A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis

The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles of OA cartilage GSE114007 and GSE57218 were obtained from the Gene Expression Omnibus. And the limma method was used to detect differentially expressed genes (DEGs). Second, the biological functions of the DEGs in OA were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Wayne plots were employed to visualize the differentially expressed mitochondrial genes (MDEGs) in OA. Subsequently, the LASSO and SVM-RFE algorithms were employed to elucidate potential OA signature genes within the set of MDEGs. As a result, GRPEL and MTFP1 were identified as signature genes. Notably, GRPEL1 exhibited low expression levels in OA samples from both experimental and test group datasets, demonstrating high diagnostic efficacy. Furthermore, RT-qPCR analysis confirmed the reduced expression of Grpel1 in an in vitro OA model. Lastly, ssGSEA analysis revealed alterations in the infiltration abundance of several immune cells in OA cartilage tissue, which exhibited correlation with GRPEL1 expression. Altogether, this study has revealed that GRPEL1 functions as a novel and significant diagnostic indicator for OA by employing two machine learning methodologies. Furthermore, these findings provide fresh perspectives on potential targeted therapeutic interventions in the future.

A methylation-related lncRNA-based prediction model in lung adenocarcinomas

BACKGROUND

The collaboration between methylation and the lung adenocarcinoma (LUAD) occurrence and development is closes. Long noncoding RNA (lncRNA), as a regulatory factor of various biological functions, can be used for cancer diagnosis. Our study aimed to construct a robust methylation-related lncRNA signature of LUAD.

METHODS

In the Cancer Genome Atlas (TCGA) dataset, we download the RNA expression data and clinical information of LUAD cases. To develop the best prognostic signature based on methylation-related lncRNAs, Cox regression analyses were utilized. Using Kaplan-Meier analysis, overall survival rates were compared between risk category included both low- and high-risk patients. To categorize genes according to their functional significance, GSEA (Subramanian et al, 2005) was used. Single-sample gene set enrichment analysis (ssGSEA) was used to further reveal the potential molecular mechanism of the methylation-related lncRNA prognostic model in immune infiltration. Using TRLnc (http://www.licpathway.net/TRlnc) and lncRNASNP to analyse the SNP sites and TRLnc of these 18 lncRNAs. LncSEA website was used to analyse 18 lncRNA in the process of tumour development and development. Go was used to analyse the enriched pathways enriched by TFs (transcription factors), Cerna networks, and proteins bound to each other of these 18 lncRNAs. The 'prophetic' package was used to analyse the value of this prognostic model in guiding personalized immunotherapy.

RESULTS

In this study, we identified 18 methylation-related lncRNAs (AP002761.1, AL118558.3, CH17-340M24.3, AL353150.1, AC004687.1, LINC00996, AF186192.1, HSPC324, AC087752.3, FAM30A, AC106047.1, AC026355.1, ABALON, LINC01843, AL606489.1, NKILA, AP001453.2, GSEC) to establish a methylation-related lncRNA signature that can detect patients prognosis in LUAD. The enriched pathways enriched by proteins interacting with 18 lncRNAs are mainly EMT, hypoxia, stemness and proliferation, among which LINC00996 and AF186192.1 are regulated by multiple tumour associated transcription factors, such as TP53 and TP63, and fam30a and mRNA form a Cerna network. There are 2319 SNP loci in LINC00996, 36 of which are risk SNP loci and 205 SNP loci in af186192.1; AF186192.1 affects 95 conserved miRNAs and 123 non-conserved miRNAs, promotes the binding of 149 pairs of miRNAs: lncRNAs and inhibits the binding of 95 pairs of miRNAs: lncRNAs. The ROC curve demonstrated that the established methylation-related lncRNA signature was more effective in predicting the prognosis of patients in LUAD than the clinicopathological parameters. Our research has confirmed that patients in the high-risk group which was separated by the risk score model based on methylation-related lncRNA had shorter OS. According to GSEA, the high-risk group had a predominantly tumour- and immune-related pathway enrichment. A significant association was shown by ssGSEA between predictive signature and immune status in LUAD patients. In addition, principal component analysis (PCA) demonstrated the prognostic and predictive value of our signature. The correlation between the predictive signature of methylation-related lncRNA and IC50 of conventional chemotherapy drugs can provide personalized chemotherapy regimens for LUAD patients. Methylation-related lncRNA signature can effectively predict DFS of patients in LUAD.

A New Ferroptosis-Related Long Non-Coding RNA Risk Model Predicts the Prognosis of Patients With Papillary Thyroid Cancer

Background

Ferroptosis is a novel form of regulated cell death that involves in cancer progression. However, the role of ferroptosis-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC) remains to be elucidated. The purpose of this paper was to clarify the prognostic value of ferroptosis-related lncRNAs in PTC.

Methods

The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between ferroptosis-related genes (FRGs) and lncRNA was determined using Pearson correlation analysis. Multivariate Cox regression model (P < 0.01) was performed to establish a ferroptosis-related lncRNAs risk model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, risk curve and nomograms were then performed to assess the accuracy and clinical applicability of prognostic models. The correlations between the prognosis model and clinicopathological variables, immune and m6A were analyzed. Finally, in vitro assays were performed to verify the role of LINC00900, LINC01614 and PARAL1 on the proliferation, migration and invasion in TPC-1 and BCPAP cells, as well as the relationship between three lncRNAs and ferroptosis.

Results

A five-ferroptosis-related lncRNAs (PARAL1, LINC00900, DPH6-DT, LINC01614, LPP-AS2) risk model was constructed. Based on the risk score, samples were divided into the high- and low-risk groups. Patients in the low-risk group had better prognosis than those in high-risk group. Compared to traditional clinicopathological features, risk score was more accurate in predicting prognosis in patients with PTC. Additionally, the difference of immune cell, function and checkpoints was observed between two groups. Moreover, experiments showed that LINC00900 promoted the proliferation, migration and invasion in TPC-1 and BCPAP cells, while LINC01614 and PARAL1 revealed opposite effects, all of which were related to ferroptosis.

