UNASSIGNED: Epigenetic modifications play a crucial role in cancer development, and our study utilized public data to analyze which leads to the discovery of significant epigenetic abnormalities in lncRNAs, offering valuable insights into prognosis and treatment strategies for renal carcinoma.
UNASSIGNED: Public data were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) database. The analysis of the online public data was all completed in R software.
UNASSIGNED: We discovered a great number of epigenetic abnormalities of lncRNA in renal cancer, which is achieved by comparing the following modification and methylation of histone region changes on the promoter and enhancer of lncRNA: H3K27ac, H3K4me1, H3K4me3. As a result, 12 specific epigenetic disorders of lncRNA genes in renal cancer were identified. Finally, based on this lncRNA, we investigated the prognosis of renal cancer samples, among which 8 lncRNA can be seen as markers of prognosis in renal cancer, which had great prediction ability for ccRCC prognosis. Meanwhile, high risk score may pose response better to axitinib and nilotinib, but not sorafenib or sunitinib. Beyond, we observed an elevated level of risk score in immunotherapy non-responders. Further, biological enrichment and immuno-infiltration analysis was conducted to investigate the fundamental differences between patients categorized as high or low risk.
UNASSIGNED: Our research improves the understanding in the function of epigenetic dysregulated long non-coding RNAs in renal carcinoma.

BACKGROUND: Endoplasmic reticulum stress (ERS) could be a strategy for treating malignant tumors. Moreover, long noncoding RNAs (lncRNAs) can promote tumorigenesis and progression, and forecast the prognosis of cancers. Nevertheless, the prognostic value of ERS-related lncRNAs has not been reported in lung adenocarcinoma (LUAD).
METHODS: The messenger RNA (mRNA), microRNA (miRNA) and lncRNA expression data related to LUAD were obtained in public databases (TCGA and GEO databases). Prognostic ERS-related differentially expressed lncRNAs (ERS-DELs) were obtained and used to build an ERS-related model by Cox regression analysis. Moreover, we further screened independent prognostic elements and built a nomogram. Furthermore, enrichment analysis of genes was conducted to investigate the functions. A lncRNA-miRNA-mRNA network was built to explore mechanism of lncRNAs. Finally, qRT-PCR was utilized to examine the expression levels of lncRNAs.
RESULTS: 30 ERS-DELs were identified, and an ERS-related signature was built based on AF131215.2, LINC00472, LINC01352, RP1-78O14.1, RP11-253E3.3, RP11-98D18.9, and SNHG12. Gene set enrichment analysis indicated that genes in the high-risk group were chiefly focused on the regulation of mRNA binding, and genes in the low-risk group were significantly focused on protein localization to cilia. A lncRNA-miRNA-mRNA network, containing 7 signature lncRNAs, 23 miRNAs, and 128 mRNAs, was also established. Eventually, quantitative real-time polymerase chain reaction was used to confirm that seven prognostic lncRNAs had a consistent expression with the analysis.
CONCLUSIONS: An ERS-related signature containing seven prognostic lncRNAs was built, which offered new thinking concerning the role of ERS-related lncRNAs in LUAD.

The PI3K/Akt pathway plays a critical role in the progression and treatment of oral squamous cell carcinoma (OSCC). Recent research has uncovered the involvement of long non-coding RNAs (lncRNAs) in regulating this pathway, influencing OSCC cell proliferation, survival, and metastasis. This review explores the latest findings on how certain lncRNAs act as either cancer promoters or cancer inhibitors within the PI3K/Akt signaling pathway. Certain lncRNAs act as oncogenic or tumor-suppressive agents, making them potential diagnostic and prognostic markers. Targeting these lncRNAs may lead to novel therapeutic strategies. The evolving fields of precision medicine and artificial intelligence promise advancements in OSCC diagnosis and treatment, enabling more personalized and effective patient care.

