A Prognostic Ferroptosis-Related lncRNAs Signature Associated With Immune Landscape and Radiotherapy Response in Glioma

Identification of a ferroptosis-related prognostic signature and validation of ITGA6-AS1 in enhancing cell proliferation, migration and invasion in glioma

Glioma is the most common malignant tumor of the adult central nervous system. In this study, we aimed to identify a novel model for predicting glioma prognosis and a potential therapeutic target. Here, lncRNAs related to prognosis and ferroptosis were analyzed and screened through R software and online websites. A nomogram model was established and evaluated with calibration curve, receiver operating characteristic curve and decision curve analysis. Further, an enrichment analysis and immune infiltration analysis were performed. In addition, the expression level and biological function of ITGA6-AS1 were verified in vitro. We obtained a ferroptosis-related 7-lncRNA signature, and constructed a nomogram prognostic model with good predictability for 1-, 3- and 5-year overall survival of glioma patients. The enrichment analysis indicated potential involvement of certain pathways and suggested a correlation between the high-risk group and infiltration of M2 macrophages and MDSCs. Furthermore, the expression level of ITGA6-AS1 in the U118, U87, and LN229 cells was upregulated compared to the H1800 cell. Interestingly, knockdown of ITGA6-AS1 may inhibit U118 cells' proliferation, migration and invasion in vitro. while overexpression of ITGA6-AS1 in LN229 cells plays a promoting role. This study implies that the 7-lncRNA signature may contribute to the stratification of glioma prognosis, and the immune suppressive microenvironment may be associated with macrophage-ferroptosis crosstalk. Furthermore, ITGA6-AS1 may be a potential therapeutic target for patients with glioma.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

Bioinformatic analysis and experimental validation of cuproptosis-related LncRNA as a novel biomarker for prognosis and immunotherapy of oral squamous cell carcinoma

BACKGROUND

The novel form of regulatory cell death, cuproptosis, is characterized by proteotoxicity, which ultimately leads to cell death. Its targeting has emerged as a promising therapeutic approach for oral squamous cell carcinoma (OSCC). Long noncoding RNAs (lncRNAs) participate in epigenetic regulation and have been linked to the progression, prognosis, and treatment of OSCC. Thus, this study aimed to identify new cuproptosis-related lncRNAs (CRLs), establish predictive models for clinical prognosis, immune response, and drug sensitivity, and provide novel insights into immune escape and tumor drug resistance.

METHODS

The present study screened eight CRLs (THAP9-AS1, STARD4-AS1, WDFY3-AS2, LINC00847, CDKN2A-DT, AL132800.1, GCC2-AS1, AC005746.1) using Lasso Cox regression analysis to develop an eight-CRL prognostic model. Patients were categorized into high- and low-risk groups using risk scores. To evaluate the predictive ability of the model, Kaplan-Meier analysis, ROC curves, and nomograms were employed. Furthermore, the study investigated the differences in immune function and anticancer drug sensitivity between the high- and low-risk groups. To validate the expression of CRLs in the model, OSCC cell lines were subjected to quantitative real-time fluorescence PCR (qRT-PCR).

RESULTS

The results of the study showed that the high-risk group had a shorter overall survival (OS) time in OSCC patients. Cox regression analysis demonstrated that the high-risk score was an independent risk factor for a poor prognosis. The validity of the model was confirmed using ROC curve analysis, and a nomogram was developed to predict the prognosis of OSCC patients. Furthermore, patients in the high-risk group with high TMB had a poorer prognosis. Patients in the low-risk group responded better to immunotherapy than those in the high-risk group. Additionally, the risk scores were significantly associated with drug sensitivity in OSCC patients. Finally, the findings of qRT-PCR supported the reliability of the proposed risk model.

CONCLUSION

The study identified and established the 8-CRL model, which represents a novel pathway of lncRNA regulation of cuproptosis in OSCC. This model provides guidance for the prognosis and treatment of OSCC and offers a new insight into immune escape and tumor drug resistance.

Amplifying T cell-mediated antitumor immune responses in nonsmall cell lung cancer through photodynamic therapy and anti-PD1

Photodynamic therapy (PDT) is nowadays widely employed in cancer treatment. We sought to assess the efficacy of combining PDT with anti-programmed cell death protein 1 (PD1) and to investigate the associated mechanisms in nonsmall cell lung cancer (NSCLC). We established a xenograft tumor model in C57BL/6J mice using Lewis lung carcinoma (LLC) cells, recorded tumor growth, and quantified reactive oxygen species (ROS) levels using a ROS detection kit. Pathological changes were assessed through H&E staining, while immunofluorescence (IF) was used to determine the expression of CD8 and Foxp3. Transcriptomic analysis was conducted, analyzing differential expressed genes (DEGs) among control, PDT, and PDT combined with anti-PD1 (PDT+anti-PD1) groups. Functional enrichment analysis via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed. The Cancer Genome Atlas (TCGA) database was utilized to analyze the expression of aminolevulinate synthase gene (ALAS2), integrin alpha10 (ITGA10), ATP1A2, a disintegrin and metalloprotease 12 (ADAM12), and Lox1 in lung adenocarcinoma and adjacent tissues, with concurrent immune infiltration analysis. Quantitative real-time polymerase chain reaction and western blot were employed to measure mRNA and protein expression levels. Treatment with PDT combined with anti-PD1 significantly inhibited tumor growth and increased the number of CD8+ cells while decreasing Foxp3+ cells. Immune infiltration results presented ALAS2, ADAM12, and ITGA10 were associated with various types of T cells or macrophages. Additionally, the expression levels of EGFR, ERK, and PI3K/Akt were suppressed after PDT with anti-PD1 treatment. Our findings collectively suggest that PDT combined with anti-PD1 treatment could enhance the infiltration of CD8+ T cells, suppressing tumor growth, and this effect was associated with ALAS2, ITGA10, and ADAM12. The underlying mechanism might be linked to EGFR, ERK, and PI3K/Akt signaling. Overall, this study provides valuable insights into the application of PDT combined with anti-PD1 treatment in NSCLC.

LncRNA WAC-AS1 promotes osteosarcoma Metastasis and stemness by sponging miR-5047 to upregulate SOX2

Cancer stemness and osteosarcoma (OS) malignant progression are closely associated. However, the molecular mechanisms underlying this association have not been fully demonstrated. Long noncoding RNAs (lncRNAs) are an intriguing class of widely prevalent endogenous RNAs involved in OS progression, the vast majority of which have not been characterized functionally. Here, we identified tumor promoter lncRNA WAC-AS1 to be highly expressed in OS tumors and associated with worse survival. Further analysis revealed that WAC-AS1 increased tumorsphere formation of OS cells and promoted metastasis, as confirmed by cell proliferation, transwell and wound healing assays. MiR-5047 was identified as a downstream target of WAC-AS1. Subsequently, based on bioinformatics analysis, RIP assay and luciferase reporter assay, SOX2 mRNA was verified as a target of miR-5047. WAC-AS1 enhanced OS cell proliferation and stemness via acting as a ceRNA by binding to miR-5047, thereby increasing SOX2 expression. In addition, SOX2 bound to the promoter region of WAC-AS1 and promoted its transcription, thereby forming a positive feedback loop to regulate OS malignancy. Taken together, our findings show WAC-AS1 is a tumor promoter and a key regulator of OS cell stemness and metastasis via a miR-5047/SOX2 axis.

