Development and validation of a ferroptosis-related lncRNAs prognosis signature in colon cancer

Novel ferroptosis signature for improving prediction of prognosis and indicating gene targets from single-cell level in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is one of the most prevalent kinds of cancers. Therefore, there is a pressing need to create a new risk scoring model to personalize the prognosis of OSCC patients and screen for patient-specific therapeutic agents and molecular targets.

Methods

Firstly, A series of bioinformatics was performed to construct a novel ferroptosis-related prognostic model; Further, drug sensitivity analysis was used to screen for specific therapeutic agents for OSCC; Single-cell analysis was employed to investigate the enrichment of FRDEGs (ferroptosis-related differentially expressed genes) in the OSCC microenvironment; Finally, various experiments were conducted to screen and validate molecular therapeutic targets for OSCC.

Results

In this study, we constructed a novel 10-FRDEGs risk scoring model. Base on the risk scoring model, we founded three potential chemotherapeutic agents for OSCC: 5Z)-7-Oxozeaenol, AT-7519, KIN001-266; In addition, FRDEGs were enriched in the epithelial cells of OSCC. Finally, we found that CA9 and CAV1 could regulate OSCC proliferation, migration and ferroptosis in vitro.

Conclusion

A novel 10-FRDEGs risk scoring model can predict the prognosis of patients with OSCC.Further,5Z)-7-Oxozeaenol, AT-7519, KIN001-266 are potential chemotherapeutic agents for OSCC.Moreover, we identified CA9、CAV1 as potential molecular target for the treatment of OSCC.Our findings provide new directions for prognostic assessment and precise treatment of oral cell squamous carcinoma.

Exploring the prognostic significance of iron death‑related lncRNAs in colorectal cancer: A systematic review and meta‑analysis

Colorectal cancer is a significant health challenge globally, with its prognosis associated with the profile of molecular biomarkers. Recently, the role of iron death-associated long non-coding (lnc)RNAs in cancer development has garnered attention; however, their expression patterns and prognostic value in colorectal cancer remain poorly elucidated. The present study aimed to assess the expression levels of iron death-related lncRNAs in colorectal cancer tissues and evaluate their relationship with patient outcomes through a comprehensive meta-analysis. Systematic searches were performed across multiple databases, including PubMed, Embase, Web of Science, Cochrane Library, CNKI, Wanfang and VIP databases, to identify studies relevant to the objective of the present study. Selected studies met predetermined inclusion criteria, from which data were extracted. R software version 4.3.1 was used for the meta-analysis, evaluating the association between iron death-related lncRNAs expression and colorectal cancer prognosis. Publication bias was assessed using funnel plot analysis, and Begg's and Egger's test. A total of 11 studies, including 3,984 patients with colorectal cancer, were included in the present meta-analysis. The results demonstrated a significant association between iron death-related lncRNAs and tumor stage classification [odds ratio (OR)=2.00; 95% confidence interval (CI), 1.77-2.24]. Notably, a significant association was also revealed between iron death-related lncRNAs and T stage classification in colorectal cancer (OR=1.82; 95% CI, 1.50-2.20). Furthermore, a statistically significant association was demonstrated between iron death-related lncRNAs and lymph node metastasis in colorectal cancer (OR=1.31; 95% CI, 1.03-1.66). The findings also highlighted a significant association between iron death-associated lncRNA and distant metastasis in colon cancer (OR=2.04; 95% CI, 1.62-2.56). Moreover, a significant association between iron death-related lncRNAs and risk score in colorectal cancer was revealed (OR=1.75; 95% CI, 1.25-2.46). In conclusion, the findings of the present meta-analysis underscore the potential of high ferroptosis-associated lncRNA expression as an indicator of adverse outcomes in colorectal cancer, suggesting their viability as biomarkers for cancer progression and prognosis. This insight opens potential new avenues for clinical application and further research into colorectal cancer management.

Advancing the frontiers of colorectal cancer treatment: harnessing ferroptosis regulation

In recent years, colorectal cancer incidence and mortality have increased significantly due to poor lifestyle choices. Despite the development of various treatments, their effectiveness against advanced/metastatic colorectal cancer remains unsatisfactory due to drug resistance. However, ferroptosis, a novel iron-dependent cell death process induced by lipid peroxidation and elevated reactive oxygen species (ROS) levels along with reduced activity of the glutathione peroxidase 4 (GPX4) antioxidant enzyme system, shows promise as a therapeutic target for colorectal cancer. This review aims to delve into the regulatory mechanisms of ferroptosis in colorectal cancer, providing valuable insights into potential therapeutic approaches. By targeting ferroptosis, new avenues can be explored for innovative therapies to combat colorectal cancer more effectively. In addition, understanding the molecular pathways involved in ferroptosis may help identify biomarkers for prognosis and treatment response, paving the way for personalized medicine approaches. Furthermore, exploring the interplay between ferroptosis and other cellular processes can uncover combination therapies that enhance treatment efficacy. Investigating the tumor microenvironment's role in regulating ferroptosis may offer strategies to sensitize cancer cells to cell death induction, leading to improved outcomes. Overall, ferroptosis presents a promising avenue for advancing the treatment of colorectal cancer and improving patient outcomes.

