Comprehensive Analysis of Acetylation-Related lncRNAs and Identified AC099850.3 as Prognostic Biomarker in Non-Small Cell Lung Cancer

Identification and validation of non-coding RNA-mediated high expression of IQGAP3 in poor prognosis of lung adenocarcinoma

BACKGROUND

The primary reason for tumor-related deaths worldwide is lung adenocarcinoma (LUAD). The oncogene IQ motif-containing GTPase activating protein 3 (IQGAP3) is crucial for contributing to tumor initiation and progression. However, the precise function and molecular mechanism of IQGAP3 in LUAD remain unknown. The present study aimed to investigate the expression, prognosis, mechanism and tumor immunity associated with IQGAP3 in LUAD.

METHODS

The relationship between IQGAP3 and the poor prognosis of LUAD was analyzed using The Cancer Genome Atlas (TCGA) database. This analysis was further validated on lung cancer tissues and cell lines. The function of IQGAP3 was investigated by silencing it in LUAD cell lines. To predict microRNA (miRNA) and long non-coding RNA associated with IQGAP3, the starBase database was utilized, and the predictions were verified by enhancing the function of miRNA. Finally, the relationship between IQGAP3 and tumor immunity was evaluated using Spearman's correlation analysis.

RESULTS

TCGA database revealed that higher levels of IQGAP3 were associated with advanced tumor stage, N stage and poor prognosis in LUAD patients. To confirm that, we conducted experiments on lung cancer tissues and cell lines and found that silencing IQGAP3 significantly inhibited tumor cell proliferation and migration. The expression of IQGAP3 showed a negative correlation with has-miR-101-3p and has-miR-135a-5p, whereas it showed a positive correlation with GSEC, AC005034.3 and TYMSOS. Furthermore, the introduction of miRNA-mimics into lung cancer cell resulted in a significant inhibition of cancer cell growth and migration. Following that, the level of IQGAP3 showed a positive correlation with the infiltration of immune cells in tumors.

CONCLUSIONS

These results reveal that IQGAP3 significantly promotes LUAD progression and could serve as a prognostic biomarker for LUAD. Furthermore, IQGAP3 is most likely regulated by the GSEC/TYMSOS-hsa-miR-101-3p axis and the AC005034.3-hsa-miR-135a-5p axis in LUAD.

Long non-coding RNA AC099850.4 correlates with advanced disease state and predicts worse prognosis in triple-negative breast cancer

Our understanding of the function of long non-coding RNAs (lncRNAs) in health and disease states has evolved over the past decades due to the many advances in genome research. In the current study, we characterized the lncRNA transcriptome enriched in triple-negative breast cancer (TNBC, n = 42) and estrogen receptor (ER+, n = 42) breast cancer compared to normal breast tissue (n = 56). Given the aggressive nature of TNBC, our data revealed selective enrichment of 57 lncRNAs in TNBC. Among those, AC099850.4 lncRNA was chosen for further investigation where it exhibited elevated expression, which was further confirmed in a second TNBC cohort (n = 360) where its expression correlated with a worse prognosis. Network analysis of AC099850.4high TNBC highlighted enrichment in functional categories indicative of cell cycle activation and mitosis. Ingenuity pathway analysis on the differentially expressed genes in AC099850.4high TNBC revealed the activation of the canonical kinetochore metaphase signaling pathway, pyridoxal 5'-phosphate salvage pathway, and salvage pathways of pyrimidine ribonucleotides. Additionally, upstream regulator analysis predicted the activation of several upstream regulator networks including CKAP2L, FOXM1, RABL6, PCLAF, and MITF, while upstream regulator networks of TP53, NUPR1, TRPS1, and CDKN1A were suppressed. Interestingly, elevated expression of AC099850.4 correlated with worse short-term relapse-free survival (log-rank p = 0.01). Taken together, our data are the first to reveal AC099850.4 as an unfavorable prognostic marker in TNBC, associated with more aggressive clinicopathological features, and suggest its potential utilization as a prognostic biomarker and therapeutic target in TNBC.