Integrative analysis of multi-omics data reveals a pseudouridine-related lncRNA signature for prediction of glioma prognosis and chemoradiotherapy sensitivity

BACKGROUND

Glioblastoma is the most common type of glioma with a high incidence and poor prognosis, and effective medical treatment remains challenging. Pseudouridine (Ψ) is the first post-transcriptional modification discovered and one of the most abundant modifications to RNA. However, the prognostic value of Ψ-related lncRNAs (ΨrLs) for glioma patients has never been systematically evaluated. This study aims to construct a risk model based on ΨrLs signature and to validate the predictive efficiency of the model.

METHOD

Transcriptomic data, genomic data, and relevant clinical data of glioma patients were extracted from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). ΨrLs with significant correlation with Ψ-related genes were identified, and univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were used to further select biomarkers and construct a ΨrLs signature risk model. Then, the expression of lncRNAs of ΨrLs signature in multiple glioma cell lines was detected by qPCR. Further, ROC analysis, stratification analysis, correlation analysis, survival analysis, nomogram, enrichment analysis, immune infiltration analysis, chemoradiotherapy sensitivity analysis, somatic mutation, and recurrent copy number variation (CNV) analysis were used to validate the predictive efficiency of ΨrLs signature in TCGA and CGGA datasets.

RESULTS

A four-lncRNA ΨrLs signature (DNAJC27-AS1, GDNF-AS1, ZBTB20-AS4, and DNMBP-AS1) risk model was constructed. By ROC analysis, stratified analysis, correlation analysis, survival analysis, and nomogram, the signature showed satisfactory predictive efficiency. Functional enrichment analysis revealed the differences in immune-related biological processes between high- and low-risk groups. Immune infiltration analysis showed that the high-risk group had lower tumor purity and higher stromal, immune and ESTIMATE scores. Mitoxantrone was identified as effective drug for low-risk group of glioma patients. Key genes in glioma development, including IDH1, EGFR, PTEN, etc., were differentially mutated between risk groups. The main recurrent CNVs in low-risk groups were 19q13.42 deletion and 7q34 amplification; 10q23.31 deletion and 12q14.1 in the high-risk group.

CONCLUSIONS

Our study identified a four-lncRNA ΨrLs signature that effectively predicts the prognosis of glioma patients and may serve as a diagnostic tool. Risk scores of glioma patients generated by the signature is associated with immune-related biological processes and chemoradiotherapy sensitivity. These findings may inform the development of more targeted and effective therapies for glioma patients.

Mutation-derived, genomic instability-associated lncRNAs are prognostic markers in gliomas

Background

Gliomas are the most commonly-detected malignant tumors of the brain. They contain abundant long non-coding RNAs (lncRNAs), which are valuable cancer biomarkers. LncRNAs may be involved in genomic instability; however, their specific role and mechanism in gliomas remains unclear. LncRNAs that are related to genomic instability have not been reported in gliomas.

Methods

The transcriptome data from The Cancer Genome Atlas (TCGA) database were analyzed. The co-expression network of genomic instability-related lncRNAs and mRNA was established, and the model of genomic instability-related lncRNA was identified by univariate Cox regression and LASSO analyses. Based on the median risk score obtained in the training set, we divided the samples into high-risk and low-risk groups and proved the survival prediction ability of genomic instability-related lncRNA signatures. The results were verified in the external data set. Finally, a real-time quantitative polymerase chain reaction assay was performed to validate the signature.

Results

The signatures of 17 lncRNAs (LINC01579, AL022344.1, AC025171.5, LINC01116, MIR155HG, AC131097.3, LINC00906, CYTOR, AC015540.1, SLC25A21.AS1, H19, AL133415.1, SNHG18, FOXD3.AS1, LINC02593, AL354919.2 and CRNDE) related to genomic instability were identified. In the internal data set and Gene Expression Omnibus (GEO) external data set, the low-risk group showed better survival than the high-risk group (P < 0.001). In addition, this feature was identified as an independent risk factor, showing its independent prognostic value with different clinical stratifications. The majority of patients in the low-risk group had isocitrate dehydrogenase 1 (IDH1) mutations. The expression levels of these lncRNAs were significantly higher in glioblastoma cell lines than in normal cells.

Conclusions

Our study shows that the signature of 17 lncRNAs related to genomic instability has prognostic value for gliomas and could provide a potential therapeutic method for glioblastoma.

LncRNA WAC-AS1 expression in human tumors correlates with immune infiltration and affects prognosis

BACKGROUND

WAC-antisense RNA1 (WAC-AS1) is a newly identified long non-coding RNA (lncRNA) implicated in the prognosis and development of a few types of tumors. However, the correlations of WAC-AS1 with immune infiltration and patient prognosis in pan-cancer remain unclear. In the present study, we aimed to investigate the prognostic value and immunological functions of WAC-AS1 across 33 different types of cancers.

METHODS

To investigate the potential oncogenic roles of WAC-AS1, bioinformatics analyses were performed using the Cancer Genome Atlas (TCGA) and Genotype Tissue-Expression (GTEx) datasets. The correlations of WAC-AS1 with prognosis, clinical phenotype, tumor mutational burden (TMB), microsatellite instability (MSI), tumor regulation-related genes, tumor microenvironment, immune cell infiltration, and drug resistance to commonly used chemotherapy drugs in different types of tumors were explored. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to explore the biological functions of WAC-AS1 in tumors. In situ hybridization (ISH) was performed in tissue microarray (TMA) to confirm the expression of WAC-AS1 in multiple tumor tissues.

RESULTS

WAC-AS1 showed aberrant expression in most cancers when compared to the normal tissues. It also has prognostic value in multiple types of cancers. Elevated WAC-AS1 expression was associated with poor prognosis and overall survival in adrenocortical carcinoma (ACC), breast invasive carcinoma (BRCA), and liver hepatocellular carcinoma (LIHC). A significant negative correlation between WAC-AS1 expression and overall survival was observed in brain lower-grade glioma (LGG), pancreatic adenocarcinoma (PAAD), and skin cutaneous melanoma (SKCM). The expression of WAC-AS1 also showed a correlation with clinical stage in six types of tumors, and with tumor mutational burden and microsatellite instability in several different types of cancers. The immune scores of those cancers were found to be significant. Additionally, the effectiveness of fluorouracil and four other anticancer drugs was significantly different based on the expression of WAC-AS1 in these cancers. Moreover, the ISH results showed in six types of tumors, the expression of WAC-AS1 was consistent with the Pan-cancer analysis using TCGA and GTEx database.