Identification of a 5-Gene Cuproptosis Signature Predicting the Prognosis for Colon Adenocarcinoma Based on WGCNA

Background: Colorectal cancer is a highly aggressive malignant tumor that primarily affects the digestive system. It is frequently diagnosed at an advanced stage. Cuproptosis is a copper-dependent form cell death mechanism, distinct from all other known pathways underlying cell death, tumor progression, prognosis, and immune response. Although the role of cuproptosis in colorectal cancer has been investigated over time, there is still an urgent need to explore new methods and insights to understand its potential function. Methods: The Gene Expression Omnibus and The Cancer Genome Atlas gene expression data were systematically explored to investigate the role of cuproptosis in colon adenocarcinoma. The weighted gene coexpression network analysis was used to construct a gene coexpression network and identify the critical module and cuproptosis-related genes correlated with colon adenocarcinoma prognosis. A cuproptosis-related genes prognostic signature for colon adenocarcinoma was identified and validated. To validate the identified gene signature, quantitative reverse transcription-polymerase chain reaction was performed. Cell proliferation assays were analyzed by CCK8 and cell cycle detection. In addition, reactive oxygen species assay was also analyzed. Results: Five hub cuproptosis-related genes (Dihydrolipoamide S-acetyltransferase, Cyclin-dependent kinase inhibitor 2A, ATOX1, VEGFA, and ULK1) were screened and a prognostic risk model for predicting overall survival was established based on these genes. The model was successfully tested in the validation cohort and the GEPIA database. Colon adenocarcinoma patients were categorized into high-risk and low-risk groups based on risk scores. The study revealed that patients with higher risk scores were more likely to have a poor prognosis. Moreover, Dihydrolipoamide S-acetyltransferase was a tumor suppressor gene that can induce cell death and affected the redox reactions in the colon cancer cell line. Conclusions: These findings suggest that the newly identified 5-gene signature may serve as a more reliable prognostic factor than clinical factors such as age and stage of disease. These findings offer a theoretical foundation for further investigation into potential cuproptosis-related biomarkers for predicting colon adenocarcinoma prognosis in the future.

DNA methylation-related lncRNAs predict prognosis and immunotherapy response in gastric cancer

BACKGROUND

LncRNAs and DNA methylation are both key regulators of tumorigenesis and immune regulation. However, the interaction between lncRNA and DNA methylation, their regulation and their clinical and immune relevance in gastric cancer (GC) remain unclear.

METHODS

In this study, we identified DNA methylation regulator-related lncRNAs through Pearson correlation analysis in The Cancer Genome Atlas datasets. Univariate Cox regression was used to screen DNA methylationrelated prognostic lncRNAs. Further, through least absolute shrinkage and selection operator Cox regression, a prognostic model based on 13 lncRNAs was established. Survival analysis and receiver operating characteristic curve analysis verified the accuracy of the model in predicting the survival of GC patients. Univariate and multivariate analyses also confirmed that the risk score obtained from the risk model could be applied as an independent prognostic factor for patients with GC. Furthermore, based on the risk score and other clinicopathological characteristics that can be used as independent prognostic factors, we constructed a nomogram that could accurately determine the survival time of each patient. In addition, a lncRNA score was constructed using a principal component analysis algorithm to quantify the DNA methylation-related lncRNA expression patterns of individual tumors.

RESULTS

We found that a higher lncRNA score indicated a worse the prognosis and was associated with a reduced tumor mutation burden and immunosuppression. A low lncRNA score was related to an increase in neoantigen load and an increase in the anti-PDL1/CTLA4 immunotherapy response. Additionally, a low lncRNA score was related to a significant therapeutic advantage and clinical benefit.

CONCLUSIONS

This study describes a DNA methylation regulator-related lncRNA signature model, which provides a new approach for predicting therapeutic response and patient stratification in GC. Assessing lncRNA expression patterns in individual tumors will contribute to enhancing our understanding of tumor microenvironment infiltration and guide more effective immunotherapy strategies.