Conclusions

In summary, we identified a five-ferroptosis-related lncRNAs risk model to predict the prognosis of PTC. Furthermore, our study also revealed that LINC00900 functioned as a tumor suppressor lncRNA, LINC01614 and PARAL1 as an oncogenic lncRNA in PTC.

Ferroptosis-related lncRNAs: Distinguishing heterogeneity of the tumour microenvironment and predicting immunotherapy response in bladder cancer

Ferroptosis, a cell death pathway dependent on iron, has been shown in research to play a role in the development, advancement, and outlook of tumours through ferroptosis-related lncRNAs (FRLRs). However, the value of the FRLRs in bladder cancer (BLCA) has not been thoroughly investigated. This research project involved developing a predictive model using ten specific FRLRs (AC099850.4, AL731567.1, AL133415.1, AC021321.1, SPAG5-AS1, HMGA2-AS1, RBMS3-AS3, AC006160.1, AL583785.1, and AL662844.4) through univariate COX and LASSO regression techniques. The validation of this signature as a standalone predictor was confirmed in a group of 65 patients from the urology bladder tumour database at the First Affiliated Hospital of Wenzhou Medical University in Wenzhou, China. Patients were categorized based on their median risk score into either a low-risk group or a high-risk group. Enrichment analysis identified possible molecular mechanisms that could explain the variations in clinical outcomes observed in high-risk and low-risk groups. Moreover, we explored the correlation between FLPS and immunotherapy-related indicators. The ability of FLPS to forecast the effectiveness of immunotherapy was validated by the elevated levels of immune checkpoint genes (PD-L1, CTLA4, and PD-1) in the group at high risk. We also screened the crucial FRLR (HMGA2-AS1) through congruent expression and prognostic conditions and established a ceRNA network, indicating that HMGA2-AS1 may affect epithelial-mesenchymal transition by modulating the Wnt signalling pathway through the ceRNA mechanism. We identified the top five mRNAs (NFIB, NEGR1, JAZF1, JCAD, and ESM1) based on random forest algorithm and analysed the relationship between HMGA2-AS1, the top five mRNAs, and immunotherapy, and their interactions with drug sensitivities. Our results suggest that patients with BLCA have a greater sensitivity to four drugs (dasatinib, pazopanib, erismodegib and olaparib). Our study provides new insights into the TME, key signalling pathways, genome, and potential therapeutic targets of BLCA, with future guidance for immunotherapy and targeted precision drugs.

Identification of cuproptosis-related long non-coding RNA and construction of a novel prognostic signature for bladder cancer: An observational study

Bladder Urothelial Carcinoma (BLCA), a prevalent and lethal cancer, lacks understanding regarding the roles and prognostic value of cuproptosis-related lncRNAs (CRLs), a novel form of cell death induced by copper. We collected RNA-seq data, clinical information, and prognostic data for 414 BLCA samples and 19 matched controls from The Cancer Genome Atlas. Using multivariate and univariate Cox regression analyses, we identified CRLs to create a prognostic signature. Patients were then divided into low- and high-risk groups based on their risk scores. We analyzed overall survival using the Kaplan-Meier method, evaluated stromal and immune scores, and explored functional differences between these risk groups with gene set enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also conducted to understand the links between CRLs and BLCA development. We developed a prognostic signature using 4 independent CRLs: RC3H1-IT1, SPAG5-AS1, FAM13A-AS1, and GNG12-AS1. This signature independently predicted the prognosis of BLCA patients. High-risk patients had worse outcomes, with gene set enrichment analysis revealing enrichment in tumor- and immune-related pathways in the high-risk group. Notably, high-risk patients exhibited enhanced responses to immunotherapy and conventional chemotherapy drugs like sunitinib, paclitaxel, and gemcitabine. The independent prognostic signature variables RC3H1-IT1, SPAG5-AS1, FAM13A-AS1, and GNG12-AS1 predicted the prognoses of BLCA patients and provided a basis for the study of the mechanism of CRLs in BLCA development and progression, and the guidance of clinical treatments for patients with BLCA.

Autophagy flux in bladder cancer: Cell death crosstalk, drug and nanotherapeutics

Autophagy, a self-digestion mechanism, has emerged as a promising target in the realm of cancer therapy, particularly in bladder cancer (BCa), a urological malignancy characterized by dysregulated biological processes contributing to its progression. This highly conserved catabolic mechanism exhibits aberrant activation in pathological events, prominently featured in human cancers. The nuanced role of autophagy in cancer has been unveiled as a double-edged sword, capable of functioning as both a pro-survival and pro-death mechanism in a context-dependent manner. In BCa, dysregulation of autophagy intertwines with cell death mechanisms, wherein pro-survival autophagy impedes apoptosis and ferroptosis, while pro-death autophagy diminishes tumor cell survival. The impact of autophagy on BCa progression is multifaceted, influencing metastasis rates and engaging with the epithelial-mesenchymal transition (EMT) mechanism. Pharmacological modulation of autophagy emerges as a viable strategy to impede BCa progression and augment cell death. Notably, the introduction of nanoparticles for targeted autophagy regulation holds promise as an innovative approach in BCa suppression. This review underscores the intricate interplay of autophagy with cell death pathways and its therapeutic implications in the nuanced landscape of bladder cancer.

Multiple programmed cell death patterns and immune landscapes in bladder cancer: Evidence based on machine learning and multi-cohorts

BACKGROUND

Bladder cancer (BLCA) is the most prevalent malignant neoplasm of the urinary tract, and ranks seventh as the most frequent systemic neoplasm in males. Dysregulation of programmed cell death (PCD) has been implicated in various stages of cancer progression, including tumorigenesis, invasion, and metastasis. However, the correlation between multiple PCD modes and BLCA is lacking. Thus, a risk prediction model was built based on 12 models of PCD to predict prognosis and immunotherapy response in patients with BLCA.

