Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

Prognostic risk model under the immune-associated long chain non-coding ribonucleic acid and its application in survival prognosis assessment of patients with breast cancer

This study aimed to develop a prognostic risk model based on immune-related long non-coding RNAs (lncRNAs). By analyzing the expression profiles of specific long non-coding RNAs, the objective was to construct a predictive model to accurately assess the survival prognosis of breast cancer (BC) patients. This effort seeks to provide personalized treatment strategies for patients and improve clinical outcomes. Based on the median risk value, 300 samples of triple-negative BC (TNBC) patients were rolled into a high-risk group (HR group, n = 140) and a low-risk group (LR group, n = 160). Multivariate Cox (MVC) analysis was performed by combining the patient risk score and clinical information to evaluate the prognostic value of the prognostic risk (PR) model. A total of 371 immune-related lncRNAs associated with the prognosis of TNBC were obtained from 300 TNBC samples. Nine associated with prognosis were obtained by univariate Cox (UVC) analysis, and 3 (AC090181.2, LINC01235, and LINC01943) were selected by MVC analysis for the construction of TNBC PR model. Survival analysis showed a great difference in TNBC patients in different groups (P < 0.001). The receiver operator characteristic (ROC) curve showed the model possessed a good area under ROC curve (AUC), which was 0.928. The patient RS jointing with clinical information as well as the MVC analysis revealed that RS was an independent risk factor (IRF) for prognosis of TNBC (P < 0.05, HR = 1.033286). Therefore, the lncRNAs associated with TNBC immunity can be screened by bioinformatics analysis, and the established PR model of TNBC could better predict the prognosis of patients with TNBC, exhibiting a high application value in clinic.

Ferroptosis-related alternative splicing signatures as potential biomarkers for predicting prognosis and therapy response in gastric cancer

Ferroptosis is linked to various tumor biological traits, and alternative splicing (AS), a crucial step in mRNA processing, plays a role in the post-transcriptional regulation of ferroptosis-related genes (FRGs). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was utilized to build a prognostic signature based on 12 AS events (p < 0.05), which was validated in gastric cancer (GC) patients. The high-risk group (n = 203) showed enrichment in cancer and metastasis pathways (p < 0.05). Significant differences existed between the high- and low-risk groups in terms of tumor microenvironment (TME) cell infiltration and immune activities (p < 0.05). The low-risk group (n = 203) was characterized by immune activation and improved prognosis (p < 0.001). Additionally, targeted treatment and immunotherapy were more likely to benefit the low-risk group (p < 0.05). Correlation analysis was performed to detect related splicing factors (SF) (Cor>0.4, FDR<0.05). Furthermore, our functional assay results suggested that high SF3A2 expression might increase ferroptosis resistance and promote cell proliferation. In conclusion, the FRAs model we built has an advantage in predicting GC prognosis. The model's demonstration of variations in the immune microenvironment and drug response could potentially inform decisions regarding treatment strategies.

Identification and verification of novel ferroptosis biomarkers predicts the prognosis of hepatocellular carcinoma

BACKGROUND

Big data mining and experiments are widely used to mine new prognostic markers.

METHODS

Candidate genes were identified from CROEMINE and FerrDb. Kaplan-Meier survival and Cox regression analysis were applied to assess the association of genes with Overall survival time (OS) and Disease-free survival time (DFS) in two HCC cohorts. Real-time quantitative polymerase chain reaction (RT-qPCR) and Immunohistochemistry were performed in HCC samples.

RESULTS

21 and 15 genes that can predict OS and DFS, which had not been reported before, were identified from 719 genes, respectively. Survival analysis showed elevated mRNA expression of GLMP, SLC38A6, and WDR76 were associated with poor prognosis, and three genes combination signature was an independent prognostic factor in HCC. RT-qPCR and Immunohistochemistry confirmed the results.

CONCLUSIONS

We established a novel computational process, which identified the expression levels of GLMP, SLC38A6, and WDR76 as potential ferroptosis-related biomarkers indicating the prognosis of HCC.