Noncoding RNAs in cancer ferroptosis: From biology to clinical opportunity

Ferroptosis is a recently discovered pattern of programmed cell death that is nonapoptotic and irondependent. It is involved in lipid peroxidation dependent on reactive oxygen species. Ferroptosis has been verified to play a crucial regulatory role in a variety of pathological courses of disease, in particularly cancer. Emerging research has highlighted the potential of ferroptosis in tumorigenesis, cancer development and resistance to chemotherapy. However, the regulatory mechanism of ferroptosis remains unclear, which limits the application of ferroptosis in cancer treatment. Noncoding RNAs (ncRNAs) are noncoding transcripts that regulate gene expression in various ways to affect the malignant phenotypes of cancer cells. At present, the biological function and underlying regulatory mechanism of ncRNAs in cancer ferroptosis have been partially elucidated. Herein, we summarize the current knowledge of the central regulatory network of ferroptosis, with a focus on the regulatory functions of ncRNAs in cancer ferroptosis. The clinical application and prospects of ferroptosis-related ncRNAs in cancer diagnosis, prognosis and anticancer therapies are also discussed. Elucidating the function and mechanism of ncRNAs in ferroptosis, along with assessing the clinical significance of ferroptosis-related ncRNAs, provides new perspectives for understanding cancer biology and treatment approaches, which may benefit numerous cancer patients in the future.

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

Purpose

Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value.

Patients and Methods

In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune microenvironment analysis, mutation analysis, and column line graph generation.

Results

We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load.

Conclusion

We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.

A Prognostic Cuproptosis-Related LncRNA Signature for Colon Adenocarcinoma

Background

Cuproptosis, a recently discovered form of cell death, is caused by copper levels exceeding homeostasis thresholds. Although Cu has a potential role in colon adenocarcinoma (COAD), its role in the development of COAD remains unclear.

Methods

In this study, 426 patients with COAD were extracted from the Cancer Genome Atlas (TCGA) database. The Pearson correlation algorithm was used to identify cuproptosis-related lncRNAs. Using the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) was used to select cuproptosis-related lncRNAs associated with COAD overall survival (OS). A risk model was established based on the multivariate Cox regression analysis. A nomogram model was used to evaluate the prognostic signature based on the risk model. Finally, mutational burden and sensitivity analyses of chemotherapy drugs were performed for COAD patients in the low- and high-risk groups.

Result

Ten cuproptosis-related lncRNAs were identified and a novel risk model was constructed. A signature based on ten cuproptosis-related lncRNAs was an independent prognostic predictor for COAD. Mutational burden analysis suggested that patients with high-risk scores had higher mutation frequency and shorter survival.

Conclusion

Constructing a risk model based on the ten cuproptosis-related lncRNAs could accurately predict the prognosis of COAD patients, providing a fresh perspective for future research on COAD.

Role of ferroptosis in colorectal cancer

Colorectal cancer (CRC) is the second deadliest cancer and the third-most common malignancy in the world. Surgery, chemotherapy, and targeted therapy have been widely used to treat CRC, but some patients still develop resistance to these treatments. Ferroptosis is a novel non-apoptotic form of cell death. It is an iron-dependent non-apoptotic cell death characterized by the accumulation of lipid reactive oxygen species and has been suggested to play a role in reversing resistance to anticancer drugs. This review summarizes recent advances in the prognostic role of ferroptosis in CRC and the mechanism of action in CRC.

Construction of a prognostic model of colon cancer patients based on metabolism-related lncRNAs

Background

Many studies have shown that metabolism-related lncRNAs may play an important role in the pathogenesis of colon cancer. In this study, a prognostic model for colon cancer patients was constructed based on metabolism-related lncRNAs.

Methods

Both transcriptome data and clinical data of colon cancer patients were downloaded from the TCGA database, and metabolism-related genes were downloaded from the GSEA database. Through differential expression analysis and Pearson correlation analysis, long non-coding RNAs (lncRNAs) related to colon cancer metabolism were obtained. CRC patients were divided into training set and verification set at the ratio of 2:1. Based on the training set, univariate Cox regression analysis was utilized to determine the prognostic differential expression of metabolic-related lncRNAs. The Optimal lncRNAs were obtain by Lasso regression analysis, and a risk model was built to predict the prognosis of CRC patients. Meanwhile, patients were divided into high-risk and low-risk groups and a survival curve was drawn accordingly to determine whether the survival rate differs between the two groups. At the same time, subgroup analysis evaluated the predictive performance of the model. We combined clinical indicators with independent prognostic significance and risk scores to construct a nomogram. C index and the calibration curve, DCA clinical decision curve and ROC curve were obtained as well. The above results were all verified using the validation set. Finally, based on the CIBERSORT analysis method, the correlation between lncRNAs and 22 tumor-infiltrated lymphocytes was explored.