METHODS

The RNA sequencing transcriptome data of BLCA were collected from the Cancer Genome Atlas Program (TCGA) and GEO datasets. Univariate Cox and LASSO regression analyzes were performed to identify PCD-related genes (PCDRGs) significant for prognosis. Multivariate Cox regression analysis was used to develop a prognostic model for PCD. Survival analysis and chi-squared test were employed to analyze the survival variations between different risk groups. Univariate and multivariate Cox analyses were performed to evaluate the model as an independent prognostic predictor. A nomogram was formulated using both clinical data and the model to predict the survival rates of BLCA patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed to analyze and elucidate the molecular mechanisms and pathways operating within different risk score groups. Furthermore, the immune landscape was investigated and the efficacy of various anti-tumor drugs was evaluated for BLCA. Finally, consensus clustering analysis was adopted to explore the association between different PCD clusters and clinical characteristics.

RESULTS

Assessment of the public datasets and multivariate Cox analysis yielded 1254 PCDRGs, of which 10 PCDRGs for BLCA were identified. Based on the PCDRGs, a prognostic model was built for BLCA patient prognosis. Compared with the low-risk group, the high-risk group had a poorer prognosis. The model predicted area under the curve (AUC) values of 0.751, 0.753, and 0.763, respectively, for 1-, 3-, and 5-year survival of BLCA patients. The nomogram further demonstrated the credibility of the prognosis model. The low-risk group patients exhibited lower TIDE scores and higher TMB scores, implying better response of the low-risk group to immunotherapy. The consensus clustering analysis indicated that compared with PCD cluster A, PCD cluster B was significantly more expressed in PCDRGs, suggesting a closer relation of PCD cluster B to PCDRGs. Patients in PCD cluster B had lower risk scores.

CONCLUSION

To summarize, the effects of 12 PCD patterns on BLCA were synthesized and the correlation between PCD and BLCA was explored. These findings provide new and convincing evidence for individualized treatment of BLCA, and help guide the treatment strategy and improve the prognosis of BLCA patients.

miRNA/epithelial-mesenchymal axis (EMT) axis as a key player in cancer progression and metastasis: A focus on gastric and bladder cancers

The metastasis a major hallmark of tumors that its significant is not only related to the basic research, but clinical investigations have revealed that majority of cancer deaths are due to the metastasis. The metastasis of tumor cells is significantly increased due to EMT mechanism and therefore, inhibition of EMT can reduce biological behaviors of tumor cells and improve the survival rate of patients. One of the gaps related to cancer metastasis is lack of specific focus on the EMT regulation in certain types of tumor cells. The gastric and bladder cancers are considered as two main reasons of death among patients in clinical level. Herein, the role of EMT in regulation of their progression is evaluated with a focus on the function of miRNAs. The inhibition/induction of EMT in these cancers and their ability in modulation of EMT-related factors including ZEB1/2 proteins, TGF-β, Snail and cadherin proteins are discussed. Moreover, lncRNAs and circRNAs in crosstalk of miRNA/EMT regulation in these tumors are discussed and final impact on cancer metastasis and response of tumor cells to the chemotherapy is evaluated. Moreover, the impact of miRNAs transferred by exosomes in regulation of EMT in these cancers are discussed.

PI3K/Akt signalling pathway-associated long noncoding RNA signature predicts the prognosis of laryngeal cancer patients

The PI3K/Akt signalling pathway is associated with the occurrence and development of tumours and significantly affects the prognosis of patients. We established a predictive signature based on the PI3K/Akt pathway to predict the prognosis of patients. The RNA-seq and clinical data of laryngeal cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Three lncRNAs (MNX1-AS1, LINC00330, LSAMP-AS1) were selected through univariate, multivariate Cox and log-rank test analysis to establish a prognostic signature. The patients were then divided into high-risk and low-risk groups based on their risk score. In the TCGA training set, the survival time of the high-risk group was shorter than that of the low-risk group (P < 0.01). Follicular helper T cells were lower in the high-risk group (P = 0.022), and CCR, inflammation promotion, parainflammation, and type I IFN immune function were suppressed. The results of the drug sensitivity analysis suggest that the high-risk group is sensitive to AKT inhibitors. The establishment of the signature was also verified based on the clinical data. Three lncRNAs can facilitate the migration, invasion, and vitality of cancer cells in vitro, and vice versa. Moreover, p-AKT (Ser473) and p-PI3K were highly activated in the cells overexpressing the abovementioned three lncRNAs. The PI3K/Akt signalling pathway-associated prognosis signature has a good predictive effect.

Identification of necroptosis-related long non-coding RNAs prognostic signature and the crucial lncRNA in bladder cancer

BACKGROUND

Research on the relationships between long non-coding RNAs (lncRNAs) and cancer is attractive and has progressed very rapidly. Necroptosis-related biomarkers can potentially be used for predicting the prognosis of cancer patients. This study aimed to establish a necroptosis-related lncRNA (NPlncRNA) signature to predict the prognosis of patients with bladder cancer (BCa).

METHODS

First, NPlncRNAs were identified using Pearson correlation analysis and machine learning algorithms, including SVM-RFE, least absolute shrinkage and selection operator (LASSO) regression, and random forest. The prognostic NPlncRNA signature was constructed using univariate and multivariate Cox regression analyses and the diagnostic efficacy and clinically predictive efficiency were evaluated and validated. The biological functions of the signature were analysed using gene set enrichment analysis (GSEA) and functional enrichment analysis. We further integrated the RNA-seq dataset (GSE133624) with our outcomes to reveal the crucial NPlncRNA that was functionally verified by assessing cell viability, proliferation, and apoptosis in BCa cells.

RESULTS

The prognostic NPlncRNAs signature was composed of PTOV1-AS2, AC083862.2, MAFG-DT, AC074117.1, AL049840.3, and AC078778.1, and a risk score based on this signature was proven to be an independent prognostic factor for the BCa patients, indicated by poor overall survival (OS) of patients in the high-risk group. Additionally, the NPlncRNAs signature had a higher diagnostic validity than that of other clinicopathological variables, with a greater area under the receptor operating characteristic and concordance index curves. A nomogram established by integrating clinical variables and risk score confirmed that the signature can accurately predict the OS of patients and has high clinical practicability. Functional enrichment analysis and GSEA revealed that some cancer-related and necroptosis-related pathways were enriched in high-risk groups. The crucial NPlncRNA MAFG-DT was associated with poor prognosis and was highly expressed in BCa cells. MAFG-DT silencing notably inhibited proliferation and enhanced apoptosis of BCa cells.