The DNA damage repair-related lncRNAs signature predicts the prognosis and immunotherapy response in gastric cancer

Background

Gastric cancer (GC) is one of the most prevalent cancers, and it has unsatisfactory overall treatment outcomes. DNA damage repair (DDR) is a complicated process for signal transduction that causes cancer. lncRNAs can influence the formation and incidence of cancers by influencing DDR-related mRNAs/miRNAs. A DDR-related lncRNA prognostic model is urgently needed to improve treatment strategies.

Methods

The data of GC samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. A total of 588 mRNAs involved in DDR were selected from MSigDB, 62 differentially expressed mRNAs from TCGA-STAD were obtained, and 137 lncRNAs were correlated with these mRNAs. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to develop a DDR-related lncRNA prognostic model. Based on the risk model, the differentially expressed gene signature A/B in the low-risk and high-risk groups of TCGA-STAD was identified for further validation.

Results

The prognosis model of 5 genes (AC145285.6, MAGI2-AS3, AL590705.3, AC007405.3, and LINC00106) was constructed and classified into two risk groups. We found that GC patients with a low-risk score had a better OS than those with a high-risk score. We found that the high-risk group tended to have higher TME scores. We also found that patients in the high-risk group had a higher proportion of resting CD4 T cells, monocytes, M2 macrophages, resting dendritic cells, and resting mast cells, whereas the low-risk subgroup had a greater abundance of activated CD4 T cells, follicular helper T cells, M0 macrophages, and M1 macrophages. We observed significant differences in the T-cell exclusion score, T-cell dysfunction, MSI, and TMB between the two risk groups. In addition, we found that patients treated with immunotherapy in the low-RS score group had a longer survival and a better prognosis than those in the high-RS score group.

Conclusion

The prognostic model has a significant role in the TME, clinicopathological characteristics, prognosis, MSI, and drug sensitivity. We also discovered that patients treated with immunotherapy in the low-RS score group had a better prognosis. This work provides a foundation for improving the prognosis and response to immunotherapy among patients with GC.

A new ferroptosis-related signature model including messenger RNAs and long non-coding RNAs predicts the prognosis of gastric cancer patients

Background and Objectives

Gastric cancer (GC) is among the most malignant tumor types, which causes heavy healthy and economic burden to the people and societies all around the world. Establishment of an effective set of prognostic marker will benefit a lot to the treatment of GC patients clinically. Ferroptosis is a newly identified regulated cell death modality, with tight relevance with GC development. However, its application in the prognosis of GC has not been studied in detail. Deregulated messenger RNA (mRNA) and long non-coding RNA (lncRNA) expression profile in tumor can serve as novel prognostic marker for predicting the survival and cancer relapse in patients.

Methods

We downloaded ferroptosis-related gene expression microarray data, clinicopathologic information and a list of 259 ferroptosis-related genes from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Ferroptosis database, respectively. Then, correlation analysis, univariate and multivariate Cox regression analysis were used to construct a novel prognostic model for GC. Then, we validated the model in the GEO datasets. Finally, we evaluated the differences in immune microenvironment between high- and low-risk groups.

Results

We utilized the ferroptosis-related mRNA and lncRNA profile to successfully construct a prognostic model (incorporating 2 mRNAs and 15 lncRNAs) in GC. Our model, integrating diverse clinical traits and critical factors of GC, showed desirable efficacy in the prognosis of GC patients. This model also manifested effectively in validation by using external patients' data.

Conclusions

Our study developed a novel ferroptosis-related signature to predict the prognosis of gastric cancer patients. The ferroptosis-related signature had a favorable predictive ability. This model may greatly boost the treatment of GC patients in clinical practice.

Identification of Cuproptosis-Related Subtypes, Establishment of a Prognostic Signature and Characterization of the Tumor Microenvironment in Gastric Cancer

Purpose

Cuproptosis is a newly identified form of programmed cell death. We aimed to comprehensively discuss the correlation of cuproptosis with gastric cancer (GC) using bioinformatic methods.