CONCLUSIONS

These results indicate an intensive involvement of WAC-AS1 in the regulation of immune responses, immune cell infiltration, and malignant properties in various types of cancers, suggesting that WAC-AS1 may serve as a prognostic marker across diverse types of cancers.

Epigenetically silenced lncRNA SNAI3-AS1 promotes ferroptosis in glioma via perturbing the m6A-dependent recognition of Nrf2 mRNA mediated by SND1

BACKGROUND

Ferroptosis has been linked to tumor progression and resistance to antineoplastic therapy. Long noncoding RNA (lncRNA) exerts a regulatory role in various biological processes of tumor cells, while the function and molecular mechanism of lncRNA in ferroptosis are yet to be clarified in glioma.

METHODS

Both gain-of-function and loss-of-function experiments were employed to investigate the effects of SNAI3-AS1 on the tumorigenesis and ferroptosis susceptibility of glioma in vitro and in vivo. Bioinformatics analysis, Bisulfite sequencing PCR, RNA pull-down, RIP, MeRIP and dual-luciferase reporter assay were performed to explore the low expression mechanism of SNAI3-AS1 and the downstream mechanism of SNAI3-AS1 in ferroptosis susceptibility of glioma.

RESULTS

We found that ferroptosis inducer erastin downregulates SNAI3-AS1 expression in glioma by increasing the DNA methylation level of SNAI3-AS1 promoter. SNAI3-AS1 functions as a tumor suppressor in glioma. Importantly, SNAI3-AS1 enhances the anti-tumor activity of erastin by promoting ferroptosis both in vitro and in vivo. Mechanistically, SNAI3-AS1 competitively binds to SND1 and perturbs the m⁶A-dependent recognition of Nrf2 mRNA 3'UTR by SND1, thereby reducing the mRNA stability of Nrf2. Rescue experiments confirmed that SND1 overexpression and silence can rescue the gain- and loss-of-function ferroptotic phenotypes of SNAI3-AS1, respectively.

CONCLUSIONS

Our findings elucidate the effect and detailed mechanism of SNAI3-AS1/SND1/Nrf2 signalling axis in ferroptosis, and provide a theoretical support for inducing ferroptosis to improve glioma treatment.

Crosstalk between SOX Genes and Long Non-Coding RNAs in Glioblastoma

Glioblastoma (GBM) continues to be the most devastating primary brain malignancy. Despite significant advancements in understanding basic GBM biology and enormous efforts in developing new therapeutic approaches, the prognosis for most GBM patients remains poor with a median survival time of 15 months. Recently, the interplay between the SOX (SRY-related HMG-box) genes and lncRNAs (long non-coding RNAs) has become the focus of GBM research. Both classes of molecules have an aberrant expression in GBM and play essential roles in tumor initiation, progression, therapy resistance, and recurrence. In GBM, SOX and lncRNAs crosstalk through numerous functional axes, some of which are part of the complex transcriptional and epigenetic regulatory mechanisms. This review provides a systematic summary of current literature data on the complex interplay between SOX genes and lncRNAs and represents an effort to underscore the effects of SOX/lncRNA crosstalk on the malignant properties of GBM cells. Furthermore, we highlight the significance of this crosstalk in searching for new biomarkers and therapeutic approaches in GBM treatment.

Protein arginine methyltransferase 8 regulates ferroptosis and macrophage polarization in spinal cord injury via glial cell-derived neurotrophic factor

OBJECTIVE

To explore the influence of protein arginine methyltransferase 8 (PRMT8) regulating glial cell-derived neurotrophic factor (GDNF) on neuron ferroptosis and macrophage polarization in spinal cord injury (SCI).

METHODS

A rat model of SCI was established through an injury induced by an external force. Basso, Beattie, and Bresnahan score, hematoxylin and eosin staining, and immunofluorescence were used, respectively, to detect changes in rat locomotion, spinal cord histopathology, and NeuN expression in the spinal cord. Iron content in the spinal cord and levels of malondialdehyde and glutathione were measured using detection kits. Transmission electron microscopy was used to reveal the morphological characteristics of mitochondria. Western blotting was performed to detect PRMT8, GDNF, cystine/glutamate transporter XCT, glutathione peroxidase 4, 4-hydroxynonenal, heme oxygenase-1, inducible nitric oxide synthase (iNOS), CD16, and arginase 1 (Arg1). The expression levels of iNOS and Arg1 in the spinal cord were visualized by immunofluorescence. ELISA was performed to measure the expression levels of IL-6, IL-1β, and TNF-α. Rat dorsal root ganglion (DRG) neurons and RMa-bm rat macrophages were treated with lipopolysaccharide under hypoxic conditions. The viability and iron content of the neurons were detected using Cell Counting Kit-8 and a specific probe, respectively. Flow cytometry and immunofluorescence were used to assess macrophage polarization. Chromatin immunoprecipitation was used to identify the binding of PRMT8 to the GDFN promoter.

RESULTS

Neuronal ferroptosis and M1 macrophage polarization were promoted, and PRMT8 expression was downregulated in SCI. PRMT8 overexpression exerted therapeutic effects on injured DRG neurons and RMa-bm cells. Moreover, PRMT8 overexpression inhibited ferroptosis and M1 macrophage polarization in rats with SCI. PRMT8 promoted GDNF expression by catalyzing H3K4 methylation. Knockdown of GDNF counteracted the therapeutic effects of PRMT8 overexpression.

CONCLUSION

Overexpression of PRMT8 may inhibit ferroptosis and M1 macrophage polarization by increasing GDNF expression, thereby alleviating SCI.

Immunity-related long noncoding RNA WDFY3-AS2 inhibited cell proliferation and metastasis through Wnt/β-catenin signaling in oral squamous cell carcinoma

OBJECTIVE

Long noncoding RNA WDFY3-AS2 has been shown to play dual roles in the modulation of cancer progression. This study aimed at clarifying the biological role of WDFY3-AS2 as well as the association between WDFY3-AS2 expression, β-catenin expression, and OSCC immunity in oral squamous cell carcinoma (OSCC).

DESIGN

Bioinformatics analyses, CCK8, EdU, wound healing, transwell, RT-qPCR, western blot, immunofluorescence, in situ hybridization, and immunohistochemistry assays were adopted for exploring the role of WDFY3-AS2 in OSCC.

