Construction and Validation of a Novel Ferroptosis-Related lncRNA Signature to Predict Prognosis in Colorectal Cancer Patients

Establishment of a circRNA-regulated E3 ubiquitin ligase signature and nomogram to predict immunotherapeutic efficacy and prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common type of malignant tumor where the prognosis is dismal. Circular RNA (CircRNA) is a novel RNA that regulates downstream gene transcription and translation to influence the progression of HCC. However, the regulatory relationship that exists between E3 ligases, which is a class of post-translational modifying proteins, and circRNA remains unclear.

METHODS

Based on the E3 ubiquitin ligase in the competitive endogenous RNA (ceRNA) network, a circRNA-regulated E3 ubiquitin ligase signature (CRE3UL) was developed. A CRE3UL signature was created using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis and merged it with clinicopathologic characteristics to generate a nomogram for prognosis prediction. The pRRophetic algorithm was utilized and immunological checkpoints were analyzed to compare the responses of patients in the high-risk group (HRG) and low-risk group (LRG) to targeted therapy and immunotherapy. Finally, experimental research will further elucidate the relationship between E3 ubiquitin ligase signature and HCC.

RESULTS

HRG patients were found to have a worse prognosis than LRG patients. Furthermore, significant variations in prognosis were observed among different subgroups based on various clinical characteristics. The CRE3UL signature was identified as being an independent prognostic indicator. The nomogram that combined clinical characteristics and the CRE3UL signature was found to accurately predict the prognosis of HCC patients and demonstrated greater clinical utility than the current TNM staging approach. According to anticancer medication sensitivity predictions, the tumors of HRG patients were more responsive to gefitinib and nilotinib. From immune-checkpoint markers analysis, immunotherapy was identified as being more probable to assist those in the HRG.

CONCLUSIONS

We found a significant correlation between the CRE3UL signature and the tumor microenvironment, enabling precise prognosis prediction for HCC patients. Additionally, a nomogram was developed that performs well in predicting the overall survival (OS) of HCC patients. This provides valuable guidance for clinicians in devising specific personalized treatment strategies.

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.

Role of long non-coding RNA ELFN1-AS1 in carcinogenesis

As one of the leading causes of death worldwide, cancer significantly burdens patients and the healthcare system. The role of long non-protein coding RNAs (lncRNAs) in carcinogenesis has been extensively studied. The lncRNA ELFN1-AS1 was discovered recently, and subsequent studies have revealed its aberrantly high expression in various cancer tissues. In vitro and in vivo experiments have consistently demonstrated the close association between increased ELFN1-AS1 expression and malignant tumor characteristics, particularly in gastrointestinal malignancies. Functional assays have further revealed the mechanistic role of ELFN1-AS1 as a competitive endogenous RNA for microRNAs, inducing tumor growth, invasive features, and drug resistance. Additionally, the investigation into the clinical implication of ELFN1-AS1 has demonstrated its potential as a diagnostic, therapeutic, and, notably, prognostic marker. This review provides a comprehensive summary of evidence regarding the involvement of ELFN1-AS1 in cancer initiation and development, highlighting its clinical significance.

LncRNA OTUD6B-AS1 overexpression promoted GPX4-mediated ferroptosis to suppress radioresistance in colorectal cancer

BACKGROUND

Radiotherapy is widely employed in colorectal cancer (CRC) treatment but is often compromised by developed radioresistance. This study explored the mechanism of long non-coding RNA ovarian tumor domain containing 6B-antisense RNA1 (lncRNA OTUD6B-AS1) in CRC radioresistance through tripartite motif 16 (TRIM16).