Patients and Methods

This study selected GC bulk and single-cell RNA sequencing profiles from public databases. Based on the enrichment pattern of cuproptosis-related gene sets (CRGSs), GC patients were classified into different cuproptosis subtypes. A series of systematic analyses was performed to investigate the correlation of cuproptosis subtype with biological function and immune cell infiltration. In addition, we established a CRGS risk score signature to quantify GC patients' risk level, and analyzed the signature's relationship with clinical features, tumor microenvironment (TME) and treatment responses. Genes used for the construction of the risk score model were also detected in GC tumor and normal tissues by real-time quantitative polymerase chain reaction (RT-qPCR).

Results

First, analysis of scRNA-seq data revealed the alterations in CRGS enrichment scores for patients with GC and precancerous diseases. Then, based on large GC patient cohorts, two cuproptosis subtypes with significant differences in survival, biological function and TME were identified. Furthermore, we established a CRGS risk score signature. High-risk patients on the CRGS risk score signature with worse overall survival were characterized by higher immune and stromal contents in the TME, more advanced clinicopathological features, and better sensitivity to a wider range of anti-tumor drugs. Low-risk patients were correlated with higher tumor purity, and demonstrated more favorable clinical outcomes and higher sensitivity to immunotherapy.

Conclusion

The current work elucidated that cuproptosis plays an important role in the regulation of TME landscapes in GC. Two cuproptosis subtypes with distinct TME characteristics were identified. In addition, the establishment of a CRGS risk score signature could provide novel insights into accurate prediction and personalized treatment for GC patients.

Insights on Ferroptosis and Colorectal Cancer: Progress and Updates

Patients with advanced-stage or treatment-resistant colorectal cancer (CRC) benefit less from traditional therapies; hence, new therapeutic strategies may help improve the treatment response and prognosis of these patients. Ferroptosis is an iron-dependent type of regulated cell death characterized by the accumulation of lipid reactive oxygen species (ROS), distinct from other types of regulated cell death. CRC cells, especially those with drug-resistant properties, are characterized by high iron levels and ROS. This indicates that the induction of ferroptosis in these cells may become a new therapeutic approach for CRC, particularly for eradicating CRC resistant to traditional therapies. Recent studies have demonstrated the mechanisms and pathways that trigger or inhibit ferroptosis in CRC, and many regulatory molecules and pathways have been identified. Here, we review the current research progress on the mechanism of ferroptosis, new molecules that mediate ferroptosis, including coding and non-coding RNA; novel inducers and inhibitors of ferroptosis, which are mainly small-molecule compounds; and newly designed nanoparticles that increase the sensitivity of cells to ferroptosis. Finally, the gene signatures and clusters that have predictive value on CRC are summarized.

Identification of cuproptosis-related long non-coding ribonucleic acid signature as a novel prognosis model for colon cancer

Cuproptosis is a novel type of cell death that may play a vital role in preventing various types of cancer. Studies examining cuproptosis are limited, and the cuproptosis-related lncRNAs (long non-Coding ribonucleic acids) involved in the regulation of colon cancer remain unclear. This study aimed to identify the prognostic signature of cupronosis-related lncRNAs and explore their potential molecular functions in colon cancer. Data on the clinical correlation were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed cuproptosis-related long non-coding ribonucleic acids (lncRNAs) were analyzed using the "limma" package. Then, the prognostic cuproptosis-related lncRNA signature (CupRLSig) was identified through univariate Cox and co-expression analyses, and a prognostic model was constructed based on CupRLSig using the least absolute shrinkage selection operator (LASSO) algorithm and Cox regression analysis. The Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used for evaluating the model's capacity for prognosis prediction. In addition, the immune landscape, and drug sensitivity of CupRLSig were analyzed. Finally, the functions of AL512306.3 and ZEB1-AS1 were verified through in vitro experiments. The high- or low-risk groups were classified according to the risk score. The signature-based risk score showed a stronger ability to predict patient's survival compared with the traditional clinicopathological features. In addition, immune responses, such as inflammation-promoting response and T-cell co-inhibition, were significantly different between the two groups. Moreover, chemotherapy drugs or inhibitors, such as axitinib, cisplatin, doxorubicin, and elesclomol, may be considered as potential therapeutic drugs for patients in high-risk groups. Finally, inhibition of AL512306.3 and ZEB1-AS1 significantly suppressed the cell proliferation in colon cancer cells. These results provide novel insights into the pathogenesis of colon cancer and offer promising biomarkers with the potential to guide research on carcinogenesis and cancer treatment.