CONCLUSIONS

A novel prognostic NPlncRNAs signature was identified in BCa in this study, which provides potential therapeutic targets among which MAFG-DT plays critical roles in the tumorigenesis of BCa.

A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer

This study constructed a novel cuproptosis-related lncRNAs signature to predict the prognosis of BLCA patients. The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA-seq data together with the relevant clinical information. The cuproptosis-related genes were first discovered. The cuproptosis-related lncRNAs were then acquired by univariate, the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to create a predictive signature. An eight cuproptosis-related lncRNAs (AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, ARHGAP5-AS1, LINC01106, LINC02446) predictive signature was created. Compared with the low-risk group, the prognosis was poorer for the high-risk group. The signature served as an independent overall survival (OS) predictor. Receiver operating characteristic (ROC) curve indicated that the signature demonstrated superior predictive ability, as evidenced by the area under the curve (AUC) of 0.782 than the clinicopathological variables. When we performed a subgroup analysis of the different variables, the high-risk group's OS for BLCA patients was lower than that of the low-risk group's patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk groups were clearly enriched in many immune-related biological processes and tumor-related signaling pathways. Single sample gene set enrichment analysis (ssGSEA) revealed that the immune infiltration level was different between the two groups. Finally, quantitative RT-PCR showed that AC005261.1, AC021321.1, AL024508.2, LINC02446 and LINC01106 were lowly expressed in tumor cells, while ARHGAP5-AS1 showed the opposite trend. In summary, the predictive signature can independently predict the prognosis and provide clinical treatment guidance for BLCA patients.

Crosstalk between autophagy and bladder transitional cell carcinoma by autophagy-related lncRNAs

The aim of this study was to investigate the crosstalk between autophagy and bladder transitional cell carcinoma (TCC) by autophagy-related long noncoding RNAs (lncRNAs). A total of 400 TCC patients from The Cancer Genome Atlas were enrolled in this study. We identified the autophagy-related lncRNA expression profile of the TCC patients and then constructed a prognostic signature using the least absolute shrinkage and selection operation and Cox regression. Risk, survival, and independent prognostic analyses were carried out. Receiver operating characteristic curve, nomogram, and calibration curves were explored. Gene Set Enrichment Analysis was employed to verify the enhanced autophagy-related functions. Finally, we compared the signature with several other lncRNA-based signatures. A 9-autophagy-related lncRNA signature was established by least absolute shrinkage and selection operation-Cox regression that was significantly associated with overall survival in TCC. Among them, 8 of the 9 lncRNAs were protective factors while the remaining was a risk factor. The risk scores calculated by the signature showed significant prognostic value in survival analysis between the high- or low-risk groups. The 5-year survival rate for the high-risk group was 26.0% while the rate for the low-risk group was 56.0% (P < .05). Risk score was the only significant risk factor in the multivariate Cox regression survival analysis (P < .001). A nomogram connecting this signature with clinicopathologic characteristics was assembled. To assess the performance of the nomogram, a C-index (0.71) was calculated, which showed great convergence with an ideal model. The Gene Set Enrichment Analysis results demonstrated 2 major autophagy-related pathways were significantly enhanced in TCC. And this signature performed a similar predictive effect as other publications. The crosstalk between autophagy and TCC is significant, and this 9 autophagy-related lncRNA signature is a great predictor of TCC.

The epigenetic regulatory mechanisms of ferroptosis and its implications for biological processes and diseases

Ferroptosis is a form of regulated cell death triggered by the iron-dependent peroxidation of phospholipids. Interactions of iron and lipid metabolism factors jointly promote ferroptosis. Ferroptosis has been demonstrated to be involved in the development of various diseases, such as tumors and degenerative diseases (e.g., aortic dissection), and targeting ferroptosis is expected to be an effective strategy for the treatment of these diseases. Recent studies have shown that the regulation of ferroptosis is affected by multiple mechanisms, including genetics, epigenetics, posttranscriptional modifications, and protein posttranslational modifications. Epigenetic changes have garnered considerable attention due to their importance in regulating biological processes and potential druggability. There have been many studies on the epigenetic regulation of ferroptosis, including histone modifications (e.g., histone acetylation and methylation), DNA methylation, and noncoding RNAs (e.g., miRNAs, circRNAs, and lncRNAs). In this review, we summarize recent advances in research on the epigenetic mechanisms involved in ferroptosis, with a description of RNA N6-methyladenosine (m6A) methylation included, and the importance of epigenetic regulation in biological processes and ferroptosis-related diseases, which provides reference for the clinical application of epigenetic regulators in the treatment of related diseases by targeting ferroptosis.

A novel oxidative-stress related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a primary malignant tumour of tubular epithelial origin and is most common in the urinary tract. Growing evidence suggests that oxidative stress (OS), generates high levels of reactive oxygen species (ROS) and free radicals, and plays a critical role in cancer in humans. However, the predictive value of OS-related long non-coding RNAs (lncRNAs) in ccRCC remains unclear. We constructed a predictive signature of survival based on OS-related lncRNAs that were obtained from The Cancer Genome Atlas (TCGA-KIRC), to predict the prognosis of patients with ccRCC. The signature comprised seven lncRNAs: SPART-AS1, AL162586.1, LINC00944, LINC01550, HOXB-AS4, LINC02027, and DOCK9-DT. OS-related signature of lncRNAs had diagnostic efficiency higher than that of clinicopathological variables, with an area of 0.794 under the receiver operating characteristic curve. Additionally, the nomogram based on risk scores and clinicopathological variables (age, gender, grade, stage, M-stage, and N-stage) showed strong predictive performance. Patients with high-risk were found to be more sensitive to the therapeutic drugs ABT.888, AICAR, MS.275, sunitinib, AZD.2281, and GDC.0449. Our constructed the predictive signature can independently predict the prognosis of patients with ccRCC; however, the underlying mechanism needs further investigation.