Results

By difference analysis, 2491 differential lncRNAs were obtained, of which 226 were metabolic-related lncRNAs. Based on Cox regression analysis and Lasso results, a multi-factor prognostic risk prediction model with 13 lncRNAs was constructed. Survival curve results suggested that patients with high scores and have a poorer prognosis than patients with low scores (P<0.05). The area under the ROC curve (AUC) for the 3-year survival and 5-year survival were 0.768 and 0.735, respectively. Cox regression analysis showed that age, distant metastasis and risk scores can be used as independent prognostic factors. Then, a nomogram including age, distant metastasis and risk scores was built. The C index was 0.743, and the ROC curve was drawn to obtain the AUC of the 3-year survival and the 5-year survival, which were 0.802 and 0.832, respectively. The above results indicated that the nomogram has a good predictive effect. Enrichment analysis of KEGG pathway revealed that differential lncRNAs may be related to chemokines, amino acid and sugar metabolism, NOD-like receptor and Toll-like receptor activation as well as other pathways. Finally, the analysis results based on the CIBERSORT algorithm showed that the lncRNAs used to construct the model had a strong polarized correlation with B cells, CD8+T cells and M0 macrophages.

Conclusion

13 metabolic-related lncRNAs affecting the prognosis of CRC were screened by bioinformatics methods, and a prognostic risk model was constructed, laying a solid foundation for the research of metabolic-related lncRNAs in CRC.

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients

Background

Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease.

Methods

The Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the “MAFTools” r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted.

Results

We identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib.

Conclusion

We successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

A novel defined cuproptosis-related gene signature for predicting the prognosis of lung adenocarcinoma

Lung adenocarcinoma (LUAD) has become the most prevalent histologic subset of primary lung cancer, and effective innovative prognostic models are needed to enhance the feasibility of targeted therapies for the disease. Programmed cell death (PCD) performs an integral function in the origin and treatment of cancer. Some PCD-related effective signatures for predicting prognosis in LUAD patients could provide potential therapeutic options in LUAD. A copper-dependent cell death referred to as cuproptosis is distinct from known PCD. However, whether cuproptosis is associated with LUAD patients' prognoses and the potential roles of cuproptosis-related genes involved is still unknown. For the prediction of LUAD prognosis, we developed a unique cuproptosis-associated gene signature. In The Cancer Genome Atlas (TCGA) cohort, the score derived from the risk signature on the basis of six cuproptosis-related genes was found to independently serve as a risk factor for anticipating lung cancer-related death. The differentially expressed genes between the high- and low-risk groups were linked to the cilium-related function. LUAD patients’ prognoses may now be predicted by a unique gene signature identified in this work. This discovery also provides a substantial foundation for future research into the links between cuproptosis-associated genes and cilium-related function in LUAD patients.

A novel m7G-related lncRNA risk model for predicting prognosis and evaluating the tumor immune microenvironment in colon carcinoma

N7-Methylguanosine (m7G) modifications are a common type of posttranscriptional RNA modifications. Its function in the tumor microenvironment (TME) has garnered widespread focus in the past few years. Long non-coding RNAs (lncRNAs) played an essential part in tumor development and are closely associated with the tumor immune microenvironment. In this study, we employed a comprehensive bioinformatics approach to develop an m7G-associated lncRNA prognostic model based on the colon adenocarcinoma (COAD) database from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation analysis was performed to identify m7G-related lncRNAs. Differential gene expression analysis was used to screen lncRNAs. Then, we gained 88 differentially expressed m7G-related lncRNAs. Univariate Cox analysis and Lasso regression analysis were performed to build an eight-m7G-related-lncRNA (ELFN1-AS1, GABPB1-AS1, SNHG7, GS1-124K5.4, ZEB1-AS1, PCAT6, C1RL-AS1, MCM3AP-AS1) risk model. Consensus clustering analysis was applied to identify the m7G-related lncRNA subtypes. We also verified the risk prediction effect of a gene signature in the GSE17536 test set (177 patients). A nomogram was constructed to predict overall survival rates. Furthermore, we analyzed differentially expressed genes (DEGs) between high-risk and low-risk groups. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with the analyzed DEGs. At last, single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, MCP-COUNTER, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were utilized to discover the relationship between the risk model and the TME. Consequently, the m7G-related lncRNA risk model for COAD patients could be a viable prognostic tool and treatment target.

Construction and validation of a cuproptosis-related lncRNA signature as a novel and robust prognostic model for colon adenocarcinoma

Background

Cuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods.

Methods

The COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis.

Results

The high-risk group was associated with shorter overall survival (OS) time in COAD patients (p&lt;0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p&lt;0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR.