RESULTS

Bioinformatics analyses showed that WDFY3-AS2 conferred a poor prognosis for OSCC patients. Further analyses identified WDFY3-AS2 as an independent prognostic indicator for OSCC. Moreover, silencing WDFY3-AS2 inhibits OSCC cell proliferation, migration and invasion. Gene set enrichment analysis indicated that WDFY3-AS2 participated in the regulation of Wnt signaling. In addition, WDFY3-AS2 expression was positively associated with β-catenin mRNA levels, the key component of Wnt signaling. Interestingly, WDFY3-AS2 knockdown inhibited β-catenin expression and nuclear translocation, thus suppressing OSCC progression through Wnt signaling. Furthermore, WDFY3-AS2 expression correlated with an immunosuppressive phenotype in the tumor immune microenvironment. In situ hybridization and immunohistochemistry verified that WDFY3-AS2 was positively associated with total and nuclear β-catenin protein levels and negatively associated with CD4 expression.

CONCLUSIONS

This study demonstrates that the immunity-associated WDFY3-AS2 augments OSCC proliferation and metastasis through Wnt/β-catenin signaling and may serve as a novel treatment target and a new prognostic factor for OSCC.

Combining multi-omics approaches to prioritize the variant-regulated functional long non-coding RNAs in autism spectrum disorder

OBJECTIVE

Rising evidence has indicated that long non-coding RNA (lncRNA) may play an essential role in the development of autism spectrum disorder (ASD). However, identifying the lncRNAs associated with ASD and the risk loci on them remains a major challenge. This study aims to identify potential causative variants and explore the related mechanisms.

METHODS

By leveraging differential expression analysis, WGCNA analysis and cis-expression quantitative analysis, our study mined functional SNPs with the regulated long non-coding RNA genes in brain tissues. We recruited 611 ASD children and 645 healthy children in the case-control study.

RESULTS

Total 68 different expressed lncRNAs were validated by calculating the brain tissue-specific expression using RNA-seq data. By the WGCNA method, 9 functional lncRNAs classified as e-lncRNA were found to interact with 976 ASD risk genes. Furthermore, we mined functional SNPs regulated long non-coding RNAs in brain tissues. We analyzed the association between candidate SNPs and ASD risks in Chinese children, which showed BDNF-AS rs1565228 allele G to C reduced the risk of ASD (OR = 0.81, 95%CI: 0.66-0.98). Further bioinformatics analysis showed that the variant rs1565228 C>G with the low binding affinity of transcription factor SRF caused the decreased expression of lncRNA BDNF-AS. Our study revealed that rs2295412 in the non-coding sequence of the lncRNA gene region was significantly associated with the risk of ASD.

DISCUSSION

These findings suggested that the SNPs in the non-coding region of lncRNA may play important roles in the genetic susceptibility of ASD, which may facilitate the early screening of ASD.

Ferroptosis Biomarkers for Predicting Prognosis and Immunotherapy Efficacy in Adrenocortical Carcinoma

BACKGROUND

Numerous studies have suggested that ferroptosis plays an important regulatory role in cancer cell death. Nonetheless, the potential effects of ferroptosis regulators on the prognosis, the expression of immunomodulatory factors in the tumor microenvironment and on the efficacy of immunotherapy in adrenocortical carcinoma (ACC) remain largely unknown.

METHODS

Public ACC datasets were used to investigate the relationship between ferroptosis regulators and prognosis and clinical features. A ferroptosis scoring system was established for individual cases of ACC using principal component analysis algorithms. Hub ferroptosis-related genes involved in immunoregulation and immunotherapy efficacy in ACC were further identified.

RESULTS

Twenty ferroptosis regulators were differentially expressed in ACC and 17 ferroptosis regulators were closely related to prognosis in ACC. A ferroptosis scoring system was developed based on ACSL4, FANCD2, and SLC7A1 expression, and the ferroptosis regulators could serve as an independent prognostic factor for ACC. Further analyses indicated that the ferroptosis score integrated with the tumor mutation burden (TMB), and immune-checkpoint gene expression could predict prognosis in ACC. RNA isolation and reverse transcription‑quantitative polymerase chain reaction (RT-qPCR) demonstrated significant differences in the expression levels of ACSL4, FANCD2, and SLC7A1 between ACC and normal tissues. Furthermore, FANCD2 was significantly related to immunotherapy efficacy and prognosis in ACC.

CONCLUSION

Our study demonstrated that ferroptosis was significantly associated with prognosis, clinical characteristics, immune-checkpoint gene expression, and tumor microenvironment immune cell infiltration in ACC. The current study provides comprehensive evidence for further research on ferroptosis regulators in ACC and provides new insight into the epigenetic regulation of the antitumor immune response.

LncRNA as potential biomarker and therapeutic target in glioma

Glioma is the most frequent type of malignant tumor in the central nervous system, accounting for about 80% of primary malignant brain tumors, usually with a poor prognosis. A number of studies have been conducted on the molecular abnormalities in glioma to further understand its pathogenesis, and it has been found that lncRNAs (long non-coding RNA) play a key role in angiogenesis, tumor growth, infiltration and metastasis of glioma. Since specific lncRNAs have an aberrant expression in brain tissue, cerebrospinal fluid as well as peripheral circulation of glioma patients, they are considered to be potential biomarkers. This review focuses on the biological characteristics of lncRNA and its value as a biomarker for glioma diagnosis and prognosis. Moreover, in view of the role of lncRNAs in glioma proliferation and chemoradiotherapy resistance, we discussed the feasibility for lncRNAs as therapeutic targets. Finally, the persisting deficiencies and future prospects of using lncRNAs as clinical biomarkers and therapeutic targets were concluded.

Identification and Validation of Ferroptosis-Related LncRNAs Signature as a Novel Prognostic Model for Chronic Lymphocytic Leukemia

Background

Chronic lymphocytic leukemia (CLL) is a subtype of B-cell malignancy with high heterogeneity. Ferroptosis is a novel cell death induced by iron and lipid peroxidation and exhibits prognostic value in many cancers. Emerging studies on long non-coding RNAs (lncRNAs) and ferroptosis reveal the unique value in tumorigenesis. However, the prognostic value of ferroptosis-related lncRNAs (FRLs) remains unclear in CLL.

Aim

We aimed to construct a FRLs risk model to predict prognosis and improve prognostic stratification for clinical practice.

Methods

RNA-sequencing data and clinical characteristics of CLL patients were downloaded from the GEO database. Based on ferroptosis-related genes from FerrDb database, differentially expressed FRLs with prognostic significance were identified and used to generate the risk model. The capability of the risk model was assessed and evaluated. GO and KEGG analyses were performed to confirm biological roles and potential pathways.

Results

A novel ferroptosis-related lncRNAs prognostic score (FPS) model containing six FRLs (PRKCQ, TRG.AS1, LNC00467, LNC01096, PCAT6 and SBF2.AS1) was identified. Patients in the training and validation cohort were evenly divided into high- and low-risk groups. Our results indicated that patients in the high-risk group had worse survival than those in the low-risk group. Functional enrichment analyses showed that the differently expressed genes (DEGs) between the two groups were enriched in the chemokine signaling pathway, hematopoietic cell lineage, T cell differentiation, TCR pathway and NF-κB pathway. Moreover, significant differences in immune cell infiltration were also observed. Surprisingly, FPS was proved to be an independent prognostic indicator for OS.