METHODS

CRC and non-cancerous tissues were collected and radioresistant CRC cells were established, with real-time quantitative polymerase chain reaction to determine gene expression in tissues and cells. Radioresistance was evaluated by cell counting kit-8 assay and immunofluorescence (γ-H2AX) and ferroptosis was tested by Western blot assay (ACSL4/GPX4) and assay kits (Fe2+/ROS/MDA/GSH). The association between ferroptosis and lncRNA OTUD6B-AS1-inhibited radioresistance was testified using ferroptosis inhibitor. The subcellular localization of lncRNA OTUD6B-AS1 was tested by the nuclear/cytoplasmic fractionation assay, with RNA immunoprecipitation assay to validate gene interactions. Rescue experiments were conducted to analyze the role of TRIM16 in CRC radioresistance.

RESULTS

LncRNA OTUD6B-AS1 and TRIM16 were poorly expressed (P < 0.01) in CRC tissues and cells and further decreased (P < 0.01) in radioresistant CRC cells. OTUD6B-AS1 overexpression decreased cell survival (P < 0.01), increased γ-H2AX levels (P < 0.01), and elevated ferroptosis and oxidative stress (P < 0.01) after X-ray radiation. Ferroptosis inhibitor attenuated radioresistance (P < 0.01) caused by lncRNA OTUD6B-AS1 overexpression. LncRNA OTUD6B-AS1 stabilized TRIM16 mRNA via binding to HuR. TRIM16 knockdown reduced ferroptosis and increased radioresistance (P < 0.05).

CONCLUSION

OTUD6B-AS1 overexpression stabilized TRIM16 via binding to HuR and increased GPX4-mediated ferroptosis, thus attenuating CRC radioresistance. Our study provided a new rationale for the treatment of CRC.

Tumor-derived exosome ElNF1-AS1 affects the progression of gastric cancer by promoting M2 polarization of macrophages

BACKGROUND

This study attempted to investigate the role of exosome ELFN1-AS1 in gastric cancer (GC).

METHODS

The study used various techniques to determine the level of exosomal ELFN1-AS1 in GC tissue and cells, including quantitative real-time PCR. Pull-down assay and dual-luciferase reporter assay were employed to identify interactions between ELFN1-AS1 and miR-4644, as well as miR-4644 and PKM. Western blot was employed to explore the potential regulatory mechanism. Several in vitro assays were conducted in xenograft models to investigate the impacts of exosomal ELFN1-AS1 on GC development, metastasis and macrophage polarization.

RESULTS

ELFN1-AS1 was upregulated in GC tissue and cells, with high enrichment in GC-derived exosomes. Exosomal ELFN1-AS1 enhances the cell abilities and stemness of GC. ELFN1-AS1 targeted and regulated miR-4644, which triggered PKM expression. Exosomal ELFN1-AS1 modulated glycolysis via PKM in an HIF-1α dependent manner in GC, promoting M2 polarization and macrophage recruitment. Furthermore, exosomal ELFN1-AS1 enhanced GC cell growth, metastasis and M2 polarization in vivo.

CONCLUSIONS

The study suggests that ELFN1-AS1 could be a promising biomarker for the diagnosis and treatment of GC.

Based on cuproptosis-related lncRNAs, a novel prognostic signature for colon adenocarcinoma prognosis, immunotherapy, and chemotherapy response

