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Noncoding RNAs (ncRNAs) constitute the majority of the human transcribed genome. This largest class of RNA transcripts plays diverse roles in a multitude of cellular processes, and has been implicated in many pathological conditions, especially cancer. The different subclasses of ncRNAs include microRNAs, a class of short ncRNAs; and a variety of long ncRNAs (lncRNAs), such as lincRNAs, antisense RNAs, pseudogenes, and circular RNAs. Many studies have demonstrated the involvement of these ncRNAs in competitive regulatory interactions, known as competing endogenous RNA (ceRNA) networks, whereby lncRNAs can act as microRNA decoys to modulate gene expression. These interactions are often interconnected, thus aberrant expression of any network component could derail the complex regulatory circuitry, culminating in cancer development and progression. Recent integrative analyses have provided evidence that new computational platforms and experimental approaches can be harnessed together to distinguish key ceRNA interactions in specific cancers, which could facilitate the identification of robust biomarkers and therapeutic targets, and hence, more effective cancer therapies and better patient outcome and survival.

Protein aggregation is classically considered the main cause of neuronal death in neurodegenerative diseases (NDDs). However, increasing evidence suggests that alteration of RNA metabolism is a key factor in the etiopathogenesis of these complex disorders. Non-coding RNAs are the major contributor to the human transcriptome and are particularly abundant in the central nervous system, where they have been proposed to be involved in the onset and development of NDDs. Interestingly, some ncRNAs (such as lncRNAs, circRNAs and pseudogenes) share a common functionality in their ability to regulate gene expression by modulating miRNAs in a phenomenon known as the competing endogenous RNA mechanism. Moreover, ncRNAs are found in body fluids where their presence and concentration could serve as potential non-invasive biomarkers of NDDs. In this review, we summarize the ceRNA networks described in Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis and spinocerebellar ataxia type 7, and discuss their potential as biomarkers of these NDDs. Although numerous studies have been carried out, further research is needed to validate these complex interactions between RNAs and the alterations in RNA editing that could provide specific ceRNET profiles for neurodegenerative disorders, paving the way to a better understanding of these diseases.

Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).BACKGROUNDTumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).\"GEO2R,\" \"limma\" R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. \"Survival,\" \"survminer,\" \"rms\" R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.METHODS\"GEO2R,\" \"limma\" R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. \"Survival,\" \"survminer,\" \"rms\" R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = -0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By \"survival ROC\" R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.RESULTSA total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = -0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By \"survival ROC\" R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.CONCLUSIONIn conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.

Pseudogenes are now known to regulate their protein-coding counterparts. Additionally, disturbances of 3′UTRs could increase the risk of cancer susceptibility by acting as modulators of gene expression. The aim of this study was to investigate the roles of the pseudogene CYP4Z2P-3′UTR and functional gene CYP4Z1-3′UTR in breast cancer angiogenesis process. The levels of CYP4Z2P- and CYP4Z1-3′UTR and miRNA of interests were measured in 22 cancerous tissues paired with non-cancerous samples by qRT-PCR. The effects of CYP4Z2P- and CYP4Z1-3′UTR were studied by overexpression and RNA interference approaches in vitro and ex vivo . Insights of the mechanism of competitive endogenous RNAs were gained from bioinformatic analysis, luciferase assays, and western blot. The positive CYP4Z2P/CYP4Z1 interaction and negative interaction between predicted miRNAs and CYP4Z2P or CYP4Z1 were identified via qRT-PCR assay and bivariate correlation analysis. CYP4Z2P- and CYP4Z1-3′UTR share several miRNA-binding sites, including miR-211, miR-125a-3p, miR-197, miR-1226, and miR-204. The CYP4Z2P- and CYP4Z1-3′UTRs arrest the interference caused by of these miRNAs, resulting in increased translation of CYP4Z1. Moreover, ectopic expression of the CYP4Z2P- and CYP4Z1-3′UTRs exhibit tumor angiogenesis-promoting properties in breast cancer collectively by inducing the phosphorylation of ERK1/2 and PI3K/Akt. Co-transfection with Dicer siRNA reversed the CYP4Z2P 3′UTR-mediated changes. Additionally, PI3K or ERK inhibitors reversed CYP4Z2P- and CYP4Z1-3′UTR-mediated changes in VEGF-A expression. Increased CYP4Z2P- and CYP4Z1-3′UTR expression promotes tumor angiogenesis in breast cancer partly via miRNA-dependent activation of PI3K/Akt and ERK1/2. The CYP4Z2P- and CYP4Z1-3′UTRs could thus be used as combinatorial miRNA inhibitors.