Conclusion

We established and evaluated a novel prognostic risk model with 6 FRLs that could predict prognosis accurately and describe the distinct immune infiltration in CLL.

Targeting stearoyl-coa desaturase enhances radiation induced ferroptosis and immunogenic cell death in esophageal squamous cell carcinoma

Overcoming resistance to radiation is a major challenge in cancer treatment. Stearoyl-coa desaturase (SCD1) is the enzyme responsible for oleic acid (OA) and palmitoleic acid (POA) formation. Here, we provided evidence that targeting SCD1 was capable of inducing ferroptosis and immunogenic cell death (ICD), thereby improving the radiation sensitivity of esophageal squamous cell carcinoma (ESCC). ESCC cell lines with high SCD1 expression were treated with MF-438 (SCD1 inhibitor) to determine cell viability. Colony formation assay was performed to evaluate the radiation sensitization of SCD1 inhibitor. Tumor cell ferroptosis and ICD was analyzed in MF-438, radiation therapy (RT) and the combination treatment group. The potential molecular mechanisms underlying MF-438 as a novel radiation sensitizer in ESCC were explored. We concluded by assessing SCD1 as a prognostic factor in ESCC. MF-438 exhibited antitumor activity in ESCC cells. Our outcomes revealed significant improvement of radiation sensitivity by MF-438. Moreover, the combination treatment enhanced tumor cell ferroptosis and ICD. Further analyses revealed SCD1 conferred radiation resistance via alleviating ferroptosis in tumor cells; targeting SCD1 inhibited the biosynthesis of OA and POA, and improved radiation induced ferroptosis in ESCC cells. Clinical analysis indicated high expression of SCD1 was associated with unfavorable survival in patients of ESCC. In summary, our results demonstrated that MF-438 acted as a ferroptosis inducer. Targeting SCD1 conferred the immunogenicity of ferroptotic cancer cells and increased the effectiveness of RT in ESCC. SCD1 could be considered as a useful prognostic indicator of survival in ESCC.

Identification and validation of a ferroptosis-related lncRNA signature to robustly predict the prognosis, immune microenvironment, and immunotherapy efficiency in patients with clear cell renal cell carcinoma

Background

Ferroptosis is a new type of iron- and reactive oxygen species-dependent cell death, studies on ferroptosis-related long noncoding RNAs (FerLncRNAs) in clear cell renal cell carcinoma (ccRCC) are limited. The purpose of this study was to investigate the potential prognostic value of FerLncRNAs and their relationship with the immune microenvironment and immunotherapy response of ccRCC.

Methods

RNA sequencing data of 526 patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. The patients with ccRCC in TCGA were randomly divided (1:1) into a training and testing cohort. ICGC and GEO databases were used for validation. Screening for FerLncRNAs was performed using Pearson's correlation analysis with the reported ferroptosis-related genes. A FerLncRNA signature was constructed using univariate, LASSO, and multivariate Cox regression analyses in the training cohort. Internal and external datasets were performed to verify the FRlncRNA signature. Four major FRlncRNAs were verified through in vitro experiment.

Results

We identified seven FerLncRNAs (LINC00894, DUXAP8, LINC01426, PVT1, PELATON, LINC02609, and MYG1-AS1), and established a risk signature and nomogram for predicting the prognosis of ccRCC. Four major FRlncRNAs were verified with the prognosis of ccRCC in the GEPIA and K-M Plotter databases, and their expressions were validated by realtime PCR. The risk signature can also effectively reflect the immune environment, immunotherapy response and drug sensitivity of ccRCC. These FRlncRNAs have great significance to the implementation of individualized treatment and disease monitoring of ccRCC patients.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

SARS-CoV-2 Pattern Provides a New Scoring System and Predicts the Prognosis and Immune Therapeutic Response in Glioma

OBJECTIVE

Glioma is the most common primary malignancy of the adult central nervous system (CNS), with a poor prognosis and no effective prognostic signature. Since late 2019, the world has been affected by the rapid spread of SARS-CoV-2 infection. Research on SARS-CoV-2 is flourishing; however, its potential mechanistic association with glioma has rarely been reported. The aim of this study was to investigate the potential correlation of SARS-CoV-2-related genes with the occurrence, progression, prognosis, and immunotherapy of gliomas.

METHODS

SARS-CoV-2-related genes were obtained from the human protein atlas (HPA), while transcriptional data and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Glioma samples were collected from surgeries with the knowledge of patients. Differentially expressed genes were then identified and screened, and seven SARS-CoV-2 related genes were generated by LASSO regression analysis and uni/multi-variate COX analysis. A prognostic SARS-CoV-2-related gene signature (SCRGS) was then constructed based on these seven genes and validated in the TCGA validation cohort and CGGA cohort. Next, a nomogram was established by combining critical clinicopathological data. The correlation between SCRGS and glioma related biological processes was clarified by Gene set enrichment analysis (GSEA). In addition, immune infiltration and immune score, as well as immune checkpoint expression and immune escape, were further analyzed to assess the role of SCRGS in glioma-associated immune landscape and the responsiveness of immunotherapy. Finally, the reliability of SCRGS was verified by quantitative real-time polymerase chain reaction (qRT-PCR) on glioma samples.

RESULTS

The prognostic SCRGS contained seven genes, REEP6, CEP112, LARP4B, CWC27, GOLGA2, ATP6AP1, and ERO1B. Patients were divided into high- and low-risk groups according to the median SARS-CoV-2 Index. Overall survival was significantly worse in the high-risk group than in the low-risk group. COX analysis and receiver operating characteristic (ROC) curves demonstrated excellent predictive power for SCRGS for glioma prognosis. In addition, GSEA, immune infiltration, and immune scores indicated that SCRGS could potentially predict the tumor microenvironment, immune infiltration, and immune response in glioma patients.

CONCLUSIONS

The SCRGS established here can effectively predict the prognosis of glioma patients and provide a potential direction for immunotherapy.

m6A-related lncRNA signature for predicting prognosis and immune response in head and neck squamous cell carcinoma

OBJECTIVES

N6-methyladenosine (m⁶A) and long non-coding RNAs (lncRNAs) significantly impact the prognosis and the response to immunotherapy in head and neck squamous cell carcinoma (HNSCC). Therefore, this study aimed to develop an m⁶A-related lncRNA (m⁶AlncRNA) model for predicting the prognosis and the immunotherapeutic response in HNSCC.

METHODS

We identified the m⁶AlncRNAs and constructed a risk assessment signature by using univariable Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The Kaplan-Meier analysis, receiver-operating characteristic (ROC) curves, principal component analysis (PCA), decision curve analysis (DCA), consistency index (C-index), and nomogram were applied to assess the risk model. Finally, we investigated the predictability of this model in prognosis and response to immunotherapy and evaluated various novel compounds for the clinical treatment of HNSCC.