Introduction: Colon adenocarcinoma (COAD) is a special pathological subtype of colorectal cancer (CRC) with highly heterogeneous solid tumors with poor prognosis, and novel biomarkers are urgently required to guide its prognosis. Material and methods: RNA-Seq data of COAD were downloaded through The Cancer Genome Atlas (TCGA) database to determine cuproptosis-related lncRNAs (CRLs) using weighted gene co-expression network analysis (WGCNA). The scores of the pathways were calculated by single-sample gene set enrichment analysis (ssGSEA). CRLs that affected prognoses were determined via the univariate COX regression analysis to develop a prognostic model using multivariate COX regression analysis and LASSO regression analysis. The model was assessed by applying Kaplan-Meier (K-M) survival analysis and receiver operating characteristic curves and validated in GSE39582 and GSE17538. The tumor microenvironment (TME), single nucleotide variants (SNV), and immunotherapy response/chemotherapy sensitivity were assessed in high- and low-score subgroups. Finally, the construction of a nomogram was adopted to predict survival rates of COAD patients during years 1, 3, and 5. Results: We found that a high cuproptosis score reduced the survival rates of COAD significantly. A total of five CRLs affecting prognosis were identified, containing AC008494.3, EIF3J-DT, AC016027.1, AL731533.2, and ZEB1-AS1. The ROC curve showed that RiskScore could perform well in predicting the prognosis of COAD. Meanwhile, we found that RiskScore showed good ability in assessing immunotherapy and chemotherapy sensitivity. Finally, the nomogram and decision curves showed that RiskScore would be a powerful predictor for COAD. Conclusion: A novel prognostic model was constructed using CRLs in COAD, and the CRLs in the model were probably a potential therapeutic target. Based on this study, RiskScore was an independent predictor factor, immunotherapy response, and chemotherapy sensitivity for COAD, providing a new scientific basis for COAD prognosis management.

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 and validation of a novel algorithm based on oncosis-related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis

Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis-related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis-related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut-off point was subsequently used to divide the samples into groups labelled as high- or low-risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high- and low-risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity.

Ferroptosis: Reviewing CRC with the Third Eye

Colorectal cancer (CRC) has been one of the most common cancers and maintains the second-highest incidence and mortality rates among all cancers. The high risk of recurrence and metastasis and poor survival are still huge challenges in CRC therapy, in which the discovery of ferroptosis provides a novel perspective. It has been ten years since a unique type of regulated cell death driven by iron accumulation and lipid peroxidation was proposed and named ferroptosis. During the past decade, there have been multiple pieces of evidence suggesting that ferroptosis participates in the pathophysiological processes during disease progression. In this review, we describe ferroptosis as an imbalance of oxidant systems and anti-oxidants which results in lipid peroxidation, membrane damage, and finally cell death. We elaborate on the mechanisms of ferroptosis and systematically summarize recent studies on the regulatory pathways of ferroptosis in CRC from various perspectives, ranging from encoding genes, noncoding RNAs to regulatory proteins. Finally, we discuss the potential therapeutic role of ferroptosis in CRC treatments.

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.

Construction and verification of a novel hypoxia-related lncRNA signature related with survival outcomes and immune microenvironment of bladder urothelial carcinoma by weighted gene co-expression network analysis

Background: Bladder urothelial carcinoma (BLCA) is a common malignant tumor with the greatest recurrence rate of any solid tumor. Hypoxia is crucial in the growth and immune escape of malignant tumors. To predict clinical outcomes and immunological microenvironment of patients with BLCA, a hypoxia-related long non-coding RNA (HRlncRNA) signature was established.

Methods: The Cancer Genome Atlas (TCGA) provided us with the differentially expressed profile of HRlncRNAs as well as clinical data from patients with BLCA, and we used weighted gene co-expression network analysis (WGCNA) to identify gene modules associated with malignancies.

Results: Finally, Cox analysis revealed that HRlncRNAs, which comprised 13 lncRNAs, were implicated in the predictive signature. The training, testing, and overall cohorts of BLCA patients were divided into the low-risk group and high-risk group based on the median of the risk score. The Kaplan–Meier curves revealed that BLCA patients with a high-risk score had a poor prognosis, and the difference between subgroups was statistically significant. The receiver operating characteristic curves revealed that this signature outperformed other strategies in terms of predicting ability. Multivariate analysis revealed that the risk score was an independent prognostic index for overall survival (HR = 1.411; 1.259–1.582; p < 0.001). Then, a nomogram with clinicopathological features and risk score was established. This signature could effectively enhance the capacity to predict survival, according to the calibration plots, stratification, and clinical analysis. The majority of Kyoto Encyclopedia of Genes and Genomes (KEGG) were WNT, MAPK, and ERBB signaling pathways. Two groups had different immune cell subtypes, immune checkpoints, immunotherapy response, and anti-tumor drug sensitivity, which might result in differing survival outcomes. We then validated the differential expression of signature-related genes between tumor and normal tissues using TCGA paired data.