Conclusion

A ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients.

Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma

Purpose

Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.

Methods

Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.

Results

We identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.

Conclusion

Our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

Non-Canonical Programmed Cell Death in Colon Cancer

Simple Summary

Non-canonical PCD is an important player in colon cancer cell suicide. It influences colon cancer in many ways, such as through tumorigenesis, treatment, and prognosis. In this review, we present the mechanism, application, and prospect of different types of non-canonical PCD in colon cancer.

Abstract

Programmed cell death (PCD) is an evolutionarily conserved process of cell suicide that is regulated by various genes and the interaction of multiple signal pathways. Non-canonical programmed cell death (PCD) represents different signaling excluding apoptosis. Colon cancer is the third most incident and the fourth most mortal worldwide. Multiple factors such as alcohol, obesity, and genetic and epigenetic alternations contribute to the carcinogenesis of colon cancer. In recent years, emerging evidence has suggested that diverse types of non-canonical programmed cell death are involved in the initiation and development of colon cancer, including mitotic catastrophe, ferroptosis, pyroptosis, necroptosis, parthanatos, oxeiptosis, NETosis, PANoptosis, and entosis. In this review, we summarized the association of different types of non-canonical PCD with tumorigenesis, progression, prevention, treatments, and prognosis of colon cancer. In addition, the prospect of drug-resistant colon cancer therapy related to non-canonical PCD, and the interaction between different types of non-canonical PCD, was systemically reviewed.

Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer

Background

Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor-suppressive function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic predictor for bladder cancer (BCa).

Methods

We retrieved BCa-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression models were used to identify and develop predictive models and validate the model accuracy. Finally, we explored the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m⁶A methylation genes.

Results

Kaplan–Meier analyses screened 11 differentially expressed lncRNAs associated with poor BCa prognosis. The signature (AUC = 0.720) could be utilized to predict BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in the low-risk group. TCGA showed that the p53 signaling pathway, ferroptosis, Kaposi sarcoma − associated herpesvirus infection, IL − 17 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, PI3K − Akt signaling pathway and HIF − 1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were differentially expressed between the two risk groups. m⁶A methylation-related genes were significantly differentially expressed between the two risk groups.

Conclusion

A new ferroptosis-associated lncRNAs signature developed for predicting the prognosis of BCa patients will improve the treatment and management of BCa patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09805-9.

Establishment and Validation of a Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Lower-Grade Glioma

Background

The prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG.

Methods

We first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG.

Results

A total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups.

Conclusion

In summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.

The role of lncRNA OIP5-AS1 in cancer development and progression

OIP5-AS1, a conserved lncRNA, has been reported to be involved in several biological and pathological processes, including oncogenesis. OIP5-AS1 exerts its oncogenic or antitumor functions via regulation of different miRNAs in various cancer types. In this review, we describe the dysregulation of OIP5-AS1 expression in a variety of human cancers. Moreover, we discuss the multiple functions of OIP5-AS1 in cancer, including in proliferation, apoptosis, autophagy, ferroptosis, cell cycle, migration, metastasis, invasion, epithelial to mesenchymal transition, angiogenesis, cancer stem cells and drug resistance. Furthermore, we provide a future perspective for OIP5-AS1 research. We conclude that targeting OIP5-AS1 might be a promising cancer therapy approach.

Comprehensive Analysis of Immune Infiltrates of Ferroptosis-Related Long Noncoding RNA and Prediction of Colon Cancer Patient Prognoses

Ferroptosis is a newly defined mode of programmed oxidative cell death. Knowledge of ferroptosis-related long noncoding (lnc) RNA in the tumor immune microenvironment of colon cancer is lacking. We systematically analyzed the correlations between ferroptosis-related lncRNAs and the tumor microenvironment, immune cell infiltration, and patient prognosis for 379 colon cancer samples in the Cancer Genome Atlas (TCGA). Using consensus clustering, we divided the 379 colon cancer patients into two subgroups (clusters 1 and 2) based on the differentially expressed ferroptosis-related lncRNAs. Cluster 1 was preferentially associated with longer overall survival, upregulated immune checkpoint inhibitor expressions, higher immunoscores, higher stromal scores, higher estimated scores, and distinct immune cell infiltration. Cancer- and metabolism-related pathways were enriched by gene set enrichment analyses. We constructed a prognostic signature of 15 ferroptosis-related lncRNAs (ZEB1-AS1, LINC01011, AC005261.3, LINC01063, LINC02381, ELFN1-AS1, AC009283.1, LINC02361, AC105219.1, AC002310.1, AL590483.1, MIR4435-2HG, NKILA, AC021054.1, and AL450326.1) and divided the patients into the high- and low-risk-score groups. The signature was validated using TCGA training and testing cohorts. The risk signature was an independent prognostic factor for predicting survival and excellently predicted the prognoses of patients with colon cancer. Moreover, the risk signature was related to immune characteristics. Chemosensitivity analyses showed that low-risk-score patients were more sensitive to sorafenib. In summary, our work revealed the important role of ferroptosis-related lncRNAs in the tumor microenvironment and immune cell infiltration and may help determine personalized prognoses and treatment for patients with colon cancer.