RESULTS

HNSCC patients were assigned to high- and low-risk groups based on the median risk scores, and the high- and low-risk groups had different clinical features, tumor immune infiltration status, tumor immune dysfunction and exclusion (TIDE), tumor mutational burden (TMB), sensitivity to novel potential compounds, and immunotherapeutic response.

CONCLUSIONS

The model we developed was accurate and efficient in predicting the prognosis of patients with HNSCC. It was also sensitive in stratifying HNSCC patients with good response to immunotherapy. Therefore, our study provided insight into elucidating the processes and mechanisms of m⁶AlncRNAs.

Molecular subtypes of cuproptosis regulators and their correlation with clinical prognosis and immune response in glioma

Cuproptosis is a newly described form of cell death. However, nothing is known about the roles of cuproptosis regulators in glioma. First, we explored the characteristics of cuproptosis molecular subtypes and relevant tumor microenvironment (TME) immune cell infiltration patterns in glioma. Using unsupervised clustering analysis, we identified two cuproptosis subtypes and three gene clusters that exhibited different clinical characteristics and TME cell infiltration patterns. Then, we developed and validated a cuproptosis-related prognostic model for predicting the overall survival of glioma patients. We established a risk score tool based on a nomogram to assess the clinical applicability of the cuproptosis model. A high cuproptosis risk score with high immune cell infiltration level, tumor mutation burden, gene alterations, and immunity activation had an unfavorable overall survival. Next, we identified possible competing endogenous ribonucleic acid regulatory networks based on significantly differentially expressed genes between high-risk and low-risk groups and screened several candidate small molecular compounds that may improve chemotherapy. Data from IMvigor and GSE78200 showed that the cuproptosis score affected the prognosis of patients who received immunotherapy. Our study indicated that cuproptosis regulators are involved in TME immune infiltration and impact the clinical prognosis in glioma. It is necessary for clinical practice to develop different therapeutic strategies according to the different phenotypes associated with immune response. The present findings provide new insight for improving immunotherapy strategies and individualized treatment in glioma.

Identification and validation of cuproptosis-related LncRNA signatures as a novel prognostic model for head and neck squamous cell cancer

Background

Head and neck squamous cell cancer (HNSCC) is a common malignant cancer. We aimed to explore prognostic cuproptosis-related lncRNAs (CRLs) and prognostic risk models for HNSCC.

Methods

The transcriptome profiles and clinical data were obtained from the TCGA database, and 19-cuproptosis-related genes (CRGs) were acquired from previous studies. Then, the prognostic model based on seven CRLs was established. We analysed its value to evaluate the prognosis, drug sensitivity, and tumour immune functions of patients with HNSCC. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT‒PCR) to validate the seven CRLs.

Results

We established a 7-CRL signature. Kaplan‒Meier (K–M) curve analysis demonstrated a significantly preferable prognosis in the low-risk group. Multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor. Nomogram, ROC curve, and principal component analysis indicated that the signature presented significant predictive capability. Moreover, most of the high-risk group showed lower levels of IC50 for certain chemotherapy drugs, such as cisplatin, cytarabine, docetaxel, doxorubicin, etoposide, gemcitabine, methotrexate, paclitaxel, and dasatinib. Finally, the expression of AP001372.2, MIR9-3HG, AL160314.2, POLH-AS1, and AL109936.2 was upregulated, while AC090587.1 and WDFY3-AS2 were downregulated in HNSCC cell lines compared with normal cell lines by qRT‒PCR.

Conclusions

The 7-CRL signature was presented to be a novel biomarker for predicting prognosis for HNSCC.

A prognostic pyroptosis-related LncRNA classifier associated with the immune landscape and therapy efficacy in glioma

Background: Glioma has the highest fatality rate among intracranial tumours. Besides, the heterogeneity of gliomas leads to different therapeutic effects even with the same treatment. Developing a new signature for glioma to achieve the concept of “personalised medicine” remains a significant challenge.

Method: The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were searched to acquire information on glioma patients. Initially, correlation and univariate Cox regression analyses were performed to screen for prognostic pyroptosis-related long noncoding RNAs (PRLs). Secondly, 11 PRLs were selected to construct the classifier using certain algorithms. The efficacy of the classifier was then detected by the “timeROC” package for both the training and validation datasets. CIBERSORT and ESTIMATE packages were applied for comparing the differences (variations) in the immune landscape between the high- and low-risk groups. Finally, the therapeutic efficacy of the chemotherapy, radiotherapy, and immunotherapy were assessed using the “oncoPredict” package, survival analysis, and the tumour immune dysfunction and exclusion (TIDE) score, respectively.

Results: A classifier comprising 11 PRLs was constructed. The PRL classifier exhibits a more robust prediction capacity for the survival outcomes in patients with gliomas than the clinical characteristics irrespective of the dataset (training or validation dataset). Moreover, it was found that the tumour landscape between the low- and high-risk groups was significantly different. A high-risk score was linked to a more immunosuppressive tumour microenvironment. According to the outcome prediction and analysis of the chemotherapy, patients with different scores showed different responses to various chemotherapeutic drugs and immunotherapy. Meanwhile, the patient with glioma of WHO grade Ⅳ or aged >50 years in the high risk group had better survival following radiotherapy.

Conclusion: We constructed a PRL classifier to roughly predict the outcome of patients with gliomas. Furthermore, the PRL classifier was linked to the immune landscape of glioma and may guide clinical treatments.

Construction and Verification of a Novel Pyroptosis-Related lncRNA Signature Associated with Immune Landscape in Gliomas

Gliomas are the most common tumor in the central nervous system with limited prognostic markers making it difficult to research progression. Induction of cellular immunogenic death is a promising treatment for glioma. Pyroptosis is one of the recently discovered programmed immuogenic cell death modes which remains unclear in glioma. We obtained glioma datasets from the CGGA and TCGA websites. Pearson correlation analysis was used to find pyroptosis-related lncRNAs. Subsequently, the univariate, LASSO, and multivariate Cox regression were applied to construct a prognostic signature based on pyroptosis-related lncRNAs. Kaplan-Meier plots, ROC curves, and PCA were utilized for testing the prognostic performance of the signature. We conducted the univariate and multivariate Cox regressions to ascertain if the signature worked as an independent factor for predicting overall survival (OS) for individuals with glioma from other characteristics. For evaluating the immune landscape differences between the subgroups, ESTIMATE, CIBERTSORT, and ssGSEA were adopted. Additionally, biological functions and pathways of DEGs were identified by KEGG and GO. We also screened potential drugs and measured sensitivities of chemotherapeutics between the subgroups by CellMiner and pRRophetic package. Finally, shRNA was conducted to knockdown of COX10-AS1 in U87 cells to determine its relationship with pyroptosis. We successfully created an effective pyroptosis-related lncRNA signature that divided individuals into groups of low- and high-risk, and individuals in the high-risk group were with poor prognosis in comparison to the individuals in the other group. A nomogram including clinical factors and risk scores to predict the OS was built. Furthermore, the two groups appeared to have different immune landscapes; the high-risk group showed greater levels of ESTIMATE scores, immune cell infiltration, and immune checkpoints. Additionally, immune-related pathways and functions were shown to be enriched according to KEGG and GO findings. Knockdown of COX10-AS1 inhibited U87 cell growth, upregulated CASP1 and NLRP3, and released more IL1-β and IL-18 than the negative control. In summary, our study developed an lncRNA signature related to pyroptosis for OS prediction of gliomas and demonstrated its relationship with immune infiltration and drug sensitivity.

Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.

Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.

Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.

Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.

Long non-coding RNA WAC antisense RNA 1 mediates hepatitis B virus replication <em>in vitro</em> by reinforcing miR-192-5p/ATG7-induced autophagy

Long non-coding RNA WAC antisense RNA 1 (lncRNA WAC-AS1) is involved in the replication of the hepatitis B virus (HBV). The purpose of this study was to determine its functions and specific mechanism. The levels of lncRNA WAC-AS1, RNA (miR)-192-5p and were examined in serum of HBV-infected patients and in HepG2.2.15 cells using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and Western blotting. Using the database starBase, the target binding sites of lncRNA WAC-AS1 and miR-192-5p were predicted and confirmed by dual-luciferase reporter assay and RNA pull-down assay. The expression of pgRNA and HBV DNA was determined by qRT-PCR, while the levels of HBeAg and HBsAg were measured by enzyme-linked immunosorbent assay (ELISA). Using laser scanning confocal microscopy, the light chain 3 (LC3) expression was analyzed. qRT-PCR and Western blotting were used to assess the expression of beclin-1, p62, and LC3I/II. Overexpression of lncRNA WAC-AS1, upregulation of ATG7. and downregulation of miR-192-5p were observed in the serum of HBV-infected patients and the in vitro model. miR-192-5p directly targets lncRNA WAC-AS1. LncRNA WAC-AS1 was downregulated in lncRNA WAC-AS1-shRNA‒transfected cells. miR-192-5p was upregulated in lncRNA WAC-AS1-shRNA-transfected cells and downregulated in cells transfected with a miR-192-5p inhibitor. In HepG2 2.15 cells, the downregulation of lncRNA WAC-AS1 inhibited HBV replication and autophagy. In contrast, the miR-192-5p inhibitor-transfected group exhibited the opposite results, and ATG7 overexpression reversed the effects of miR-192-5p mimic or lncRNA WAC-AS1-shRNA on HBV replication and cell autophagy. Our findings indicate that lncRNA WAC-AS1 regulates HBV replication by reinforcing the autophagy induced by miR-192-5p/ATG7. Consequently, lncRNA WAC-AS1 may serve as a therapeutically-promising target in HBV patients.

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

Background

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

Methods

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

Results

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

Conclusion

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

The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

m6A-Related lncRNA Signature Is Involved in Immunosuppression and Predicts the Patient Prognosis of the Age-Associated Ovarian Cancer

Background

Epithelial ovarian cancers are age-associated diseases, usually diagnosed at an advanced stage. lncRNA has been discovered to interplay with N6-methyladenosine (m6A), working in tandem to promote cancer progression and worsening patient outcomes. This study is aimed at investigating the roles and mechanism of m6A-related lncRNA signature on ovarian cancers.

Methods

We retrieved TCGA and CGGA sequencing data to identify m6A-related lncRNA signature and constructed an m6A score (MS) using the LASSO algorithm. A clinical nomogram was then established to predict the overall survival of patients. Subsequently, GSEA analyses were conducted to obtain pathways involved. Expression of HLA genes, 28 tumor-infiltrating lymphocyte infiltration, and anticancer cycle were analyzed the immunological differences between high-MS and low-MS groups. Finally, immune checkpoint gene expressions and IC₅₀ of chemotherapeutic drugs were calculated, and CMap was run to identify the potential compounds and their corresponding mechanisms.

Results

We identified 16 m6A-related lncRNAs and constructed an MS model. The high-MS group showed a poor prognosis. A clinical nomogram consists of MS, and age was constructed and predicted the 1-, 3-, and 5-year survival with high accuracy. GSEA analyses presented downregulated antigen processing and presentation pathways. Immunocyte infiltrating analyses demonstrated that high-MS was associated with high infiltration of Treg cells, macrophages, and low Th1/Th2 rate. Also, high expression of immune checkpoint genes NRP1, TNFSF9, and VSIR was observed in the high-MS group. Finally, the high-MS group also predicted low IC₅₀ of vinorelbine and vorinostat.

Conclusion

This study constructed a robust prediction model for prognostic management and revealed the cross-talk between m6A and immunosuppression. Besides, the m6A lncRNA signature can predict the chemotherapeutic drug response. These will shed light on the development of novel therapeutic strategies and render survival benefits for ovarian patients.

A novel signature based on necroptosis-related long non-coding RNAs for predicting prognosis of patients with glioma

Necroptosis is closely related to the occurrence and development of tumors, including glioma. A growing number of studies indicate that targeting necroptosis could be an effective treatment strategy against cancer. Long non-coding RNA (lncRNA) is also believed to play a pivotal role in tumor epigenetics. Therefore, it is necessary to identify the functions of necroptosis-related lncRNAs in glioma. In this study, the transcriptome and clinical characteristic data of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases were collected, and the differentially expressed necroptosis-related lncRNAs in TCGA that have an impact on overall survival (OS) were screened out to construct risk score (RS) formula, which was verified in CGGA. A nomogram was constructed to predict the prognosis of glioma patients based on clinical characteristics and RS. In addition, Gene Set Enrichment Analysis (GSEA) was used to analyze the main enrichment functions of these necroptosis-related lncRNAs and the immune microenvironment. A total of nine necroptosis-related lncRNAs have been identified to construct the RS formula, and the Kaplan–Meier (K-M) survival analysis showed significantly poorer outcomes in the high RS group in both TCGA and CGGA databases. Moreover, the receiver operating characteristic (ROC) curve shows that our prediction RS model has good predictability. Regarding the analysis of the immune microenvironment, significant differences were observed in immune function and immune checkpoint between the high RS group and the low RS group. In conclusion, we constructed a necroptosis-related lncRNA RS model that can effectively predict the prognosis of glioma patients and provided the theoretical basis and the potential therapeutic targets for immunotherapy against gliomas.

The prognostic value and immune landscape of a cuproptosis-related lncRNA signature in head and neck squamous cell carcinoma

Background: Cuproptosis has been recognized as a novel regulatory cell death, which has been confirmed to promote the occurrence and development of tumors. However, whether cuproptosis-related lncRNA has an impact on the prognosis of squamous cell carcinoma of the head and neck (HNSCC) is still unclear.