Metal-dependent programmed cell death-related lncRNA prognostic signatures and natural drug sensitivity prediction for gastric cancer

Background: Gastric cancer is one of the most important malignancies with poor prognosis. Ferroptosis and cuproptosis are newly discovered metal-dependent types of programmed cell death, which may directly affect the outcome of gastric cancer. Long noncoding RNAs (lncRNAs) can affect the prognosis of cancer with stable structures, which could be potential prognostic prediction factors for gastric cancer.

Methods: Differentially expressed metal-dependent programmed cell death (PCD)-related lncRNAs were identified with DESeq2 and Pearson’s correlation analysis. Through GO and KEGG analyses and GSEA , we identified the potential effects of metal-dependent PCD-related lncRNAs on prognosis. Using Cox regression analysis with the LASSO method, we constructed a 12-lncRNA prognostic signature model. Also, we evaluated the prognostic efficiency with Kaplan–Meier (K-M) survival curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) methods. The sensitivities for antitumor drugs were then predicted with the pRRophetic method. Also, we discuss Chinese patent medicines and plant extracts that could induce metal-dependent programmed cell death.

Results: We constructed a metal-dependent PCD-related lncRNA-gene co-expression network. Also, a metal-dependent PCD-related gastric cancer prognostic signature model including 12 lncRNAs was constructed. The K-M survival curve revealed a poor prognosis in the high-risk group. ROC curve analysis shows that the AUC of our model is 0.766, which is better than that of other published models. Moreover, the half-maximum inhibitory concentration (IC50) for dasatinib, lapatinib, sunitinib, cytarabine, saracatinib, and vinorelbine was much lower among the high-risk group.

Conclusion: Our 12 metal-dependent PCD-related lncRNA prognostic signature model may improve the OS prediction for gastric cancer. The antitumor drug sensitivity analysis results may also be helpful for individualized chemotherapy regimen design.

Long Noncoding RNAs and Circular RNAs Regulate AKT and Its Effectors to Control Cell Functions of Cancer Cells

AKT serine-threonine kinase (AKT) and its effectors are essential for maintaining cell proliferation, apoptosis, autophagy, endoplasmic reticulum (ER) stress, mitochondrial morphogenesis (fission/fusion), ferroptosis, necroptosis, DNA damage response (damage and repair), senescence, and migration of cancer cells. Several lncRNAs and circRNAs also regulate the expression of these functions by numerous pathways. However, the impact on cell functions by lncRNAs and circRNAs regulating AKT and its effectors is poorly understood. This review provides comprehensive information about the relationship of lncRNAs and circRNAs with AKT on the cell functions of cancer cells. the roles of several lncRNAs and circRNAs acting on AKT effectors, such as FOXO, mTORC1/2, S6K1/2, 4EBP1, SREBP, and HIF are explored. To further validate the relationship between AKT, AKT effectors, lncRNAs, and circRNAs, more predicted AKT- and AKT effector-targeting lncRNAs and circRNAs were retrieved from the LncTarD and circBase databases. Consistently, using an in-depth literature survey, these AKT- and AKT effector-targeting database lncRNAs and circRNAs were related to cell functions. Therefore, some lncRNAs and circRNAs can regulate several cell functions through modulating AKT and AKT effectors. This review provides insights into a comprehensive network of AKT and AKT effectors connecting to lncRNAs and circRNAs in the regulation of cancer cell functions.

