Identification of Enhancer RNA CDK6-AS1 as a Potential Novel Prognostic Biomarker in Gastric Cancer

The Relationship Between Gene Mutations and the Clinicopathological Features and Prognosis of Gastric Cancer

Current treatments for gastric cancer (GC) are suboptimal. Potential therapeutic targets for GC were screened using next-generation sequencing. We examined many mutation genes linked to GC, including TP53 (60%), PIK3CA (19%), LRP1B (13%), and ERBB2 (12%), ARID1A (9%), KMT2C (9%), and KRAS (7%). The KMT2C, KRAS, CDK6, and ARID1A wild-type genes were dominant in diffuse-type GC (P < .05), but mutations did not influence prognosis. Patients with APC (6%) and CDH1 (8%) wild-type GC presented with vascular invasion (P < .05). Patients with ATR (2%) wild-type GC were prone to lymph node metastasis (P < .05). Patients with ARID1A (9%) wild-type GC had reduced programmed death ligand 1 expression (<1, P < .05). We found that patients who received chemotherapy had a better prognosis than those who did not (although there was no statistical difference), with platinum-based group having better prognosis and uracil combined with paclitaxel group having worse prognosis.

Integrated analysis of transcriptome and epigenome reveals ENSR00000272060 as a potential biomarker in gastric cancer

Background: Enhancer RNAs (eRNAs) are involved in gene expression regulation. Although functional roles of eRNAs in the pathophysiology of neoplasms have been reported, their involvement in gastric cancer (GC) is less known. Materials & methods: A network-based integrative approach was utilized for analyzing transcriptome and epigenome alterations in GC, and an eRNA was selected for experimental validation. Survival analysis and clinicopathological associations were also performed. Results: A hub eRNA, ENSR00000272060, showed significantly increased expression in tumor versus nontumor tissues, as well as an association with clinicopathological features. A seven-gene prognostic model was also constructed. Conclusion: The constructed network provides a comprehensive understanding of the underlying processes implicated in the progression of GC, along with a starting point from which to derive potential diagnostic/prognostic biomarkers.

Identification of enhancer RNA AC005515.1 as a novel biomarker for prognosis in esophageal cancer and predictors of immunotherapy response

Background

The enhancer RNA (eRNA) signature shows excellent promise in the prognostic role of many malignancies, but its value has not been fully explored in esophageal cancer (ESCA).

Methods

We comprehensively analyzed 33 oncogene expression matrices and clinical data from The Cancer Genome Atlas (TCGA) and identified ESCA prognostic-related key eRNAs by Kaplan-Meier and co-expression analysis. We also investigated the prognostic role of the key eRNA using a series of bioinformatics approaches, including immune infiltration, immune function, immune subtypes, and the tumor microenvironment. Finally, the tumor immune dysfunction and exclusion (TIDE) score was used to predict the immune response to immune checkpoint blockade (ICB) therapy.

Results

We identified eRNA AC005515.1, AC012368.1, AP003469.2, Clorf61, and WDFY3-AS2 were associated with the prognosis of ESCA. AC005515.1 is a critical prognostic-related eRNA in ESCA and was significantly co-expressed with immune checkpoint genes (CTLA4, CD274, etc.). In the pan-cancer analysis, AC005515.1 was also associated with the prognosis of seven cancers, including kidney renal papillary cell carcinoma and low-grade brain glioma. It was also found to be co-expressed with immune checkpoint genes in these tumors. Moreover, high expression of AC005515.1 was associated with CD8+ T cells and M1 macrophages infiltration, and the AC005515.1 high-expression group had a higher TIDE score in ESCA.

Conclusions

Overall, eRNA AC005515.1 is associated with the local immune environment of ESCA and may become a new biomarker of ESCA prognosis and immunotherapy response.

Clinical Significance and Immune Infiltration Analyses of a Novel Nerve-Related lncRNA Signature in Gastric Cancer

Gastric cancer (GC) is a progressive disease with high morbidity and mortality. Accumulating evidence indicated that nervous system-cancer crosstalk can affect the occurrence and progression of GC. However, the role of nerve-related lncRNAs (NRLs) in GC remains largely unexplored. In this study, a total of 441 nerve-related genes were collected from the KEGG database, and two approaches, unsupervised clustering and WGCNA, were employed to identify NRLs. Lasso regression analysis was then used to construct the nerve-related lncRNA signature (NRLS). Based on the expression profiles of 5 lncRNAs, we developed a stable NRLS to predict survival in GC patients, and survival analyses showed significantly shorter overall survival (OS) in patients with high NRLS. In addition, the NRLS was found to be positively correlated with immune characteristics, including tumor-infiltrating immune cells, immune modulators, cytokines and chemokines. We then analyzed the role of NRLS in predicting chemotherapy and immunotherapy responses, and constructed the OS nomogram combining NRLS and other clinical features. In conclusion, we constructed a robust NRLS model to stratify GC patients and predict the outcomes of chemotherapy and immunotherapy. This study can provide a new perspective for future individualized treatment of GC.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma

Background

Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC.