Conclusion: This prognostic signature based on 13 HRlncRNAs may become a novel and potential prognostic biomarker, providing more accurate clinical decision-making and effective treatment for BLCA patients.

Establishment of a circular RNA regulatory stemness-related gene pair signature for predicting prognosis and therapeutic response in colorectal cancer

Background

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract with a poor prognosis. Cancer stem cells (CSCs) affect disease outcomes and treatment responses in CRC. We developed a circular RNA (circRNA) regulatory stemness-related gene pair (CRSRGP) signature to predict CRC patient prognosis and treatment effects.

Methods

The circRNA, miRNA, and mRNA expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CRSRGPs were established based on stemness-related genes in the competing endogenous RNA (ceRNA) network. A CRSRGP signature was generated using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis of TCGA training set. The prognosis was predicted by generating a nomogram integrating the CRSRGP signature and clinicopathologic features. The model was validated in an external validation set (GSE17536). The antitumor drug sensitivity and immunotherapy responses of CRC patients in the high-risk group (HRG) and low-risk group (LRG) were evaluated by the pRRophetic algorithm and immune checkpoint analysis.

Results

We established an 18-CRSRGP signature to predict the prognosis and treatment responses of CRC patients. In the training and external validation sets, risk scores were used to categorize CRC patients into the HRG and LRG. The Kaplan–Meier analysis showed a poor prognosis for patients in the HRG and that subgroups with different clinical characteristics had significantly different prognoses. A multivariate Cox analysis revealed that the CRSRGP signature was an independent prognostic factor. The nomogram integrating clinical features and the CRSRGP signature efficiently predicted CRC patient prognosis, outperformed the current TNM staging system, and had improved practical clinical value. Anticancer drug sensitivity predictions revealed that the tumors of patients in the HRG were more sensitive to pazopanib, sunitinib, gemcitabine, lapatinib, and cyclopamine. Analysis of immune checkpoint markers demonstrated that patients in the HRG were more likely to benefit from immunotherapy.

Conclusion

An efficient, reliable tool for evaluating CRC patient prognosis and treatment response was established based on the 18-CRSRGP signature and nomogram.

Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma

Background

Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).

Methods

A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.

Results

In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.

Conclusion

The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients’ immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

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.

The m7G-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumour Immune Infiltration in Colon Cancer

With high morbidity and mortality, colon cancer (CC) is considered as one of the most often diagnosed cancers around the world. M7G-related lncRNA may provide a regulatory function in the formation of CC, but the principle of regulation is still unclear. The purpose of this research was to establish a novel signature that may be used to predict survival and tumour immunity in CC patients. Data about CC in TCGA was collected for analysis, coexpression analysis and univariate Cox analysis were used to screen prognostic m7G-related lncRNAs. A consensus clustering analysis based on prognostic m7G-related lncRNAs was applied, and a prognosis model based on least absolute shrinkage and selection operator (LASSO) regression analysis was established. Independent prognostic analysis, nomogram, PCA, clinicopathological correlation analysis, TMB, survival analysis, immune correlation analysis, qRT–PCR and clinical therapeutic compound prediction were also applied. 90 prognostic m7G-related lncRNAs were found, GO and KEGG analysis showed that prognostic m7G-related lncRNAs were mainly related to cell transcription and translation. The results of the consensus clustering analysis revealed substantial disparities in survival prognosis and tumour immune infiltration between two clusters. We built a risk model with 21 signature m7G-related lncRNAs, patients in the high-risk group had a considerably poorer prognosis than those in the low-risk group. Independent prognostic analysis confirmed that patients’ prognosis was linked to their tumour stage and risk score. PCA, subgroups with distinct clinicopathological characteristics were studied for survival, multi-index ROC curve, c-index curve, the survival analysis of TMB, and model comparison tested the reliability of risk model. A tumour immunoassay revealed a substantial difference in immune infiltration between high-risk and low-risk individuals. Five chemicals were eliminated, and qRT–PCR indicated that the four lncRNAs were expressed differently. Overall, m7G-related lncRNA is closely related to colon cancer and the 21 signature lncRNAs risk model can efficiently evaluate the prognosis of CC patients, which has a possible positive consequence for the future diagnosis and therapy of CC.