The Impact of Oxidative Stress and AKT Pathway on Cancer Cell Functions and Its Application to Natural Products

Oxidative stress and AKT serine-threonine kinase (AKT) are responsible for regulating several cell functions of cancer cells. Several natural products modulate both oxidative stress and AKT for anticancer effects. However, the impact of natural product-modulating oxidative stress and AKT on cell functions lacks systemic understanding. Notably, the contribution of regulating cell functions by AKT downstream effectors is not yet well integrated. This review explores the role of oxidative stress and AKT pathway (AKT/AKT effectors) on ten cell functions, including apoptosis, autophagy, endoplasmic reticulum stress, mitochondrial morphogenesis, ferroptosis, necroptosis, DNA damage response, senescence, migration, and cell-cycle progression. The impact of oxidative stress and AKT are connected to these cell functions through cell function mediators. Moreover, the AKT effectors related to cell functions are integrated. Based on this rationale, natural products with the modulating abilities for oxidative stress and AKT pathway exhibit the potential to regulate these cell functions, but some were rarely reported, particularly for AKT effectors. This review sheds light on understanding the roles of oxidative stress and AKT pathway in regulating cell functions, providing future directions for natural products in cancer treatment.

Novel insight into ferroptosis-related genes, molecular subtypes, and immune characteristics in intracranial aneurysms

OBJECTIVES

This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA).

METHODS

GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics.

RESULTS

We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs.

CONCLUSION

Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.

A novel cuproptosis-related lncRNA nomogram to improve the prognosis prediction of gastric cancer

Background

Cuproptosis is a copper-triggered modality of mitochondrial cell death and cuproptosis process may play important roles in gastric cancer development. However, little is known about cuproptosis-related lncRNAs in gastric adenocarcinoma (STAD). This study is aimed to investigate the potential prognostic signatures of cuproptosis-related lncRNAs in STAD.

Methods

The Cancer Genome Atlas (TCGA) database were used to obtain gene expression profiles, clinicopathological, and OS information for STAD. Cuproptosis-related genes were collected based on previous studies and cuproptosis-related lncRNAs were screened out by co-expression analysis. The nomogram constructed by Cox regression analysis with the minimum absolute contraction and selection operator (lasso) algorithm. In addition, the potential response of ICB therapy and immune evasion incidence were estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Immune checkpoint expressions associated with risk scores were also analyzed. The correlation of immune checkpoint CD209 and HAVCR2 expressions associated with risk scores were experimentally testified by RT-qPCR, Western Blot, and IHC.

Results

Patients were classified into high-risk and low-risk groups based on the risk score calculated in this model. The Kaplan–Meier survival curve analysis revealed that the high-risk group was associated with poor prognosis. Multivariate Cox regression analysis suggested that this lncRNA prediction model was an independent risk factor affecting the OS rate. Furthermore, ROC curve indicates that the nomogram was superior to traditional clinicopathological features in predicting STAD prognosis. Finally, functional enrichment analysis and immune checkpoint investigation revealed that the nomogram is notably associated with cholesterol metabolism and immune functions, RT-qPCR and Western Blotting demonstrated the co-expression relationship of LINC01150 with CD209 and HAVCR2.

Conclusion

A novel cuproptosis-related lncRNAs signature impacts on the prognosis and immunological features of GC.

Cell death affecting the progression of gastric cancer

Gastric cancer is a gastrointestinal tumor with high morbidity and mortality rates. Several factors influence its progression, cell death being an important element. In this review, we summarized the effects of necrosis, apoptosis, necroptosis, pyroptosis, ferroptosis, and eight less common cell death modalities on gastric cancer cells and the tumor microenvironment, detailed the molecular mechanisms of various cell death and their major regulatory pathways in gastric cancer, explored the prevalence and complexity of cell death in gastric cancer progression and highlighted the potentials of cell death-related therapies in gastric cancer.