Regulation of ncRNAs involved with ferroptosis in various cancers

As a special pattern of programmed cell death, ferroptosis is reported to participate in several processes of tumor progression, including regulating proliferation, suppressing apoptotic pathways, increasing metastasis, and acquiring drug resistance. The marked features of ferroptosis are an abnormal intracellular iron metabolism and lipid peroxidation that are pluralistically modulated by ferroptosis-related molecules and signals, such as iron metabolism, lipid peroxidation, system Xc−, GPX4, ROS production, and Nrf2 signals. Non-coding RNAs (ncRNAs) are a type of functional RNA molecules that are not translated into a protein. Increasing studies demonstrate that ncRNAs have a diversity of regulatory roles in ferroptosis, thus influencing the progression of cancers. In this study, we review the fundamental mechanisms and regulation network of ncRNAs on ferroptosis in various tumors, aiming to provide a systematic understanding of recently emerging non-coding RNAs and ferroptosis.

An m7G-related lncRNA signature predicts prognosis and reveals the immune microenvironment in bladder cancer

Bladder cancer (BC) is a representative malignant tumor type, and the significance of N7-methyguanosine (m7G)-related lncRNAs in BC is still unclear. Utilizing m7G-related lncRNAs, we developed a prognostic model to evaluate BC's prognosis and tumor immunity. First, we selected prognostic lncRNAs related to m7G by co-expression analysis and univariate Cox regression and identified two clusters by consensus clustering. The two clusters differed significantly in terms of overall survival, clinicopathological factors, and immune microenvironment. Then, we further constructed a linear stepwise regression signature by multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients fell into high-risk (HR) and low-risk (LR) groups considering the train group risk score. HR group had worse prognoses when stratified by clinicopathological factors. The receiver operating curve (ROC) suggested that the signature had a better prognostic value. Tumor mutation burden (TMB) showed a negative relevance to the risk score, and patients with low TMB presented a better prognosis. Validation of the signature was carried out with multivariate and univariate Cox regression analysis, nomogram, principal component analysis (PCA), C-Index, and quantitative reverse transcriptase PCR (qRT-PCR). Finally, the gene set enrichment analysis (GSEA) demonstrated the enrichment of tumor-related pathways in HR groups, and single-sample gene set enrichment analysis (ssGSEA) indicated a close association of risk score with tumor immunity. According to the drug sensitivity test, the signature could predict the effects of conventional chemotherapy drugs. In conclusion, our study indicates the close relevance of m7G-related lncRNAs to BC, and the established risk signature can effectively evaluate patient prognosis and tumor immunity and is expected to become a novel prognostic marker for BC patients.

Identification and validation of the pyroptosis-related long noncoding rna signature to predict the prognosis of patients with bladder cancer

Bladder cancer ranked the second most frequent tumor among urological malignancies. This work investigated bladder cancer prognosis, including the relevance of pyroptosis-related long noncoding RNA (lncRNA) in it and its potential roles. The Cancer Genome Atlas database offered statistics on lncRNAs and clinical data from 411 bladder cancer patients. Pearson correlation analysis was used to evaluate pyroptosis-related lncRNAs. To explore prognosis-associated lncRNAs, we performed univariate Cox regression, least absolute shrinkage and selection operator regression analyses, as well as the Kaplan-Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Afterward, a nomogram was constructed according to the risk score and clinical variables. Finally, to investigate the potential functions of pyroptosis-related lncRNAs, gene set enrichment analysis was employed. Eleven pyroptosis-related lncRNAs were screened to be closely associated with patients prognosis. On this foundation, a risk score model was created to classify patients into high and low risk groups. The signature was shown to be an independent prognostic factor (P < .001) with an area under the curve of 0.730. Then a nomogram was established including risk scores and clinical characteristics. The nomogram prediction effect is excellent, with a concordance index of 0.86. The 11-lncRNAs signature was associated with the supervision of oxidative stress, epithelial-mesenchymal transition, cell adhesion, TGF-β, and Wingless and INT-1 signaling pathway, according to the gene set enrichment analysis. Our findings indicate that pyroptosis-related lncRNAs, which may affect tumor pathogenesis in many ways, might be exploited to assess the prognosis of bladder cancer patients.

Construction of a cuproptosis-related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients

BACKGROUND

Long noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients.

METHODS

Transcriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, C-index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high-risk and low-risk patients. We further conducted the ESTIMATE, single-smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups.

RESULTS

We screened out four cuproptosis-related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low-risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy.

CONCLUSIONS

We developed a novel cuproptosis-related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.

Glutathione S-transferase zeta 1 alters the HMGB1/GPX4 axis to drive ferroptosis in bladder cancer cells

OBJECTIVE

The ability of glutathione S-transferase zeta 1 (GSTZ1) to modulate homeostasis of cellular redox and induce ferroptosis was explored in bladder cancer cells, and the involvement of the high mobility group protein 1/glutathione peroxidase 4 (HMGB1/GPX4) in these effects was studied.

METHODS

BIU-87 cells stably overexpressing GSTZ1 were transfected with appropriate plasmids to deplete HMGB1 or overexpress GPX4, then treated with deferoxamine and ferrostatin-1. Antiproliferative effects were assessed by quantifying levels of ferroptosis markers, such as iron, glutathione (GSH), malondialdehyde (MDA), reactive oxygen species (ROS), GPX4, transferrin, and ferritin.

RESULTS

GSTZ1 was significantly downregulated in bladder cancer cells. GSTZ1 overexpression downregulated GPX4 and GSH, while greatly increasing levels of iron, MDA, ROS, and transferrin. GSTZ1 overexpression also decreased proliferation of BIU-87 cells and activated HMGB1/GPX4 signaling. The effects of GSTZ1 on ferroptosis and proliferation were antagonized by HMGB1 knockdown or GPX4 overexpression.

CONCLUSION

GSTZ1 induces ferroptotic cell death and alters cellular redox homeostasis in bladder cancer cells, and these effects involve activation of the HMGB1/GPX4 axis.

