNAs was discovered. hsa-miR-31-5p, hsa-miR-302a-3p, hsa-miR-302b-3p, hsa-miR-302d-3p, hsa-miR-3619-5p and the RNAs interaction with them have the potential to become novel targets to change the effect of existing immunotherapy. GO and KEGG analyses showed that these differentially expressed miRNAs, lncRNAs, and mRNAs may play key roles in tumorigenesis, tumor development, and drug efficacy, with natural killer cells potentially becoming the next emerging targets for immunotherapy enhancement. Moreover, survival analysis identified 10 lncRNAs as potential survival markers.

CONCLUSIONS

This study identified novel immunotherapy targets and revealed potential biomarkers for COAD according to microsatellite status.

Construction of a competing endogenous RNA network related to the prognosis of cholangiocarcinoma and comprehensive analysis of the immunological correlation

Background

Cholangiocarcinoma (CCA) is a malignant tumor of the digestive system, with occult onset in the early stage, a high degree of malignancy in the late stage, and poor prognosis. At present, the pathogenesis of CCA is not clear, and there is a lack of effective immunotherapy. The purpose of this study was to identify the potential regulatory mechanism of CCA and analyze the possibility of its related immunotherapy.

Methods

The circular RNAs (circRNAs) expression profile data of CCA was downloaded from the Gene Expression Omnibus (GEO) database; the miRNA and mRNA expression profile data of CCA were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic factors were screened by univariate Cox regression analysis, and the competing endogenous RNA (ceRNA) network was constructed via survival analysis. Multivariate Cox analysis was used to screen the independent prognostic factors and construct a prognostic correlation subnetwork. Analyzing the tumor microenvironment of CCA and survival analysis were performed according to the score of the microenvironment, and the distribution of tumor infiltrating immune cells (TICs) in CCA was calculated using the CIBERSORT algorithm. We explored the expression pattern of the target genes in pan-cancer, and the correlation between the key genes in the ceRNA subnetwork, TICs and immune checkpoints was analyzed using an online database. Finally, the expression levels of target genes were validated based on the Human Protein Atlas (HPA) databases.

Results

We screened four circRNAs, 10 miRNAs, and 17 mRNAs with significant differences, and constructed the ceRNA network. Independent prognostic factors were screened by multivariate Cox regression analysis, and a subnetwork containing five nodes (hsa_circ_0002073→hsa-mir-4524a-3p→SLC16A3/SLC35E4/DDX4) was constructed. Further analysis showed that SLC16A3 was not only an independent posterior factor of CCA, but was also closely correlated with immune cells, immune checkpoints, and immunotherapy, and had a certain regulatory effect on the tumor microenvironment.

Conclusions

Our study identified a novel prognostic marker of CCA, SLC16A3, and revealed the regulatory role of SLC16A3 in the tumor microenvironment, which is expected to provide new insights for the early diagnosis, prognosis, and targeted therapy of CCA.

Identification and quantification of immune infiltration landscape on therapy and prognosis in left- and right-sided colon cancer

Background

The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits.

Methods

The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by “ESTIMATE” and “CIBERSORT.” We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort.

Results

The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4⁺ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1.

Conclusion

The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-021-03076-2.

Integrated Analysis of the Expression Characteristics, Prognostic Value, and Immune Characteristics of PPARG in Breast Cancer

Background: Breast cancer (BRCA) is the most frequent malignancy. Identification of potential biomarkers could help to better understand and combat the disease at early stages.

Methods: We selected the overlapping genes of differential expressed genes and genes in BRCA-highly correlated modules by Weighted Gene Co-Expression Network Analysis (WGCNA) in TCGA and GEO data and performed KEGG and GO enrichment. PPARG was achieved from Protein-Protein Interaction (PPI) network analysis and prognostic analysis. TIMER, UALCAN, GEO, TCGA, and western blot analysis were used to validate the expression of PPARG in BRCA. PPARG was further analyzed by DNA methylation, immune parameters, and tumor mutation burden.

Results: Among 381 overlapping genes, the lipid metabolic process was identified as highly enriched pathways in BRCA by TCGA and GEO data. When the prognostic analysis of 10 core genes by PPI network was performed, results revealed that high expression of PPARG was significantly correlated to a better prognosis. PPARG was lesser expression in BRCA according to TIMER, UALCAN, GEO, TCGA, and western blot in both mRNA level and protein level. PPARG had several high DNA methylation level sites and the methylation level is negatively correlated to expression. PPARG is also correlated to TNM stages, tumor microenvironment, and tumor burden.