Epigenetic regulation of epithelial-mesenchymal transition during cancer development

Epithelial-mesenchymal transition (EMT) plays essential roles in promoting malignant transformation of epithelial cells, leading to cancer progression and metastasis. During EMT-induced cancer development, a wide variety of genes are dramatically modified, especially down-regulation of epithelial-related genes and up-regulation of mesenchymal-related genes. Expression of other EMT-related genes is also modified during the carcinogenic process. Especially, epigenetic modifications are observed in the EMT-related genes, indicating their involvement in cancer development. Mechanically, epigenetic modifications of histone, DNA, mRNA and non-coding RNA stably change the EMT-related gene expression at transcription and translation levels. Herein, we summarize current knowledge on epigenetic regulatory mechanisms observed in EMT process relate to cancer development in humans. The better understanding of epigenetic regulation of EMT during cancer development may lead to improvement of drug design and preventive strategies in cancer therapy.

Acetyl-CoA: An interplay between metabolism and epigenetics in cancer

Due to its high mortality and severe economic burden, cancer has become one of the most difficult medical problems to solve today. As a key node in metabolism and the main producer of energy, acetyl-coenzyme A (acetyl-CoA) plays an important role in the invasion and migration of cancer. In this review, we discuss metabolic pathways involving acetyl-CoA, the targeted therapy of cancer through acetyl-CoA metabolic pathways and the roles of epigenetic modifications in cancer. In particular, we emphasize that the metabolic pathway of acetyl-CoA exerts a great impact in cancer; this process is very different from normal cells due to the "Warburg effect". The concentration of acetyl-CoA is increased in the mitochondria of cancer cells to provide ATP for survival, hindering the growth of normal cells. Therefore, it may be possible to explore new feasible and more effective treatments through the acetyl-CoA metabolic pathway. In addition, a growing number of studies have shown that abnormal epigenetic modifications have been shown to play contributing roles in cancer formation and development. In most cancers, acetyl-CoA mediated acetylation promotes the growth of cancer cells. Thus, acetylation biomarkers can also be detected and serve as potential cancer prediction and prognostic markers.

Potential prognostic value of a eight ferroptosis-related lncRNAs model and the correlative immune activity in oral squamous cell carcinoma

Background

To investigate the prognostic value of ferroptosis-related long noncoding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC) and to construct a prognostic risk and immune activity model.

Methods

We obtained clinical and RNA-seq information on OSCC patient data in The Cancer Genome Atlas (TCGA) Genome Data Sharing (GDC) portal. Through a combination of a differential analysis, Pearson correlation analysis and Cox regression analysis, ferroptosis-related lncRNAs were identified, and a prognostic model was established based on these ferroptosis-related lncRNAs. The accuracy of the model was evaluated via analyses based on survival curves, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA). Univariate Cox and multivariate Cox regression analyses were performed to evaluate independent prognostic factors. Then, the infiltration and functional enrichment of immune cells in high- and low-risk groups were compared. Finally, certain small-molecule drugs that potentially target OSCC were predicted via use of the L1000FWD database.

Results

The prognostic model included 8 ferroptosis-related lncRNAs (FIRRE, LINC01305, AC099850.3, AL512274.1, AC090246.1, MIAT, AC079921.2 and LINC00524). The area under the ROC curve (AUC) was 0.726. The DCA revealed that the risk score based on the prognostic model was a better prognostic indicator than other clinical indicators. The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OSCC. There were differences in immune cell infiltration, immune functions, m6A-related gene expression levels, and signal pathway enrichment between the high- and low-risk groups. Subsequently, several small-molecule drugs were predicted for use against differentially expressed ferroptosis-related genes in OSCC.

Conclusions

We constructed a new prognostic model of OSCC based on ferroptosis-related lncRNAs. The model is valuable for prognostic prediction and immune evaluation, laying a foundation for the study of ferroptosis-related lncRNAs in OSCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01097-z.

Comprehensive analysis of the prognostic signature and tumor microenvironment infiltration characteristics of cuproptosis-related lncRNAs for patients with colon adenocarcinoma

Background

Cuproptosis, a newly described method of regulatory cell death (RCD), may be a viable new therapy option for cancers. Long noncoding RNAs (lncRNAs) have been confirmed to be correlated with epigenetic controllers and regulate histone protein modification or DNA methylation during gene transcription. The roles of cuproptosis-related lncRNAs (CRLs) in Colon adenocarcinoma (COAD), however, remain unknown.

Methods

COAD transcriptome data was obtained from the TCGA database. Thirteen genes associated to cuproptosis were identified in published papers. Following that, correlation analysis was used to identify CRLs. The cuproptosis associated prognostic signature was built and evaluated using Lasso regression and COX regression analysis. A prognostic signature comprising six CRLs was established and the expression patterns of these CRLs were analyzed by qRT-PCR. To assess the clinical utility of prognostic signature, we performed tumor microenvironment (TME) analysis, mutation analysis, nomogram generation, and medication sensitivity analysis.