Prognosis Prediction of Disulfidptosis-Related Genes in Bladder Cancer and a Comprehensive Analysis of Immunotherapy

As a newly discovered mechanism of cell death, disulfidptosis is expected to help diagnose and treat bladder cancer patients. First, data obtained from public databases were analyzed using bioinformatics techniques. SVA packages were used to combine data from different databases to remove batch effects. Then, the differential analysis and COX regression analysis of ten disulfidptosis-related genes identified four prognostically relevant differentially expressed genes which were subjected to Lasso regression for further screening to obtain model-related genes and output model formulas. The predictive power of the prognostic model was verified and the immunohistochemistry of model-related genes was verified in the HPA database. Pathway enrichment analysis was performed to identify the mechanism of bladder cancer development and progression. The tumor microenvironment and immune cell infiltration of bladder cancer patients with different risk scores were analyzed to personalize treatment. Then, information from the IMvigor210 database was used to predict the responsiveness of different risk patients to immunotherapy. The oncoPredict package was used to predict the sensitivity of patients at different risk to chemotherapy drugs, and its results have some reference value for guiding clinical use. After confirming that our model could reliably predict the prognosis of bladder cancer patients, the risk scores were combined with clinical information to create a nomogram to accurately calculate the patient survival rate. A prognostic model containing three disulfidptosis-related genes (NDUFA11, RPN1, SLC3A2) was constructed. The functional enrichment analysis and immune-related analysis indicated patients in the high-risk group were candidates for immunotherapy. The results of drug susceptibility analysis can guide more accurate treatment for bladder cancer patients and the nomogram can accurately predict patient survival. NDUFA11, RPN1, and SLC3A2 are potential novel biomarkers for the diagnosis and treatment of bladder cancer. The comprehensive analysis of tumor immune profiles indicated that patients in the high-risk group are expected to benefit from immunotherapy.

Starvation-induced long non-coding RNAs are significant for prognosis evaluation of bladder cancer

BACKGROUND

Starving intratumoral microenvironment prominently alters genic profiles including long non-coding RNAs (lncRNAs), which further regulate bladder cancer (BCa) malignant biological properties, such as invasion and migration.

METHODS

Transcriptome RNA-sequencing data of 414 BCa tumor tissues and 19 normal tissues were obtained from TCGA database and paired samples of 132 BCa patients. A chain of in vitro validations such as qPCR, migration and invasion assays were performed to reveal the clinical relevance of AC011472.4 and AL157895.1.

RESULTS

A total of 11 lncRNAs were identified as starvation-related lncRNAs, of which AC011472.4 and AL157895.1 were relevant to overall survival of BCa patients. Besides, a starvation-related risk score model was established based on the levels of AC011472.4 and AL157895.1. BCa patients with higher levels of AL157895.1 were divided into the high-risk group and usually obtained higher mortality rate, but AC011472.4 was contrary. AL157895.1 expressed highly in BCa cell lines and tumour tissues, especially in patients with the advanced grade, stage and T-stage, while AC011472.4 showed the reversed result. Moreover, increased level of AL157895.1 was remarkably correlated to T-stage, muscle invasion status and distant metastasis. SiRNAs-mediated silence of AC011472.4 and AL157895.1 respectively increased and diminished invasion and migration properties of BCa cells.

CONCLUSIONS

In this study, we highlight the significant roles of AC011472.4 and AL157895.1 on evaluating prognoses of BCa patients and validate their correlation with various clinical parameters. These findings provide an appropriate risk score model for BCa clinical decision making.

Construction of a ferroptosis-related signature based on seven lncRNAs for prognosis and immune landscape in clear cell renal cell carcinoma

BACKGROUND

Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC).

METHODS

In the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups.

RESULTS

A 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups.

CONCLUSION

We established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.

Construction of an lncRNA model for prognostic prediction of bladder cancer

OBJECTIVE

We aimed to investigate the role and potential mechanisms of long non-coding RNAs (lncRNAs) in bladder cancer (BC), as well as determine their prognostic value.

METHODS

LncRNA expression data and clinical data from BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. R software was used to carry out principal component analysis (PCA), differential analysis, and prognostic analysis. Lasso regression and multivariate Cox regression analyses were performed to identify potential prognostic genes. The expression of five identified genes and their correlation with prognosis were verified using TCGA and GSE13507 datasets. In addition, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm the expression of these five genes in cell lines (two human BC cell lines and one human bladder epithelial cell line) and tissues (84 pairs of BC tissues and the corresponding paracancerous tissues). Risk scores that had been generated from the five genes and their prognostic ability were assessed by receiver operating characteristic (ROC) and Kaplan-Meier (KM) curves. Co-expressed genes were screened by WGCNA and analyzed by GO and KEGG, while functional enrichment and immune infiltration analyses were performed using STRING ( https://cn.string-db.org/ ) and TIMER2.0 ( http://timer.cistrome.org/ ) online tools, respectively.

RESULTS

CYP4F8, FAR2P1, LINC01518, LINC01764, and DTNA were identified as potential prognostic genes. We found that these five genes were differentially expressed in BC tissue, as well as in BC cell lines, and were significantly correlated with the prognosis of BC patients. KM analysis considering risk scores as independent parameters revealed differences in overall survival (OS) by subgroups. The ROC curve revealed that a combined model consisting of all five genes had good predictive ability at 1, 3, and 5 years. GO and KEGG analyses of 567 co-expressed genes revealed that these genes were significantly associated with muscle function.

CONCLUSION

LncRNAs can be good predictors of BC development and prognosis, and may act as potential tumor markers and therapeutic targets that may be beneficial in helping clinicians decide the most effective treatment strategies.