Integrated Analysis of a Ferroptosis-Related LncRNA Signature for Evaluating the Prognosis of Patients with Colorectal Cancer

LncRNAs have been well known for their multiple functions in the tumorigenesis, development, and relapse of colorectal cancer (CRC). Accumulating studies demonstrated that the expression of lncRNAs can be regulated by ferroptosis, a biological process that has been revealed to suppress CRC progression. However, the functions and clinical implications of ferroptosis-associated lncRNAs in CRC remain largely unknown. We, herein, aim to construct a prognostic signature with ferroptosis-related lncRNAs for the prognostic estimation of CRC patients. Firstly, we identified the lncRNAs related to ferroptosis based on the RNA-Seq data of CRC from the TCGA database. The univariate and multivariate Cox analyses were then performed to establish a prognostic signature composed of eight ferroptosis-related lncRNAs (AL161729.4, AC010973.2, CCDC144NL-AS1, AC009549.1, LINC01857, AP003555.1, AC099850.3, and AC008494.3). Furthermore, we divided the CRC patients into high- and low-risk groups based on the signature and found the overall survival (OS) of patients in the high-risk group was significantly shorter than that in the low-risk group (p = 3.31 × 10-11). Moreover, the patients in the high-risk groups had shorter recurrence-free survival (RFS) (p = 6.5 × 10-3) and disease-free survival (DFS) (p = 4.27 × 10-4), as well as higher tumor recurrence rate. Additionally, we found that the oncogenic pathways were enriched in the high-risk group, whereas the ferroptosis pathway that probably repressed CRC development was enriched in the low-risk group. In summary, our signature may provide a theoretical foundation for not only accurate judgment for prognosis but also evaluation for recurrence and metastasis in CRC patients.

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

Background

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

Methods

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

Results

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

Conclusion

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

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.

Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).

Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.

Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.

Identification of RNA Methylation-Related lncRNAs Signature for Predicting Hot and Cold Tumors and Prognosis in Colon Cancer

N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), and 7-methylguanosine (m7G) are the major forms of RNA methylation modifications, which are closely associated with the development of many tumors. However, the prognostic value of RNA methylation-related long non-coding RNAs (lncRNAs) in colon cancer (CC) has not been defined. This study summarised 50 m6A/m1A/m5C/m7G-related genes and downloaded 41 normal and 471 CC tumor samples with RNA-seq data and clinicopathological information from The Cancer Genome Atlas (TCGA) database. A total of 1057 RNA methylation-related lncRNAs (RMlncRNAs) were identified with Pearson correlation analysis. Twenty-three RMlncRNAs with prognostic values were screened using univariate Cox regression analysis. By consensus clustering analysis, CC patients were classified into two molecular subtypes (Cluster 1 and Cluster 2) with different clinical outcomes and immune microenvironmental infiltration characteristics. Cluster 2 was considered to be the “hot tumor” with a better prognosis, while cluster 1 was regarded as the “cold tumor” with a poorer prognosis. Subsequently, we constructed a seven-lncRNA prognostic signature using the least absolute shrinkage and selection operator (LASSO) Cox regression. In combination with other clinical traits, we found that the RNA methylation-related lncRNA prognostic signature (called the “RMlnc-score”) was an independent prognostic factor for patients with colon cancer. In addition, immune infiltration, immunotherapy response analysis, and half-maximum inhibitory concentration (IC50) showed that the low RMlnc-score group was more sensitive to immunotherapy, while the high RMlnc-score group was sensitive to more chemotherapeutic agents. In summary, the RMlnc-score we developed could be used to predict the prognosis, immunotherapy response, and drug sensitivity of CC patients, guiding more accurate, and personalized treatment regimens.