Role of ferroptosis and ferroptosis-related non-coding RNAs in the occurrence and development of gastric cancer

Gastric cancer (GC) is a malignant cancer of the digestive tract and is a life-threatening disease worldwide. Ferroptosis is a newly discovered form of regulated cell death, which involves the accumulation of iron-dependent lipid peroxides. It has been found that ferroptosis plays an important regulatory role in the occurrence, development, drug resistance, and prognosis of GC. Non-coding RNAs (ncRNAs) play a critical role in the occurrence and progression of a variety of diseases including GC. In recent years, the role of ferroptosis and ferroptosis-related ncRNAs (miRNA, lncRNA, and circRNA) in the occurrence, development, drug resistance, and prognosis of GC has attracted more and more attention. Herein, we briefly summarize the roles and functions of ferroptosis and ferroptosis-related ncRNAs in GC tumorigenesis, development, and prognosis. We also prospected the future research direction and challenges of ferroptosis and ferroptosis-related ncRNAs in GC.

Clinical outcomes and potential therapies prediction of subgroups based on a ferroptosis-related long non-coding RNA signature for gastric cancer

Background: Gastric cancer (GC) is one of the most aggressive malignant tumors worldwide. Ferroptosis is a kind of iron-dependent cell death, which is proved to be closely related to tumor progression. In this study, we aim at constructing a ferroptosis-related lncRNAs signature to predict the prognosis of GC and explore potential therapies.

Methods: Ferroptosis-Related LncRNAs Signature for GC patients (FRLSG) was constructed through univariate Cox regression, the LASSO algorithm, and multivariate Cox regression. Kaplan–Meier analysis, receiver operating characteristic curves, and risk score plot were applied to verify the predictive power of FRLSG. Gene Set Enrichment Analysis (GSEA) and immune infiltration analyses were conducted to explore the potential clinical value of the FRLSG. In addition, drug sensitivity prediction was applied to identify chemotherapeutic drugs with potential therapeutic effect.

Results: Five ferroptosis-related lncRNAs (AC004816.1, AC005532.1, LINC01357, AL355574.1 and AL049840.4) were identified to construct FRLSG, whose expression level in GC were confirmed by experimental validation. Kaplan-Meier curve and ROC curve proved the reliability and effectiveness of the FRLSG in predicting the prognosis for GC patients. Several immune-related pathways were enriched in the high-FRLSG group, and further immune infiltration analyses demonstrated the high immune infiltration status of the high-FRLSG group. In addition, 19 and 24 candidate drugs with potential therapeutic effect were identified for the high- and low-FRLSG groups, respectively.

Conclusions: FRLSG was an effective tool in predicting the prognosis of GC, which might help to prioritize potential therapeutics for GC patients.

Identification of Ferroptosis-Associated Long Noncoding RNA Prognostic Model and Tumor Immune Microenvironment in Thyroid Cancer

Background

Thyroid cancer (TC) is a rapidly increasing incidence of endocrine malignancies, occupying 3% of new cancer incidence, of which 10% has a heterogeneous prognosis. Ferroptosis is a form of cell death distinct from apoptosis, which involves antitumor drug-related research. Long noncoding RNAs (lncRNAs) could affect cancer prognosis by regulating the ferroptosis; thus, ferroptosis-associated lncRNAs are emerging as prospective biomarkers for cancer therapy and prognosis. However, the prognostic factors of ferroptosis-associated lncRNAs in this solid tumor and their mechanisms remain unknown.

Methods

The TC lncRNA data were extracted from RNA sequencing files of The Cancer Genome Atlas (TCGA). Then, we performed a two-cluster analysis and grouped 502 patients with TC in a 7 : 3 ratio. Both the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis were conducted to create and validate the ferroptosis-associated lncRNA prognostic model (Ferr-LPM). Based on the median Ferr-LPM-based risk score (LPM_score) of the training cohort, we categorized patients into high and low LPM_score groups, which were then subjected to prognostic correlation and difference analysis. We also created a nomogram and assessed its predictive ability. Furthermore, immune-related mechanisms were investigated by analyzing the tumor immune microenvironment (TIME) and applying algorithms such as CIBERSROT.

Results

We built a highly accurate nomogram to promote the clinical applicability of Ferr-LPM. The area under the receiver operating characteristic curve (AUC-ROC) reached above 0.9. Survival analysis suggested that when the Ferr-LPM score was higher, the overall survival (OS) of patients within this group was shorter. Meanwhile, we found a strong association between Ferr-LPM and TIME. Interestingly, the LPM_score was inversely proportional to the tumor purity but positively related to immune checkpoint blockade (ICB) response.