A recently discovered dimension of post-transcriptional gene regulation involves co-regulatory crosstalk between RNA transcripts, which compete for common pools of microRNA (miRNA) molecules. These competing endogenous RNAs (ceRNAs), or natural miRNA sponges, have an active role in regulating miRNA availability within the cell and form intertwined regulatory networks. Recent reports have implicated diverse RNA species including protein-coding messenger RNAs and non-coding RNAs as ceRNAs in human development and diseases including human cancer. In this review, we discuss the most recent discoveries that implicate natural miRNA decoys in human cancer biology, as well as exciting advances in the study of ceRNA networks and dynamics. The structure and topology of intricate genome-scale ceRNA networks can be predicted computationally, and their dynamic response to fluctuations in ceRNA and miRNA levels can be studied via mathematical modeling. Additionally, the development of new methods to quantitatively determine absolute expression levels of miRNA and ceRNA molecules have expanded the capacity to accurately study the efficiency of ceRNA crosstalk in diverse biological models. These major milestones are of critical importance to identify key components of ceRNA regulatory networks that could aid the development of new approaches to cancer diagnostics and oligonucleotide-based therapeutics.

Circular RNAs (circRNAs) are connected at the 3′ and 5′ ends by exon or intron cyclization, forming a complete ring structure. circRNA is more stable and conservative than linear RNA and abounds in various organisms. In recent years, increasing numbers of reports have found that circRNA plays a major role in the biological functions of a network of competing endogenous RNA (ceRNA). circRNAs can compete together with microRNAs (miRNAs) to influence the stability of target RNAs or their translation, thus, regulating gene expression at the transcriptional level. circRNAs are involved in biological processes such as tumor cell proliferation, apoptosis, invasion, and migration as ceRNAs. circRNAs, therefore, represent promising candidates for clinical diagnosis and treatment. Here, we review the progress in studying the role of circRNAs as ceRNAs in tumors and highlight the participation of circRNAs in signal transduction pathways to regulate cellular functions.

Shipu Liu,1 Zijian Zhang2 1Department of Occupational Medicine and Clinical Toxicology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, People’s Republic of China; 2Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, People’s Republic of ChinaCorrespondence: Zijian Zhang, Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China, Email zzhang742@uwalumni.comIntroduction: Emerging research emphasizes the critical role of local microbiota in shaping the tumor microenvironment (TME) and influencing cancer progression. Lung adenocarcinoma (LUAD) is distinguished by unique bacterial communities that appear to regulate immune responses, gene expression, and patient outcomes.Methods: We compiled microbiome profiles from several cancer types—including LUAD, lung squamous cell carcinoma (LUSC), breast carcinoma (BRCA), and thyroid carcinoma (THCA)—using public databases. Non-negative matrix factorization (NMF) was employed to categorize LUAD cases based on TME features, while DESeq2 was used to pinpoint bacterial taxa with differing abundance. Multi-omics networks were developed to integrate microbial, transcriptomic, and clinical data. For in vitro verification, we conducted siRNA-mediated knockdown of the long non-coding RNA LCIIAR and ISG15 in Lewis lung carcinoma cells, followed by proliferation assays.Results: In contrast to LUSC, BRCA, and THCA, LUAD exhibited distinct microbial populations, with notable enrichment of Cylindrospermopsis, Cyanothece, and Sulfolobus. NMF clustering identified two LUAD subtypes with differing prognoses. One longer survival cluster, marked by reduced bacterial presence and stronger antitumor immunity—reflected in stronger immune response, increased effector T cells activity, and greater immune cell infiltration. A competing endogenous RNA (ceRNA) network analysis established a link between LCIIAR and ISG15, both overexpressed in LUAD and associated with worse survival outcomes. Knockdown LCIIAR or ISG15 through siRNA significantly inhibited lung cancer cell proliferation, pointing to their roles in tumor growth and ceRNA-mediated regulation.Conclusion: LUAD features a distinctive microbiota that engages with inflammatory and ceRNA regulatory pathways. These observations underscore the value of targeting microbiome-influenced mechanisms, such as the LCIIAR–ISG15 axis, as a promising approach to enhance treatment outcomes in lung adenocarcinoma.Keywords: lung adenocarcinoma, microbiota, tumor microenvironment, ceRNA network

Colorectal cancer (CRC) is the third most common cancer and has a high metastasis and reoccurrence rate. Long noncoding RNAs (lncRNAs) play an important role in CRC growth and metastasis. Recent studies revealed that lncRNAs participate in CRC progression by coordinating with microRNAs (miRNAs) and protein-coding mRNAs. LncRNAs function as competitive endogenous RNAs (ceRNAs) by competitively occupying the shared binding sequences of miRNAs, thus sequestering the miRNAs and changing the expression of their downstream target genes. Such ceRNA networks formed by lncRNA/miRNA/mRNA interactions have been found in a broad spectrum of biological processes in CRC, including liver metastasis, epithelial to mesenchymal transition (EMT), inflammation formation, and chemo-/radioresistance. In this review, we summarize typical paradigms of lncRNA-associated ceRNA networks, which are involved in the underlying molecular mechanisms of CRC initiation and progression. We comprehensively discuss the competitive crosstalk among RNA transcripts and the novel targets for CRC prognosis and therapy.