Many studies support the idea that long noncoding RNAs (lncRNAs) are significantly involved in the process of cardiomyocyte (CM) regeneration following a myocardial infarction (MI). This study aimed to systematically review the emerging role of lncRNAs in cardiac regeneration by promoting CM proliferation after MI. Furthermore, the review summarized potential targets and the underlying mechanisms of lncRNAs to induce heart regeneration, suggesting utilizing lncRNAs as innovative therapeutic targets for mitigating MI injuries. We searched the PubMed, Scopus, and Web of Science databases for studies on lncRNAs that play a role in heart regeneration after MI. We used search terms that included MI, lncRNAs, CM, and proliferation. Relevant English articles published until June 11, 2023, were systematically reviewed based on inclusion and exclusion criteria. A total of 361 publications were initially identified, and after applying the inclusion and exclusion criteria, nine articles were included in this systematic review. These studies investigated the role of critical lncRNAs in cardiac regeneration after MI, including five upregulated and four downregulated lncRNAs. Acting as a competitive endogenous RNA is one of the main roles of lncRNAs in regulating genes involved in CM proliferation through binding to target microRNAs. The main molecular processes that greatly increase CM proliferation are those that turn on the Hippo/YAP1, PI3K/Akt, JAK2-STAT3, and E2F1-ECRAR-ERK1/2 signaling pathways. This systematic review highlights the significant role of lncRNAs in heart regeneration after MI and their impact on CM proliferation. The findings suggest that lncRNAs could serve as potential targets for therapeutic interventions aiming to enhance cardiac function.

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Copper-mediated cell death presents distinct pathways from established apoptosis processes, suggesting alternative therapeutic approaches for colon cancer. Our research aims to develop a predictive framework utilizing long-noncoding RNAs (lncRNAs) related to cuproptosis to predict colon cancer outcomes while examining immune interactions and intercellular signaling. We obtained colon cancer-related human mRNA expression profiles and clinical information from the Cancer Genome Atlas repository. To isolate lncRNAs involved in cuproptosis, we applied Cox proportional hazards modeling alongside the least absolute shrinkage and selection operator technique. We elucidated the underlying mechanisms by examining the tumor mutational burden, the extent of immune cell penetration, and intercellular communication dynamics. Based on the model, drugs were predicted and validated with cytological experiments. A 13 lncRNA-cuproptosis-associated risk model was constructed. Two colon cancer cell lines were used to validate the predicted representative mRNAs with high correlation coefficients with copper-induced cell death. Survival enhancement in the low-risk cohort was evidenced by the trends in Kaplan-Meier survival estimates. Analysis of immune cell infiltration suggested that survival was induced by the increased infiltration of naïve CD4+ T cells and a reduction of M2 macrophages within the low-risk faction. Decreased infiltration of naïve B cells, resting NK cells, and M0 macrophages was significantly associated with better overall survival. Combined single-cell analysis suggested that CCL5-ACKR1, CCL2-ACKR1, and CCL5-CCR1 pathways play key roles in mediating intercellular dialogues among immune constituents within the neoplastic microhabitat. We identified three drugs with a high sensitivity in the high-risk group. In summary, this discovery establishes the possibility of using 13 cuproptosis-associated lncRNAs as a risk model to assess the prognosis, unravel the immune mechanisms and cell communication, and improve treatment options, which may provide a new idea for treating colon cancer.

Non-coding RNAs (ncRNAs) belong to a class of untranslated nucleic acids involved in regulation of gene expression. ncRNAs are categorized as small (<200 ribonucleotides in length), i.e., microRNAs (miRNAs), and long ncRNAs (lncRNAs) (200 to thousands of ribonucleotides in length) and circular RNAs (circRNAs). In contrast to miRNAs, the roles of lncRNAs in general and circRNAs in bone metabolism specifically are not well understood. As such, a comprehensive understanding of these RNA species in bone turnover could be of great value in the development of new diagnostic tools and therapeutic targets. Unfortunately, measurement of these unique RNAs lacks standardization, a component critical to clinical translation. This review examines the potential role of lncRNA and circRNA as bone biomarkers, the need for validated and standardized measurement and challenges thereof.