Conclusion

We constructed a novel ferroptosis-associated lncRNA nomogram that could highly predict the prognosis of TC patients. Ferroptosis-associated lncRNAs might possess potential functions in regulating TIME, and lncRNAs provide TC patients with new prognostic biomarkers and therapeutic targets.

A Novel Ferroptosis-Related LncRNA Pair Prognostic Signature Predicts Immune Landscapes and Treatment Responses for Gastric Cancer Patients

Background: The construction of ferroptosis-related lncRNA prognostic models in malignancies has been an intense area of research recently. However, most of the studies focused on the exact expression of lncRNAs and had limited application values. Herein, we aim to establish a novel prognostic model for gastric cancer (GC) patients and discuss its correlation with immune landscapes and treatment responses.

Methods: The present study retrieved transcriptional data of GC patients from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed ferroptosis-related lncRNAs between tumor and normal controls of GC samples. Based on a new method of cyclically single pairing, we constructed a 0 or 1 matrix of ferroptosis-related lncRNA pairs (FRLPs). A risk score signature consisting of 10 FRLPs was established using multi-step Cox regression analysis. Next, we performed a series of systematic analyses to investigate the association of the FRLP model and tumor microenvironment, biological function, and treatment responses. An alternative model to the FRLP risk score signature, the gene set score (GS) model was also constructed, which could represent the former when lncRNA expression was not available.

Results: We established a novel prognostic signature of 10 ferroptosis-related lncRNA pairs. High-risk patients in our risk score model were characterized by high infiltration of immune cells, upregulated carcinogenic and stromal activities, and heightened sensitivity to a wide range of anti-tumor drugs, whereas low-risk patients were associated with better responses to methotrexate treatment and elevated immunotherapeutic sensitivity. The practicability of the FRLP risk score model was also validated in two independent microarray datasets downloaded from Gene Expression Omnibus (GEO) using the GS model. Finally, two online dynamic nomograms were built to enhance the clinical utility of the study.

Conclusion: In this study, we developed a ferroptosis-related lncRNA pair-based risk score model that did not rely on the exact lncRNA expression level. This novel model might provide insights for the accurate prediction and comprehensive management for GC patients.

A Ferroptosis-Related lncRNA Model to Enhance the Predicted Value of Cervical Cancer

Background

Cervical cancer (CC) is a common gynecological malignant tumor. Ferroptosis is a new type of programmed cell death, which plays a crucial part in cancer. However, current knowledge regarding ferroptosis-related long noncoding RNAs (lncRNAs) in CC is still limited. Therefore, our aim is to identify ferroptosis-related lncRNAs, build a steady prediction model, and improve the prediction value of CC.

Methods

We obtained RNA expression and ferroptosis-related gene data of female CC patients from TCGA and FerrDb databases, respectively. Then, the ferroptosis-related lncRNAs were obtained by the limma R package and Cytoscape 3.7.1. We constructed the prediction model by Cox regression analysis. Finally, the prediction model was verified by the median risk score, Kaplan–Meier analysis, the time-dependent receiver operating characteristic (ROC) curve, clinical features, and immunoinfiltration analysis.

Results

We acquired 1393 ferroptosis-related lncRNAs. The ferroptosis-related lncRNA signature was obtained by multivariate Cox regression analysis, and the patients were divided into a high-risk group and a low-risk group. The prognosis of the high-risk group was worse than that of the low-risk group. We found that the risk score can be used as an independent prognostic index by multivariate Cox regression analysis. The area under the time-dependent ROC curve reached 0.847 at 1 year, 0.906 at 2 years, 0.807 at 3 years, and 0.724 at 5 years in the training cohort. Principal component analysis showed that the diffusion directions of the two groups were different. Gene set enrichment analysis indicated that lncRNAs of two groups may be involved in tumorigenesis. Further analysis showed that high-risk groups were related to immune-related pathways. Ferroptosis-related lncRNAs are related to the proportion of tumor-infiltrating immune cells in CC.