Identification of Five Ferroptosis-Related LncRNAs as Novel Prognosis and Diagnosis Signatures for Renal Cancer

Background: Ferroptosis is a novel regulated cell death that is characterized by iron-dependent oxidative damage. Renal cancer is the second most common cancer of the urinary system, which is highly correlated with iron metabolism. The aim of our present study was to identify suitable ferroptosis-related prognosis signatures for renal cancer.

Methods: We downloaded the RNA-seq count data of renal cancer from The Cancer Genome Atlas database and used the DESeq2, Survival, and Cox regression analyses to screen the prognosis signatures.

Results: We identified 5 ferroptosis-related differentially expressed lncRNAs (FR-DELs) (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) to be independently correlated with the overall survival (OS) of patients with renal cancer. The risk assessment model and diagnosis model constructed by those 5 FR-DELs could well predict the outcome and the diagnosis of renal cancer.

Conclusion: Our present study not only suggested those 5 FR-DELs could be used as prognosis and diagnosis signatures for renal cancer but also provided strategies for other cancers in the screening of ferroptosis-related biomarkers.

Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

Bladder cancer (BLCA) is one of the highly heterogeneous disorders accompanied by a poor prognosis. The present study aimed to construct a model based on pyroptosis-related long-stranded non-coding RNA (lncRNA) to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis-related lncRNAs were identified by utilizing a co-expression network of pyroptosis-related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, eight pyroptosis-related lncRNA markers were established using least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox regression analyses. Patients were separated into high- and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (P<0.001). In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS (HR > 1, P<0.01). The areas under the curve (AUCs) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739, respectively. In conclusion, these eight pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

Long Non-Coding RNA Signatures Associated with Ferroptosis Predict Prognosis in Colorectal Cancer

Background

Currently, colorectal cancer has become a common gastrointestinal malignancy that usually occurs in the colon and rectum, and ferroptosis plays a vital role in the pathology and progression of colorectal tumors.

Methods

A total of 627 patients (51 normal and 644 tumor samples) from The Cancer Genome Atlas (TCGA)-COAD and TCGA-READ were included in the study. Lasso and Cox’s regression was employed to analyze the characteristic lncRNAs in colorectal cancer samples, and a distinctive prognostic model of ferroptosis-related lncRNAs was established. By analyzing the divergence between the high and low-risk groups of ferroptosis-related lncRNAs, 15 characteristic lncRNAs related to the prognosis of colorectal cancer were evaluated. Kaplan–Meier analysis, operation characteristic curve (ROC), nomogram, and gene set enrichment analyses (GSEA) further confirmed the validity of the characteristic prognostic model with ferroptosis-related lncRNAs.

Results

Kaplan–Meier analysis confirmed a high-risk group of ferroptosis-related lncRNA interrelated with a poor prognosis in colorectal cancer. AUC estimates of 1 -, 3 -, and 5-year survival rates for ferroptosis-related lncRNA characteristic models were 0.745, 0.767 and 0.789. GSEA analysis showed that immune and malignancy-related pathways were active in the high-risk score group. In addition, differential analyses of immune function, including Checkpoint, cytolytic, HLA, and T cell co-inhibition, differed significantly betwixt low - and high-risk groups.CD160, TNFRSF18, CD27, PDCD1, CD200R1, ADORA2A, TNFRSF14, LAIR1, CD244, CD40, TNFRSF4, CD70, TNFSF14, TNFRSF25, CD276, HHLA2, VTCN1, LAG3, TNFSF18, and other immune checkpoints had different expressions betwixt the high- and low-risk group.

Conclusion

Fifteen kinds of lncRNAs with different expressions (AP003555.1, AC099850.3, AL031985.3, LINC01857, STPG3-AS1, AL137782.1, AC124067.4, AC012313.5, AC083900.1, AC010973.2, ALMS1-IT1, AC013652.1, AC133540.1, AP006621.2, AC018653.3) were closely associated with poor prognosis of colorectal cancer. These indicators were significantly correlated with the overall survival (OS) rate and could be used as prognostic evaluation criteria.