M7G-related LncRNAs: A comprehensive analysis of the prognosis and immunity in glioma

Today, numerous international researchers have demonstrated that N7-methylguanosine (m7G) related long non-coding RNAs (m7G-related lncRNAs) are closely linked to the happenings and developments of various human beings’ cancers. However, the connection between m7G-related lncRNAs and glioma prognosis has not been investigated. We did this study to look for new potential biomarkers and construct an m7G-related lncRNA prognostic signature for glioma. We identified those lncRNAs associated with DEGs from glioma tissue sequences as m7G-related lncRNAs. First, we used Pearson’s correlation analysis to identify 28 DEGs by glioma and normal brain tissue gene sequences and predicated 657 m7G-related lncRNAs. Then, eight lncRNAs associated with prognosis were obtained and used to construct the m7G risk score model by lasso and Cox regression analysis methods. Furthermore, we used Kaplan-Meier analysis, time-dependent ROC, principal component analysis, clinical variables, independent prognostic analysis, nomograms, calibration curves, and expression levels of lncRNAs to determine the model’s accuracy. Importantly, we validated the model with external and internal validation methods and found it has strong predictive power. Finally, we performed functional enrichment analysis (GSEA, aaGSEA enrichment analyses) and analyzed immune checkpoints, associated pathways, and drug sensitivity based on predictors. In conclusion, we successfully constructed the formula of m7G-related lncRNAs with powerful predictive functions. Our study provides instructional value for analyzing glioma pathogenesis and offers potential research targets for glioma treatment and scientific research.

Construction of prognostic signature of breast cancer based on N7-Methylguanosine-Related LncRNAs and prediction of immune response

Background: Long non-coding RNA (LncRNA) is a prognostic factor for malignancies, and N7-Methylguanosine (m7G) is crucial in the occurrence and progression of tumors. However, it has not been documented how well m7G-related LncRNAs predict the development of breast cancer (BC). This study aims to develop a predictive signature based on long non-coding RNAs (LncRNAs) associated with m7G to predict the prognosis of breast cancer patients.

Methods: The Cancer Genome Atlas (TCGA) database provided us with the RNA-seq data and matching clinical information of individuals with breast cancer. To identify the signature of N7-Methylguanosine-Related LncRNAs and create a prognostic model, we employed co-expression network analysis, least absolute shrinkage selection operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The signature was assessed using the Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curve. A nomogram and principal component analysis (PCA) were employed to confirm the predictive signature’s usefulness. Then, we examined the drug sensitivity between the two risk groups and utilized single-sample gene set enrichment analysis (ssGSEA) to investigate the association between predictive factors and the tumor immune microenvironment in high-risk and low-risk groups.

Results: Nine m7G-related LncRNAs (LINC01871, AP003469.4, Z68871.1, AC245297.3, EGOT, TFAP2A-AS1, AL136531.1, SEMA3B-AS1, AL606834.2) that are independently associated with the overall survival time (OS) of BC patients make up the signature we developed. For predicting 1-, 3-, and 5-year survival rates, the areas under the ROC curve (AUC) were 0.715, 0.724, and 0.726, respectively. The Kaplan-Meier analysis revealed that the prognosis of BC patients in the high-risk group was worse than that of those in the low-risk group. When compared to clinicopathological variables, multiple regression analysis demonstrated that risk score was a significant independent predictive factor for BC patients. The results of the ssGSEA study revealed a substantial correlation between the predictive traits and the BC patients’ immunological status, low-risk BC patients had more active immune systems, and they responded better to PD1/L1 immunotherapy.

Conclusion: The prognostic signature, which is based on m7G-related LncRNAs, can be utilized to inform patients’ customized treatment plans by independently predicting their prognosis and how well they would respond to immunotherapy.

PHGDH Inhibits Ferroptosis and Promotes Malignant Progression by Upregulating SLC7A11 in Bladder Cancer

Background: Bladder cancer (BCa) is a prevalent urologic malignancy that shows a poor prognosis. Abnormal metabolism and its key genes play a critical role in BCa progression. In this study, the role played by PhosphoGlycerol Dehydrogenase (PHGDH), an important molecule of serine metabolism, was investigated with regard to the regulation of ferroptosis in BCa.

Methods: The BCa tissues of 90 patients were analyzed by RNA-sequencing for differential pathways and genes. Western blot, qPCR, and IHC were used to determine PHGDH expression in the cell lines (in vitro) and patient tissues (in vivo). R software was used to analyze PHGDH expression, prognosis, and PHGDH+SLC7A11 score. The biological functions of PHGDH were examined through organoids, and in vitro and in vivo experiments. C11 probes, electron microscopy, and ferroptosis inhibitors/ inducers were used to detect cellular ferroptosis levels. Protein profiling, co-IP, and RIP assays were used to screen proteins that might bind to PHGDH. PHGDH-targeted inhibitor NCT-502 was used to evaluate its effect on BCa cells.

Results: PHGDH was highly expressed in patients with BCa. Knock-down of PHGDH promoted ferroptosis, while the decreased proliferation of BCa cells. Additionally, PHGDH knock-down downregulated the expression of SLC7A11. Co-IP and mass spectrometry experiments indicate that PHGDH binds to PCBP2, an RNA-binding protein, and inhibits its ubiquitination degradation. PCBP2 in turn stabilizes SLC7A11 mRNA and increases its expression. NCT-502, a PHGDH inhibitor, promotes ferroptosis and inhibits tumor progression in BCa. The PHGDH+ SLC7A11 score was significantly correlated with patient prognosis.

Conclusions: To conclude, the PHGDH, via interaction with PCBP2, upregulates SLC7A11 expression. This inhibits ferroptosis and promotes the malignant progression of BCA. The results of this study indicated that NCT-502 could serve as a therapeutic strategy for BCa.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.

Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.

Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.

Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

Comprehensive Characterization of Necroptosis-Related lncRNAs in Bladder Cancer Identifies a Novel Signature for Prognosis Prediction

Background

Bladder cancer (BC) is one of the most serious genitourinary malignant diseases with a poor prognosis. Necroptosis is a regulated form of cell death, and targeting necroptosis is emerging as a potential tumor therapy strategy. Nevertheless, the roles of necroptosis-related long noncoding RNAs (nrlncRNAs) in BC remains to be illustrated. This work is aimed at studying the clinical implications of nrlncRNAs in BC.

Methods

The RNA-seq data and corresponding clinical data, downloaded from The Cancer Genome Atlas (TCGA) database, were utilized to obtain prognostic nrlncRNAs and construct a prediction nomogram for BC. The comprehensive profiling of the functional pathways, immune status, mutational landscape, and drug sensitivity related to the necroptosis-related lncRNA signature (NerRLsig) was performed.