Conclusion

We have constructed a ferroptosis-related lncRNA prediction model. The prognostic model had important clinical significance, including improving the predictive value and guiding the individualized treatment of CC patients.

Identification and Comprehensive Prognostic Analysis of a Novel Chemokine-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with poor survival outcomes. Immunotherapy can improve the prognosis of many cancers, including GC. However, in clinical practice, not all cancer patients are sensitive to immunotherapy. Therefore, it is essential to identify effective biomarkers for predicting the prognosis and immunotherapy sensitivity of GC. In recent years, chemokines have been widely reported to regulate the tumor microenvironment, especially the immune landscape. However, whether chemokine-related lncRNAs are associated with the prognosis and immune landscape of GC remains unclear. In this study, we first constructed a novel chemokine-related lncRNA risk model to predict the prognosis and immune landscape of GC patients. By using various algorithms, we identified 10 chemokine-related lncRNAs to construct the risk model. Then, we determined the prognostic efficiency and accuracy of the risk model. The effectiveness and accuracy of the risk model were further validated in the testing set and the entire set. In addition, our risk model exerted a crucial role in predicting the infiltration of immune cells, immune checkpoint genes expression, immunotherapy scores and tumor mutation burden of GC patients. In conclusion, our risk model has preferable prognostic performance and may provide crucial clues to formulate immunotherapy strategies for GC.

A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.

Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD), which accounts for approximately 95% of gastric cancer types, is a malignancy cancer with high morbidity and mortality. Tumor angiogenesis plays important roles in the progression and pathogenesis of STAD, in which long noncoding RNAs (lncRNAs) have been verified to be crucial for angiogenesis. Our study sought to construct a prognostic signature of angiogenesis-related lncRNAs (ARLncs) to accurately predict the survival time of STAD.

Methods

The RNA-sequencing dataset and corresponding clinical data of STAD were acquired from The Cancer Genome Atlas (TCGA). ARLnc sets were obtained from the Ensemble genome database and Molecular Signatures Database (MSigDB, Angiogenesis M14493, INTegrin pathway M160). A ARLnc-related prognostic signature was then constructed via univariate Cox and multivariate Cox regression analysis in the training cohort. Survival analysis and Cox regression were performed to assess the performance of the prognostic signature between low- and high-risk groups, which was validated in the validation cohort. Furthermore, a nomogram that combined the clinical pathological characteristics and risk score conducted to predict the overall survival (OS) of STAD. In addition, ARLnc-mRNA coexpression pairs were constructed with Pearson’s correlation analysis and visualized to infer the functional annotation of the ARLncs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of four ARLncs in STAD and their correlation with the angiogenesis markers, CD34 and CD105, were also validated by RT–qPCR in a clinical cohort.

Results

A prognostic prediction signature including four ARLncs (PVT1, LINC01315, AC245041.1, and AC037198.1) was identified and constructed. The OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (p < 0.001). The values of the time-dependent area under the curve (AUC) for the ARLnc signature for 1-, 3-, and 5- year OS were 0.683, 0.739, and 0.618 in the training cohort and 0.671, 0.646, and 0.680 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the ARLnc signature was an independent prognostic factor for STAD patients (p < 0.001). Furthermore, the nomogram and calibration curve showed accurate prediction of the survival time based on the risk score. In addition, 262 mRNAs were screened for coexpression with four ARLncs, and GO analysis showed that mRNAs were mainly involved in biological processes, including angiogenesis, cell adhesion, wound healing, and extracellular matrix organization. Furthermore, correlation analysis showed that there was a positive correlation between risk score and the expression of the angiogenesis markers, CD34 and CD105, in TCGA datasets and our clinical sample cohort.

Conclusion

Our study constructed a prognostic signature consisting of four ARLnc genes, which was closely related to the survival of STAD patients, showing high efficacy of the prognostic signature. Thus, the present study provided a novel biomarker and promising therapeutic strategy for patients with STAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08987-y.