Conclusions: Findings of our study identified the PPARG as a potential biomarker by confirming its low expression in BRCA and its correlation to prognosis. Moreover, its correlation to DNA methylation and tumor microenvironment may guide new therapeutic strategies for BRCA patients.

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer's Disease

BACKGROUND

As the most common neurodegenerative disease, Alzheimer's disease (AD) leads to progressive loss of cognition and memory. Presently, the underlying pathogenic genes of AD patients remain elusive, and effective disease-modifying therapy is not available. This study explored novel biomarkers that can affect diagnosis and treatment in AD based on immune infiltration.

METHODS

The gene expression profiles of 139 AD cases and 134 normal controls were obtained from the NCBI GEO public database. We applied the computational method CIBERSORT to bulk gene expression profiles of AD to quantify 22 subsets of immune cells. Besides, based on the use of the Least Absolute Shrinkage Selection Operator (LASSO), this study also applied SVM-RFE analysis to screen key genes. GO-based semantic similarity and logistic regression model analyses were applied to explore hub genes further.

RESULTS

There was a remarkable significance in the infiltration of immune cells between the subgroups. The proportions for monocytes, M0 macrophages, and dendritic cells in the AD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as NK cell resting, T-cell CD4 naive, T-cell CD4 memory activation, and eosinophils. Additionally, seven genes (ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL) were identified as hub genes. Then we performed the analysis of immune factor correlation, gene set enrichment analysis (GSEA), and GO based on seven hub genes. The AUC of ROC prediction model in test and validation sets were 0.845 and 0.839, respectively. Eventually, the mRNA expression analysis of ABCA2, NDUFA2, CREBRF, and CD72 revealed significant differences among the seven hub genes and then was confirmed by RT-PCR.

CONCLUSION

A model based on immune cell infiltration might be used to forecast AD patients' diagnosis, and it provided a new perspective for AD treatment targets.

Identification of a Competing Endogenous RNA Network Related to Immune Signature in Lung Adenocarcinoma

Background

The establishment of immunotherapy has led to a new era in oncotherapy. But the signature of immune-related genes (IRGs) in LUAD remains to be elucidated. Here we use integrated analysis to identify IRGs roles in immune signature and detect their relationship with competing endogenous RNA (ceRNA) networks in LUAD progression.

Methods

By analyzing the RNA-seq data from different platforms, we recognized the differentially expressed genes (DEGs) of each platform and screened out the top 20 hub IRGs related to immune responses. Then, we applied the CIBERSORT algorithm to explore the landscape of tumor-infiltrating immune cells (TILs) in LUAD and their connection with hub genes. Next, we predicted and validated the upstream miRNAs and lncRNAs according to their expression and prognostic roles. Finally, we constructed and validated an immune-related ceRNA network by co-expression analysis.

Results

A total of 71 IRGs were identified among 248 DEGs, which play key roles in immune responses. CIBERSORT analysis showed that six hub genes were closely related to TILs, such as SPP1 and naive B cells (R = −0.17), TEK and resting mast cells (R = 0.37). Stepwise prediction and validation from mRNA to lncRNA, including 6 hub genes, 5 miRNAs, and 9 lncRNAs, were applied to construct a ceRNA network. Ultimately, we confirmed the TMPO-AS1/miR-126-5p/SPP1 and CARD8-AS1/miR-21-5p/TEK as immune-related ceRNA networks in LUAD progression.

Conclusion

We elucidated two immune-related ceRNA networks in LUAD progression, which can be considered as immunotherapy targets for this disease.

Construction and analysis of a ceRNA network and patterns of immune infiltration in bladder cancer

Background

Bladder cancer (BC) is the ninth most common malignant tumor, accounting for an estimate of 549,000 new BC cases and 200,000 BC-related deaths worldwide in 2018. The prognosis of BC has not substantially improved despite significant advances in the diagnosis and treatment of the disease.

Methods

The RNA sequencing (RNA-seq) data and clinical information of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm was used to assess immune infiltration. The survival analyses were performed using the selected components of a ceRNA network and selected immune cell types by least absolute shrinkage and selection operator (LASSO) Cox regression to calculate the risk score. The accuracy of prognosis prediction was determined by receiver operating characteristic (ROC) curves, survival curves, and nomograms. Finally, the correlation analysis was performed to investigate the relationships between the signature components of the ceRNA network and the immune cell signature.