Results

Herein, a signature consisting of 12 necroptosis-related lncRNAs (AC015802.4, AL391807.1, AL078644.1, AC023825.2, AL132655.2, AP003352.1, STAG3L5P-PVRIG2P-PILRB, AC024451.4, MAP3K14-AS1, AL731567.1, AC010542.5, and AC009299.2) was constructed. The established signature can independently predict the poor overall survival of BC patients. Additionally, the NerRLsig had higher diagnostic validity compared to other clinicopathological variables, with a greater area under the receptor operating characteristic and concordance index curves. Finally, we found the differences in the functional signaling pathway, immune status, mutational profile, and drug sensitivity between the two subgroups.

Conclusion

This research revealed that the prognostic NerRLsig and nomogram could accurately predict the prognosis of BC.

Prediction of Prognosis and Molecular Mechanism of Ferroptosis in Hepatocellular Carcinoma Based on Bioinformatics Methods

Background

As an iron-dependent type of programmed cell death, ferroptosis plays an important role in the pathogenesis and progression of hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) have been linked to the prognosis of patients with HCC in a number of studies. Nevertheless, the predictive value of lncRNAs (FRLs) associated with ferroptosis in HCC has not been fully elucidated.

Methods

Download RNA sequencing data and clinical profiles of HCC patients from The Cancer Genome Atlas (TCGA) database. The FRLs associated with prognosis were determined by Pearson's correlation analysis. After that, prognostic signature for FRLs was established using Cox and LASSO regression analyses. Meanwhile, survival analysis, correlation analysis of clinicopathological features, Cox regression, receiver operating characteristic (ROC) curve, and nomogram were used to analyze the FRL signature's predictive capacity. The relationship between signature risk score, immune cell infiltration, and chemotherapy drug sensitivity is further studied.

Results

In total, 93 FRLs were found to be of prognostic value in patients with HCC. A five-FRL signature comprising AC015908.3, LINC01138, AC009283.1, Z83851.1, and LUCAT1 was created in order to enhance the prognosis prediction with HCC patients. The signature demonstrated a good predictive potency, according to the Kaplan-Meier and ROC curves. The five-FRL signature was found to be a risk factor independent of various clinical factors using Cox regression and stratified survival analysis. The high-risk group was shown to be enriched in tumorigenesis and immune-related pathways according to GSEA analysis. Additionally, immune cell infiltration, immune checkpoint molecules, and half-inhibitory concentrations differed considerably between risk groups, implying that this signature could be used to evaluate the clinical efficacy of chemotherapy and immunotherapy.

Conclusion

The five-FRL risk signature is helpful for assessing the prognosis of HCC patients and improving therapy options, so it can be further applied clinically.

Ferroptosis-related long non-coding RNA signature predicts the prognosis of hepatocellular carcinoma

Background: Hepatocellular Carcinoma (HCC) is a highly heterogeneous malignant tumor, and its prognostic prediction is extremely challenging. Ferroptosis is a cell mechanism dependent on iron, which is very significant for HCC development. Long non-coding RNA (lncRNA) is also linked to HCC progression. This work aimed to establish a prognosis risk model for HCC and to discover a possible biomarker and therapeutic target.

Methods: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-seq transcriptome data and clinic information of HCC patients. Firstly, univariate Cox was utilized to identify 66 prognostic ferroptosis-related lncRNAs. Then, the identified lncRNAs were further included in the multivariate Cox analysis to construct the prognostic model. Eventually, we performed quantitative polymerase chain reaction (q-PCR) to validate the risk model.

Results: We established a prognostic seventeen-ferroptosis-related lncRNA signature model. The signature could categorize patients into two risk subgroups, with the low-risk subgroup associated with a better prognosis. Additionally, the area under the curve (AUC) of the lncRNAs signature was 0.801, indicating their reliability in forecasting HCC prognosis. Risk score was an independent prognostic factor by regression analyses. Gene set enrichment analysis (GSEA) analyses demonstrated a remarkable enrichment of cancer-related and immune-related pathways in the high-risk group. Besides, the immune status was decreased in the high-risk group. Eventually, three prognostic lncRNAs were validated in human HCCLM3 cell lines.

Conclusions: The risk model based on seventeen-ferroptosis-related lncRNA has significant prognostic value for HCC and may be therapeutic targets associated with ferroptosis in clinical ways.

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Background

Some studies have revealed a close relationship between metabolism-related genes and the prognosis of bladder cancer. However, the relationship between metabolism-related long non-coding RNAs (lncRNA) regulating the expression of genetic material and bladder cancer is still blank. From this, we developed and validated a prognostic model based on metabolism-associated lncRNA to analyze the prognosis of bladder cancer.

Methods

Gene expression, lncRNA sequencing data, and related clinical information were extracted from The Cancer Genome Atlas (TCGA). And we downloaded metabolism-related gene sets from the human metabolism database. Differential expression analysis is used to screen differentially expressed metabolism-related genes and lncRNAs between tumors and paracancer tissues. We then obtained metabolism-related lncRNAs associated with prognosis by correlational analyses, univariate Cox analysis, and logistic least absolute shrinkage and selection operator (LASSO) regression. A risk scoring model is constructed based on the regression coefficient corresponding to lncRNA calculated by multivariate Cox analysis. According to the median risk score, patients were divided into a high-risk group and a low-risk group. Then, we developed and evaluated a nomogram including risk scores and Clinical baseline data to predict the prognosis. Furthermore, we performed gene-set enrichment analysis (GSEA) to explore the role of these metabolism-related lncRNAs in the prognosis of bladder cancer.

Results

By analyzing the extracted data, our research screened out 12 metabolism-related lncRNAs. There are significant differences in survival between high and low-risk groups divided by the median risk scoring model, and the low-risk group has a more favorable prognosis than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk score was closely related to the prognosis of bladder cancer. Then we established a nomogram based on multivariate analysis. After evaluation, the modified model has good predictive efficiency and clinical application value. Furthermore, the GSEA showed that these lncRNAs affected bladder cancer prognosis through multiple links.

Conclusions

A predictive model was established and validated based on 12 metabolism-related lncRNAs and clinical information, and we found these lncRNA affected bladder cancer prognosis through multiple links.