Methods

Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments.

Results

By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs.

Conclusions

In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

Clinical significance and immune landscape of cuproptosis-related lncRNAs in kidney renal clear cell carcinoma: a bioinformatical analysis

Background

Kidney renal clear cell carcinoma (KIRC) is considered an immunogenic tumor. Cuproptosis is a newly identified copper-induced regulated cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) have emerged as significant players in tumorigenesis and metastasis. However, there is a huge knowledge gap on the prognostic role of cuproptosis-related lncRNAs in KIRC. And, the clinical value of them is still unknown. Here, we aimed to develop a cuproptosis-related lncRNA prognostic signature in KIRC.

Methods

The messenger RNA (mRNA)/lncRNA expression profiles and the clinical information including age, gender, tumor stage, grade, and overall survival (OS) were acquired from The Cancer Genome Atlas (TCGA) database. The included KIRC samples were further randomly assigned into training (n=258) or testing (n=257) data sets. We performed Pearson correlation analysis to identify the cuproptosis-related lncRNAs and then constructed the prognostic signature using Cox regression analysis and LASSO algorithm. Subsequently, Kaplan-Meier survival analysis, a nomogram, and receiver operating characteristic (ROC) curve were performed to assess the predictive performance of the signature. Moreover, the immune characteristics and drug sensitivity related to the signature were also explored.

Results

The signature comprised 7 cuproptosis-related lncRNAs. The patients with a low-risk score had superior OS compared with those with a high-risk score. The survival rates of the high- and low-risk groups were 44.96% and 83.72% (P<0.001). The area under the curve (AUC) value for 1-, 3-, 5-year survival rate reached 0.814, 0.762 and 0.825, respectively. In addition, a nomogram was also generated; the AUC was 0.785 for risk score, higher than that for age (0.593), gender (0.489), grade (0.679), and stage (0.721). The high-risk group had more enriched immune- and tumor-related genes. Patients with low-risk scores were more sensitive to immunotherapy and the small molecular drugs GSK1904529A, tipifarnib, BX-912, FR-180204, and GSK1070916. Meanwhile, the high-risk group tended to be more sensitive to pyrimethamine, MS-275, and CGP-60474.

Conclusions

Collectively, we constructed a cuproptosis-related lncRNA prognostic signature with a higher predictive accuracy compared to multiple clinicopathological parameters, which may provide vital guidance for therapeutic strategies in KIRC. Combination of more prognostic biomarkers may further improve the accuracy.

Emerging Role of Enhancer RNAs as Potential Diagnostic and Prognostic Biomarkers in Cancer

Enhancers are distal cis-acting elements that are commonly recognized to regulate gene expression via cooperation with promoters. Along with regulating gene expression, enhancers can be transcribed and generate a class of non-coding RNAs called enhancer RNAs (eRNAs). The current discovery of abundant tissue-specific transcription of enhancers in various diseases such as cancers raises questions about the potential role of eRNAs in disease diagnosis and therapy. This review aimed to demonstrate the current understanding of eRNAs in cancer research with a focus on the potential roles of eRNAs as prognostic and diagnostic biomarkers in cancers.

Comprehensive transcriptomic analysis of immune-related eRNAs associated with prognosis and immune microenvironment in melanoma

Background

Recent evidence suggests that enhancer RNAs (eRNAs) play key roles in cancers. Identification of immune-related eRNAs (ireRNAs) in melanoma can provide novel insights into the mechanisms underlying its genesis and progression, along with potential therapeutic targets.

Aim

To establish an ireRNA-related prognostic signature for melanoma and identify potential drug candidates.

Methods

The ireRNAs associated with the overall survival (OS-ireRNAs) of melanoma patients were screened using data from The Cancer Genome Atlas (TCGA) via WGCNA and univariate Cox analysis. A prognostic signature based on these OS-ireRNAs was then constructed by performing the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The immune landscape associated with the prognostic model was evaluated by the ESTIMATE algorithm and CIBERSORT method. Finally, the potential drug candidates for melanoma were screened through the cMap database.

Results

A total of 24 OS-ireRNAs were obtained, of which 7 ireRNAs were used to construct a prognostic signature. The ireRNAs-related signature performed well in predicting the overall survival (OS) of melanoma patients. The risk score of the established signature was further verified as an independent risk factor, and was associated with the unique tumor microenvironment in melanoma. We also identified several potential anti-cancer drugs for melanoma, of which corticosterone ranked first.

Conclusions

The ireRNA-related signature is an effective prognostic predictor and provides reliable information to better understand the mechanism of ireRNAs in the progression of melanoma.