An immune-related lncRNA signature predicts prognosis and adjuvant chemotherapeutic response in patients with small-cell lung cancer

Background

Patients with small-cell lung cancer (SCLC) are burdened by limited treatment options and the disease’s dismal prognosis. Long non-coding RNAs (lncRNAs) are essential regulators of genetic alteration and are actively involved in tumor immunity. However, few studies have examined interactions between immune genes and lncRNAs in SCLC.

Methods

Immune-related lncRNA (irlncRNA) expression profiles and their clinical significance were explored. We enrolled 227 patients with SCLC, including 79 cases from GSE65002 and 148 cases from a validation cohort with corresponding qPCR data. The least absolute shrinkage and selection operator (LASSO) model was applied to identify prognostic irlncRNAs for an irlncRNA-based SCLC signature. We additionally investigated the potential mechanisms and immune landscape of the signature using bioinformatics methods.

Results

An irlncRNA signature including 8 irlncRNAs (ENOX1-AS1, AC005162, LINC00092, RPL34-AS1, AC104135, AC015971, AC126544, AP001189) was established for patients with SCLC in the training cohort. Low-risk patients were more likely to benefit from chemotherapy and achieve a favorable prognosis. The signature was also well-validated in the validation cohort and various clinical subgroups. Compared to other clinical parameters, the irlncRNA signature exhibited superior predictive performance for chemotherapy response and prognosis. The signature was as an independent prognostic factor in the training and validation cohorts. Interestingly, low-risk patients showed an activated immune phenotype.

Conclusion

We constructed the first irlncRNA-based signature for chemotherapy efficacy and outcome prediction. The irlncRNA signature is a reliable and robust prognostic classifier that could be useful for clinical management and determination of potential chemotherapy benefit for patients with SCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02357-1.

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

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.

Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.

Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.

Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

Identification of the ferroptosis-related long non-coding RNAs signature to improve the prognosis prediction and immunotherapy response in patients with NSCLC

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with an unfavorable prognosis. Ferroptosis is involved in the development of multiple cancers. Whereas, the prognostic value of ferroptosis-related lncRNAs in NSCLC remains uncertain.

METHODS

Gene expression profiles and clinical information of NSCLC were retrieved from the TCGA database. Ferroptosis-related genes (FRGs) were explored in the FerrDb database and previous studies, ferroptosis-related lncRNAs (FRGs-lncRNAs) were identified by the correlation analysis and the LncTarD database. The differentially expressed FRGs-lncRNAs were screened and FRGs-lncRNAs associated with the prognosis were explored by univariate Cox regression analysis and Kaplan-Meier survival analysis. Then, an FRGs-lncRNAs signature was constructed and verified by the Lasso-penalized Cox analysis. Finally, the potential correlation between risk score, immune checkpoint genes, and chemotherapeutic sensitivity was further investigated.

RESULTS

129 lncRNAs with a potential regulatory relationship with 59 differentially expressed FRGs were found in NSCLC, of which 10 were related to the prognosis of NSCLC (P < 0.05). 9 prognostic-related FRGs-lncRNAs were used to construct the prognostic model and stratify NSCLC patients into high- and low-risk groups. A worse outcome was found in patients with high risk (P < 0.05). Moreover, a good predictive capacity of this signature in predicting NSCLC prognosis was confirmed. Additionally, 45 immune checkpoint genes and 4 chemotherapeutics drugs for NSCLC were identified to be correlated with the risk score.

CONCLUSION

A novel FRGs-lncRNAs signature was successfully constructed, which may contribute to improving the management strategies of NSCLC.

A four immune-related long noncoding RNAs signature as predictors for cervical cancer

The progression, metastasis, and prognosis of cervical cancer (CC) is influenced by the tumor immune microenvironment. Studies proved that long non-coding RNAs (lncRNAs) to engage in cervical cancer development, especially immune-related lncRNAs, have emerged crucial in the tumor immune process. This study was set out to identify an immune-related lncRNA signature. In total, 13,838 lncRNA expression profiles and 328 immune genes were acquired from the clnical data of 306 CC tissues and 3 non-CC tissues. From the 433 identified immune-related lncRNAs, 4 candidate immune-related lncRNAs (SOX21-AS1, AC005332.4, NCK1-DT, LINC01871) were considered independent indicators of cervical cancer prognosis through the univariate and multivariate Cox regression analysis, and they were used to construct a prognostic and survival lncRNA signature model followed by the bootstrap method for further verification. Kaplan-Meier curves illustrated that cervical cancer patients could be divided into high-risk and low-risk groups with significant differences (P = 2.052e - 05), and the discrepancy of immune profiles between these two risk groups was illustrated by principal components analysis. Taken together, the novel survival predictive model created by the four immune-related lncRNAs showed promising clinical prediction value in cervical cancer.

Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

Ferroptosis: A Double-Edged Sword in Gastrointestinal Disease

Ferroptosis is a novel form of regulated cell death (RCD) that is typically accompanied by iron accumulation and lipid peroxidation. In contrast to apoptosis, autophagy, and necroptosis, ferroptosis has unique biological processes and pathophysiological characteristics. Since it was first proposed in 2012, ferroptosis has attracted attention worldwide. Ferroptosis is involved in the progression of multiple diseases and could be a novel therapeutic target in the future. Recently, tremendous progress has been made regarding ferroptosis and gastrointestinal diseases, including intestinal ischemia/reperfusion (I/R) injury, inflammatory bowel disease (IBD), gastric cancer (GC), and colorectal cancer (CRC). In this review, we summarize the recent progress on ferroptosis and its interaction with gastrointestinal diseases. Understanding the role of ferroptosis in gastrointestinal disease pathogenesis could provide novel therapeutic targets for clinical treatment.

Prognostic Value of Immune-Related Multi-IncRNA Signatures Associated With Tumor Microenvironment in Esophageal Cancer

Esophageal cancer is the eighth most common cancer and the sixth leading cause of cancer death worldwide. Hence, for a better understanding of tumor microenvironment and to seek for novel molecular targets for esophageal cancer, we performed related studies on two histopathological subtypes of esophageal cancer: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Bioinformatic analyses were conducted based on the RNA-seq, genomic mutation, and clinical data from TCGA and GEO cohorts. We clustered patients into high-immunity and low-immunity groups through the ssGSEA results. The ESTIMATE algorithm was used to evaluate the tumor microenvironment. Patients with high immunity in both ESCC and EAC had lower tumor purity and poor survival. Subsequently, CIBERSORT was performed to learn about the detailed difference of tumor-infiltrating lymphocytes (TILs) between high- and low-immunity patients. Specific increase of M2 macrophages and decrease of activated dendric cells can be observed in ESCC and EAC, respectively. The most enriched functions and pathways of high-immunity patients were immunoglobulin complex, MHC class II protein complex, and allograft rejection according to the GO terms and KEGG. Two prognostic immune-related multi-lncRNA risk models were constructed and validated by ROC curve and PCA in ESCC and EAC. High-risk patients in both subtypes had poor survival, advanced clinical characteristics, and higher drug susceptibility except cisplatin and sorafenib. In addition, the tumor mutation burden (TMB) was positively correlated with the risk level in the ESCC and EAC and showed distinct differences between the two subtypes. In summary, we comprehensively analyzed the tumor microenvironment for two subtypes of esophageal cancer, identified two multi-lncRNA signatures predictive for the prognosis, and explored the possibility of the signatures to forecast drug susceptibility as well as TMB for the first time. The findings may serve as a conceptual basis for innovative strategy of individualized immunotherapy for esophageal cancer.

Molecular subtypes based on ferroptosis-related genes and tumor microenvironment infiltration characterization in lung adenocarcinoma

Recently, several molecular subtypes with different prognosis have been found in lung adenocarcinoma (LUAD). However, the characteristics of the ferroptosis molecular subtypes and the associated tumor microenvironment (TME) cell infiltration have not been fully studied in LUAD. Using 1160 lung adenocarcinoma samples, we explored the molecular subtypes mediated by ferroptosis-related genes, along with the associated TME cell infiltration. The ferroptosis score was constructed using the least absolute shrinkage and selection operator regression (LASSO) method to quantify the ferroptosis characteristics of a single tumor. Three different molecular subtypes related to ferroptosis, with different prognoses, were identified in LUAD. Analysis of TME cell infiltration revealed immune heterogeneity among the three subtypes. Cluster A was characterized by immunosuppression and was associated with stromal activation. Cluster C was characterized by a large number of immune cells infiltrating the TME, promoting tumor immune response, and it was significantly enriched in immune activation-related signaling pathways. Relatively less infiltration of immune cells was a feature of cluster B. The ferroptosis score can predict tumor subtype, immunity and prognosis. A low ferroptosis score was characterized by immune activation and good prognosis, as seen in the cluster C subtype. Relative immunosuppression and poor prognosis were the characteristics of a high ferroptosis score, as seen in cluster A and B subtypes. At the same time, the anti-PD-1/L1 immunotherapy cohort demonstrated that a low ferroptosis score was associated with higher efficacy of immunotherapy. The ferroptosis score is a promising biomarker that could be of great significance to determine the prognosis, molecular subtypes, TME cell infiltration characteristics and immunotherapy effects in patients with LUAD.