BACKGROUND: It is evident that long non-coding RNAs (lncRNAs) are implicated in the pathogenesis of periodontitis. However, the detailed functional mechanisms remain unknown.
OBJECTIVE: This study aimed to elucidate the pathogenic mechanisms of lncRNAs in periodontitis by investigating their regulation of protein-coding gene expression.
METHODS: Human Gingival Fibroblasts-1 (HGF-1) were stimulated with 5 μg/mL of Lipopolysaccharide (LPS) for 24 hours to construct the periodontitis cell model. qRTPCR and western blot analyses were carried out to determine mRNA and protein levels of genes induced by LPS or involved in the inflammatory response. Cytokine levels and inflammatory proteins were assayed using ELISA. Transcriptome sequencing and analysis were conducted to reveal the expression signatures of lncRNAs. DESeq2 (v1.4.5) was used to analyze differentially expressed genes. Gene function enrichment was carried out using Phyper. AnimalTFDB v3.0 was used to analyze transcription factors involved in the pathogenesis of periodontitis. Prot\\ein domains and families of the target proteins were identified based on the Pfam protein family database.
RESULTS: In LPS-treated HGF-1 cells, we detected the secretion of TNF-α and IL-1β, along with the production of MDA and ROS, indicating that LPS significantly triggered inflammatory responses and oxidative stress in HGF-1 cells. A total of 15,295 lncRNAs were detected in both the control (ConT) and LPS-treated groups. We selected 10 significantly differentially co-expressed lncRNA-coding genes (MIR222HG, SNHG15, SNHG12, URS00005F6AA3, URS00009C153E, URS0000D57D7F, URS00019A4688, URS00019AF240, URS00019C6526, and URS0001A00B79) as potential biomarkers for diagnosing the progression of periodontitis. An interaction network consisting of 2 lncRNA- encoding genes (MIR222HG and SNHG15) and protein-encoding genes (CBX5, NUPR1, CHAC1, and MAB21L3) may be involved in the pathogenesis of periodontitis. The ceRNA network analysis revealed the differentially expressed lncRNAs to be involved in inflammatory response, immune infiltration, collagen fiber synthesis, and bone remodeling in LPS-induced periodontitis.
CONCLUSIONS: This study has identified pivotal molecules implicated in the pathogenesis of periodontitis, including those involved in inflammation regulation, collagen fiber synthesis, and bone remodeling. Our findings may contribute to explaining how lncRNAs participate in the pathological process of periodontitis.

BACKGROUND: Long non-coding RNAs (lncRNAs) are RNA transcripts of more than 200 nucleotides that do not encode canonical proteins. Their biological structure is similar to messenger RNAs (mRNAs). To distinguish between lncRNA and mRNA transcripts quickly and accurately, we upgraded the PLEK alignment-free tool to its next version, PLEKv2, and constructed models tailored for both animals and plants.
RESULTS: PLEKv2 can achieve 98.7% prediction accuracy for human datasets. Compared with classical tools and deep learning-based models, this is 8.1%, 3.7%, 16.6%, 1.4%, 4.9%, and 48.9% higher than CPC2, CNCI, Wen et al.\'s CNN, LncADeep, PLEK, and NcResNet, respectively. The accuracy of PLEKv2 was > 90% for cross-species prediction. PLEKv2 is more effective and robust than CPC2, CNCI, LncADeep, PLEK, and NcResNet for primate datasets (including chimpanzees, macaques, and gorillas). Moreover, PLEKv2 is not only suitable for non-human primates that are closely related to humans, but can also predict the coding ability of RNA sequences in plants such as Arabidopsis.
CONCLUSIONS: The experimental results illustrate that the model constructed by PLEKv2 can distinguish lncRNAs and mRNAs better than PLEK. The PLEKv2 software is freely available at https://sourceforge.net/projects/plek2/ .

Cancer drug resistance poses a significant obstacle to successful chemotherapy, primarily driven by the activity of ATP-binding cassette (ABC) transporters, which actively efflux chemotherapeutic agents from cancer cells, reducing their intracellular concentrations and therapeutic efficacy. Recent studies have highlighted the pivotal role of long noncoding RNAs (lncRNAs) in regulating this resistance, positioning them as crucial modulators of ABC transporter function. lncRNAs, once considered transcriptional noise, are now recognized for their complex regulatory capabilities at various cellular levels, including chromatin modification, transcription, and post-transcriptional processing. This review synthesizes current research demonstrating how lncRNAs influence cancer drug resistance by modulating the expression and activity of ABC transporters. lncRNAs can act as molecular sponges, sequestering microRNAs that would otherwise downregulate ABC transporter genes. Additionally, they can alter the epigenetic landscape of these genes, affecting their transcriptional activity. Mechanistic insights reveal that lncRNAs contribute to the activity of ABC transporters, thereby altering the efflux of chemotherapeutic drugs and promoting drug resistance. Understanding these interactions provides a new perspective on the molecular basis of chemoresistance, emphasizing the regulatory network of lncRNAs and ABC transporters. This knowledge not only deepens our understanding of the biological mechanisms underlying drug resistance but also suggests novel therapeutic strategies. In conclusion, the intricate interplay between lncRNAs and ABC transporters is crucial for developing innovative solutions to combat cancer drug resistance, underscoring the importance of continued research in this field.