Results

We identified 49 prognosis-related CRLs in COAD and constructed a prognostic signature consisting of six CRLs. Each patient can be calculated for a risk score and the calculation formula is: Risk score =TNFRSF10A-AS1 * (-0.2449) + AC006449.3 * 1.407 + AC093382.1 *1.812 + AC099850.3 * (-0.0899) + ZEB1-AS1 * 0.4332 + NIFK-AS1 * 0.3956. Six CRLs expressions were investigated by qRT-PCR in three colorectal cancer cell lines. In three cohorts, COAD patients were identified with different risk groups, with the high-risk group having a worse prognosis than the low-risk group. Furthermore, there were differences in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups. We also identified certain drugs that were more sensitive to the high-risk group: Paclitaxel, Vinblastine, Sunitinib and Elescloml.

Conclusions

Our findings may be used to further investigate RCD, comprehension of the prognosis and tumor microenvironment infiltration characteristics in COAD.

Molecular subtypes, clinical significance, and tumor immune landscape of angiogenesis-related genes in ovarian cancer

Angiogenesis is a physiological process, where new blood vessels are formed from pre-existing vessels through the mechanism called sprouting. It plays a significant role in supporting tumor growth and is expected to provide novel therapeutic ideas for treating tumors that are resistant to conventional therapies. We investigated the expression pattern of angiogenesis-related genes (ARGs) in ovarian cancer (OV) from public databases, in which the patients could be classified into two differential ARG clusters. It was observed that patients in ARGcluster B would have a better prognosis but lower immune cell infiltration levels in the tumor microenvironment. Then ARG score was computed based on differentially expressed genes via cox analysis, which exhibited a strong correlation to copy number variation, immunophenoscore, tumor mutation load, and chemosensitivity. In addition, according to the median risk score, patients were separated into two risk subgroups, of which the low-risk group had a better prognosis, increased immunogenicity, and stronger immunotherapy efficacy. Furthermore, we constructed a prognostic nomogram and demonstrated its predictive value. These findings help us better understand the role of ARGs in OV and offer new perspectives for clinical prognosis and personalized treatment.

Construction and Validation of a Necroptosis-Related lncRNA Signature in Prognosis and Immune Microenvironment for Glioma

Background

Glioma is the most common primary brain tumor, representing approximately 80.8% of malignant tumors. Necroptosis triggers and enhances antitumor immunity and is expected to be a new target for tumor immunotherapy. The effectiveness of necroptosis-related lncRNAs as potential therapeutic targets for glioma has not been elucidated.

Methods

We acquired RNA-seq data sets from LGG and GBM samples, and the corresponding clinical characteristic information is from TCGA. Normal brain tissue data is from GTEX. Based on TCGA and GTEx, we used univariate Cox regression to sort out survival-related lncRNAs. Lasso regression models were then built. Then, we performed a separate Kaplan-Meier analysis of the lncRNAs used for modeling. We validated different risk groups via OS, DFS, enrichment analysis, comprehensive immune analysis, and drug sensitivity.

Results

We constructed a 12 prognostic lncRNAs model after bioinformatic analysis. Subsequently, the risk score of every glioma patient was calculated based on correlation coefficients and expression levels, and the patients were split into low- and high-risk groups according to the median value of the risk score. A nomogram was established for every glioma patient to predict prognosis. Besides, we found significant differences in OS, DFS, immune infiltration and checkpoints, and immune therapy between different risk subgroups.

Conclusion

Predictive models of 12 necroptosis-related lncRNAs can facilitate the assessment of the prognosis and molecular characteristics of glioma patients and improve treatment modalities.

Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of head and neck squamous cell carcinoma

Background

Previous studies have determined that necroptosis‐related genes are potential biomarkers in head and neck squamous cell carcinoma (HNSCC). Herein, we established a novel risk model based on necroptosis‐related lncRNAs (nrlncRNAs) to predict the prognosis of HNSCC patients.

Methods

Transcriptome and related information were obtained from TCGA database, and an nrlncRNA signature was established based on univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. Kaplan–Meier analysis and time‐dependent receiver operating characteristic (ROC) analysis were used to evaluate the model, and a nomogram for survival prediction was established. Gene set enrichment analysis, immune analysis, drug sensitivity analysis, correlation with N6‐methylandenosin (m6A), and tumor stemness analysis were performed. Furthermore, the entire set was divided into two clusters for further discussion.