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

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

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

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

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

Comprehensive Analysis of Necroptosis-Related Long Noncoding RNA Immune Infiltration and Prediction of Prognosis in Patients With Colon Cancer

Colon cancer (CC) is one of the most frequent malignancies in the world, with a high rate of morbidity and death. In CC, necroptosis and long noncoding RNA (lncRNAs) are crucial, but the mechanism is not completely clear. The goal of this study was to create a new signature that might predict patient survival and tumor immunity in patients with CC. Expression profiles of necroptosis-related lncRNAs in 473 patients with CC were retrieved from the TCGA database. A consensus clustering analysis based on differentially expressed (DE) genes and a prognostic model based on least absolute shrinkage and selection operator (LASSO) regression analysis were conducted. Clinicopathological correlation analysis, expression difference analysis, PCA, TMB, GO analysis, KEGG enrichment analysis, survival analysis, immune correlation analysis, prediction of clinical therapeutic compounds, and qRT–PCR were also conducted. Fifty-six necroptosis-related lncRNAs were found to be linked to the prognosis, and consensus clustering analysis was performed. There were substantial variations in survival, immune checkpoint expression, clinicopathological correlations, and tumor immunity among the different subgroups. Six lncRNAs were discovered, and patients were split into high-risk and low-risk groups based on a risk score generated using these six lncRNAs. The survival time of low-risk patients was considerably longer than that of high-risk patients, indicating that these lncRNAs are directly associated with survival. The risk score was associated with the tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. After univariate and multivariate Cox regression analysis, the risk score and tumor stage remained significant. Cancer- and metabolism-related pathways were enriched by KEGG analyses. Immune infiltration was shown to differ significantly between high- and low-risk patients in a tumor immunoassay. Eight compounds were screened out, and qRT–PCR confirmed the differential expression of the six lncRNAs. Overall, in CC, necroptosis-related lncRNAs have an important function, and the prognosis of patients with CC can be predicted by these six necroptosis-related lncRNAs. They may be useful in the future for customized cancer therapy.

Bioinformatic Analyses of the Ferroptosis-Related lncRNAs Signature for Ovarian Cancer

Both ferroptosis and lncRNAs are significant for ovarian cancer (OC). Whereas, the study of ferroptosis-related lncRNAs (FRLs) still few in ovarian cancer. We first constructed an FRL-signature for patients with OC in the study. A total of 548 FRLs were identified for univariate Cox regression analysis, and 21 FRLs with significant prognosis were identified. The prognostic characteristics of nine FRLs was constructed and validated, showing opposite prognosis in two subgroups based on risk scores. The multivariate Cox regression analysis and nomogram further verified the prognostic value of the risk model. By calculating ferroptosis score through ssGSEA, we found that patients with higher risk scores exhibited higher ferroptosis scores, and high ferroptosis score was a risk factor. There were 40 microenvironment cells with significant differences in the two groups, and the difference of Stromal score between the two groups was statistically significant. Six immune checkpoint genes were expressed at different levels in the two groups. In addition, five m6A regulators (FMR1, HNRNPC, METTL16, METTL3, and METTL5) were higher expressed in the low-risk group. GSEA revealed that the risk model was associated with tumor-related pathways and immune-associated pathway. We compared the sensitivity of chemotherapy drugs between the two risk groups. We also explored the co-expression, ceRNA relation, cis and trans interaction of ferroptosis-related genes and lncRNAs, providing a new idea for the regulatory mechanisms of FRLs. Moreover, the nine FRLs were selected for detecting their expression levels in OC cells and tissues.