Results

Two completed survival analyses included selected components of the ceRNA network (ELN, SREBF1, DSC2, TTLL7, DIP2C, SATB1, hsa-miR-20a-5p, and hsa-miR-29c-3p) and selected immune cell types (M0 macrophages, M2 macrophages, resting mast cells, and neutrophils). ROC curves, survival curves (all P values <0.05), nomograms, and calibration curves indicated that the accuracy of the two survival analyses was acceptable. Moreover, the correlations between TTLL7 and resting mast cells (R=0.24, P<0.001), DSC2 and resting mast cells (R=−0.23, P<0.001), ELN and resting mast cells (R=0.44, P<0.001), and hsa-miR-29c-3p and M0 macrophages (R=−0.29, P<0.001) were significant, indicating that interactions of these factors may play significant roles in the prognosis of BC.

Conclusions

TTLL7, DSC2, ELN, hsa-miR-29c-3p, resting mast cells, and M0 macrophages may play an important role in the development of BC. However, additional studies are needed to confirm this hypothesis.

Hypoxia Constructing the Prognostic Model of Colorectal Adenocarcinoma and Related to the Immune Microenvironment

Background: Hypoxia is a common phenomenon in solid tumors, which plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism and chemoradiotherapy resistance. However, comprehensive analysis of hypoxia markers in colorectal adenocarcinoma (COAD) is still lacking. And there is a need for mechanism exploration and clinical application.

Methods: The gene expression, mutation and clinical data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. Tumor samples from TCGA were randomly divided into the training and internal validation groups, while tumor samples from GEO were used as the external validation group. Univariate COX—LASSO—multivariate COX method was applied to construct the prognostic model. We clustered all TCGA tumor samples into high, medium and low hypoxia groups, evaluated the correlation between hypoxia degree and immunoactivity, and explored the combined effect of mutation for common target genes and model riskscore on survival in COAD patients. Finally, we developed a dynamic nomograph App online for direct clinical application and carried out multiple validations of the prognostic model.

Results: Our hypoxia-related prognostic model for COAD patients is accurate and has been successfully validated internally and externally. Single Sample Gene Set Enrichment Analysis (ssGSEA) and Gene Set Enrichment Analysis (GSEA) results suggest that for COAD patients with higher hypoxia, the stronger the associated immunosuppressive activity, providing a possible mechanism for the lower survival rate. Finally, the dynamic nomograph App online enhances the clinical translational significance of the study.

Conclusion: In this study, an accurate prognostic model for COAD patients was established and validated. In addition, our innovative findings include correlations between hypoxia levels and immune activity, as well as an in-depth exploration of common target gene mutations.

Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) can be divided into left-sided and right-sided COAD (LCCs and RCCs, respectively). They have unique characteristics in various biological aspects, particularly immune invasion and prognosis. The purpose of our study was to develop a prognostic risk scoring model (PRSM) based on differentially expressed immune-related genes (IRGs) between LCCs and RCCs, therefore the prognostic key IRGs could be identified.

Methods

The gene sets and clinical information of COAD patients were derived from TCGA and GEO databases. The comparison of differentially expressed genes (DEGs) of LCCs and RCCs were conducted with appliance of “Limma” analysis. The establishment about co-expression modules of DEGs related with immune score was conducted by weighted gene co-expression network analysis (WGCNA). Furthermore, we screened the module genes and completed construction of gene pairs. The analysis of the prognosis and the establishment of PRSM were performed with univariate- and lasso-Cox regression. We employed the PRSM in the model group and verification group for the purpose of risk group assignment and PRSM accuracy verification. Finally, the identification of the prognostic key IRGs was guaranteed by the adoption of functional enrichment, “DisNor” and protein-protein interaction (PPI).

Results

A total of 215 genes were screened out by differential expression analysis and WGCNA. A PRSM with 16 immune-related gene pairs (IRGPs) was established upon the genes pairing. Furthermore, we confirmed that the risk score was an independent factor for survival by univariate- and multivariate-Cox regression. The prognosis of high-risk group in model group (P < 0.001) and validation group (P = 0.014) was significantly worse than that in low-risk group. Treg cells (P < 0.001) and macrophage M0 (P = 0.015) were highly expressed in the high-risk group. The functional analysis indicated that there was significant up-regulation with regard of lymphocyte and cytokine related terms in low-risk group. Finally, we identified five prognostic key IRGs associated with better prognosis through PPI and prognostic analysis, including IL2RB, TRIM22, CIITA, CXCL13, and CXCR6.

Conclusion

Through the analysis and screening of the DEGs between LCCs and RCCs, we constructed a PRSM which could predicate prognosis of LCCs and RCCs, and five prognostic key IRGs were identified as well. Therefore, the basis for identifying the benefits of immunotherapy and immunomodulatory was built.