Results

A novel signature was established with six nrlncRNAs. The areas under the ROC curves (AUCs) for 1‐, 3‐, and 5‐year overall survival (OS) were 0.699, 0.686, and 0.645, respectively. Patients in low‐risk group and cluster 2 had a better prognosis, more immune cell infiltration, higher immune function activity, and higher immune scores; however, patients in high‐risk group and cluster 1 were more sensitive to chemotherapy. Moreover, the risk score had negative correlation with m6A‐related gene expression and tumor stemness.

Conclusion

According to this study, we constructed a novel signature with nrlncRNA pairs to predict the survival of HNSCC patients and guide immunotherapy and chemotherapy. This may possibly promote the development of individualized and precise treatment for HNSCC patients.

A Novel Necroptosis-Related lncRNA Signature Predicts the Prognosis of Lung Adenocarcinoma

Background: Necroptosis is closely related to the tumorigenesis and development of cancer. An increasing number of studies have demonstrated that targeting necroptosis could be a novel treatment strategy for cancer. However, the predictive potential of necroptosis-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) still needs to be clarified. This study aimed to construct a prognostic signature based on necroptosis-related lncRNAs to predict the prognosis of LUAD.

Methods: We downloaded RNA sequencing data from The Cancer Genome Atlas database. Co-expression network analysis, univariate Cox regression, and least absolute shrinkage and selection operator were adopted to identify necroptosis-related prognostic lncRNAs. We constructed the predictive signature by multivariate Cox regression. Kaplan–Meier analysis, time-dependent receiver operating characteristics, nomogram, and calibration curves were used to validate and evaluate the signature. Subsequently, we used gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between the predictive signature and tumor immune microenvironment of risk groups. Finally, the correlation between the predictive signature and immune checkpoint expression of LUAD patients was also analyzed.

Results: We constructed a signature composed of 7 necroptosis-related lncRNAs (AC026355.2, AC099850.3, AF131215.5, UST-AS2, ARHGAP26-AS1, FAM83A-AS1, and AC010999.2). The signature could serve as an independent predictor for LUAD patients. Compared with clinicopathological variables, the necroptosis-related lncRNA signature has a higher diagnostic efficiency, with the area under the receiver operating characteristic curve being 0.723. Meanwhile, when patients were stratified according to different clinicopathological variables, the overall survival of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the low-risk group. ssGSEA further confirmed that the predictive signature was significantly related to the immune status of LUAD patients. The immune checkpoint analysis displayed that low-risk patients had a higher immune checkpoint expression, such as CTLA-4, HAVCR2, PD-1, and TIGIT. This suggested that immunological function is more active in the low-risk group LUAD patients who might benefit from checkpoint blockade immunotherapies.

Conclusion: The predictive signature can independently predict the prognosis of LUAD, helps elucidate the mechanism of necroptosis-related lncRNAs in LUAD, and provides immunotherapy guidance for patients with LUAD.

Identification of the Ferroptosis-Related Long Non-Coding RNAs Signature to Improve the Prognosis Prediction in Papillary Renal Cell Carcinoma

Papillary renal cell carcinoma (pRCC) is one of the epithelial renal cell carcinoma (RCC) histological subtypes. Ferroptosis is a new iron-dependent form of cell death that has been seen in a variety of clinical situations. Using differentially expressed ferroptosis-related long non-coding RNAs (lncRNAs) from patients with pRCC in The Cancer Genome Atlas; we built a prognostic lncRNA-based signature. We discovered seven different lncRNAs that were strongly linked to the prognosis of patients with pRCC. High-risk scores were linked to a poor prognosis for pRCC, which was confirmed by the findings of Kaplan–Meier studies. In addition, the constructed lncRNA signature has a 1-year area under the curve (AUC) of 0.908, suggesting that it has a high predictive value in pRCC. In the high-risk group, Gene set enrichment analyses (GSEA) analysis identified immunological and tumor-related pathways. Furthermore, single-sample GSEA (ssGSEA) revealed significant differences in T cell functions checkpoint, antigen presenting cell (APC) co-stimulation, inflammation promoting, and para inflammation between the two groups with different risk scores. In addition, immune checkpoints like PDCD1LG2 (PD-L2), LAG3, and IDO1 were expressed differently in the two risk groups. In summary, a novel signature based on ferroptosis-related lncRNAs could be applied in predicting the prognosis of patients with pRCC.

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.

Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.

Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.

Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.