Methods: In total, 501 HNSCC tumor samples and 44 normal were downloaded from the TCGA database. Cuproptosis-related lncRNAs were obtained by co-expressed analysis. We got prognostic lncRNA that was associated with cuproptosis by using univariate Cox regression analysis and LASSO Cox regression. Then we constructed and validated the prognostic signature of HNSCC and analyzed the immune landscape of the signature.

Results: The Prognostic Signature is based on 10 cuproptosis-related lncRNAs including AC090587.1, AC004943.2, TTN-AS1, AL162458.1, AC106820.5, AC012313.5, AL132800.1, WDFY3-AS2, CDKN2A-DT, and AL136419.3. The results of overall survival, risk score distribution, and survival status in the low-risk group were better than those in the high-risk group. In addition, all immune checkpoint genes involved were significantly different between the two risk groups (p < 0.05). The risk score was positively correlated with Eosinophils. M0 and M2 phenotype macrophages, mast cells activated, NK cells activated, and negatively related with B cells naive, mast cells resting, plasma cells, CD8T cells, T cells follicular helper, T cells regulatory (Tregs). Consensus clustering was identified in molecular subtypes of HNSC. More high-risk samples concentrated in Cluster1, which had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and Single Nucleotide Polymorphisms (SNP) alternation than Cluster2.

Conclusion: Our study elucidated the correlation between cuproptosis-related lncRNA with prognosis and immune landscape of HNSCC, which may provide references for further research on the exploration of the mechanism and functions of the prognosis for HNSCC.

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.

Ferroptosis Modulation: Potential Therapeutic Target for Glioblastoma Treatment

Glioblastoma multiforme is a lethal disease and represents the most common and severe type of glioma. Drug resistance and the evasion of cell death are the main characteristics of its malignancy, leading to a high percentage of disease recurrence and the patients’ low survival rate. Exploiting the modulation of cell death mechanisms could be an important strategy to prevent tumor development and reverse the high mortality and morbidity rates in glioblastoma patients. Ferroptosis is a recently described type of cell death, which is characterized by iron accumulation, high levels of polyunsaturated fatty acid (PUFA)-containing phospholipids, and deficiency in lipid peroxidation repair. Several studies have demonstrated that ferroptosis has a potential role in cancer treatment and could be a promising approach for glioblastoma patients. Thus, here, we present an overview of the mechanisms of the iron-dependent cell death and summarize the current findings of ferroptosis modulation on glioblastoma including its non-canonical pathway. Moreover, we focused on new ferroptosis-inducing compounds for glioma treatment, and we highlight the key ferroptosis-related genes to glioma prognosis, which could be further explored. Thereby, understanding how to trigger ferroptosis in glioblastoma may provide promising pharmacological targets and indicate new therapeutic approaches to increase the survival of glioblastoma patients.

Mitochondrial Homeostasis–Related lncRNAs are Potential Biomarkers for Predicting Prognosis and Immune Response in Lung Adenocarcinoma

The prognosis of the most common histological subtype of lung cancer, lung adenocarcinoma (LUAD), is relatively poor. Mitochondrial homeostasis depends to a great extent on the coordination between mitophagy and mitochondrial biogenesis, the deregulation of which causes various human diseases, including cancer. There is accumulating evidence that long noncoding RNAs (lncRNAs) are critical in predicting the prognosis and immune response in carcinoma. Therefore, it is critical to discern lncRNAs related to mitochondrial homeostasis in LUAD patients. In this study, we identified mitochondrial homeostasis–related lncRNAs (MHRlncRNAs) by coexpression analysis. In order to construct a prognostic signature composed of three MHRlncRNAs, univariate and multivariate Cox regression analyses were performed. Kaplan–Meier analysis, stratification analysis, principal component analysis (PCA), receiver operating characteristic (ROC) curve, gene set enrichment analysis (GSEA), and nomogram were applied to evaluate and optimize the risk model. Subsequently, we identified the mitochondrial homeostasis–related lncRNA signature (MHLncSig) as an independent predictive factor of prognosis. Based on the LUAD subtypes regrouped by this risk model, we further investigated the underlying tumor microenvironment, tumor mutation burden, and immune landscape behind different risk groups. Likewise, individualized immunotherapeutic strategies and candidate compounds were screened to aim at different risk subtypes of LUAD patients. Finally, we validated the expression trends of lncRNAs included in the risk model using quantitative real-time polymerase chain reaction (qRT-PCR) assays. The established MHLncSig may be a promising tool for predicting the prognosis and guiding individualized treatment in LUAD.

Non-coding RNAs and ferroptosis: potential implications for cancer therapy

Ferroptosis is a recently defined form of regulated cell death, which is biochemically and morphologically distinct from traditional forms of programmed cell death such as apoptosis or necrosis. It is driven by iron, reactive oxygen species, and phospholipids that are oxidatively damaged, ultimately resulting in mitochondrial damage and breakdown of membrane integrity. Numerous cellular signaling pathways and molecules are involved in the regulation of ferroptosis, including enzymes that control the cellular redox status. Alterations in the ferroptosis-regulating network can contribute to the development of various diseases, including cancer. Evidence suggests that ferroptosis is commonly suppressed in cancer cells, allowing them to survive and progress. However, cancer cells which are resistant to common chemotherapeutic drugs seem to be highly susceptible to ferroptosis inducers, highlighting the great potential of pharmacologic modulation of ferroptosis for cancer treatment. Non-coding RNAs (ncRNAs) are considered master regulators of various cellular processes, particularly in cancer where they have been implicated in all hallmarks of cancer. Recent work also demonstrated their involvement in the molecular control of ferroptosis. Hence, ncRNA-based therapeutics represent an exciting alternative to modulate ferroptosis for cancer therapy. This review summarizes the ncRNAs implicated in the regulation of ferroptosis in cancer and highlights their underlying molecular mechanisms in the light of potential therapeutic applications.

LncRNA SBF2-AS1: A Budding Star in Various Cancers

Long non-coding RNA (lncRNA) is a new kind of RNA with lengths over 200 nucleotides. Current frontiers revealed that lncRNAs implicate in various tumor progression, including tumorigenesis, proliferation, migration, invasion, metastasis and angiogenesis. Recently discovered long non-coding RNA SET-binding factor 2 antisense RNA 1 (lncRNA SBF2-AS1), an oncogenic antisense RNA to SBF2, locates at 11p15.1 locus and is 2708 nt long. Accumulating evidences have demonstrated that lncRNA SBF2-AS1 participates in various tumor progression including pathogenesis, diagnosis, treatment and prognosis of acute myeloid leukemia (AML), breast cancer (BC), cervical cancer (CC), clear cell renal cell carcinoma (ccRCC), colorectal cancer (CRC), diffuse large B-cell lymphoma (DLBCL), esophageal squamous cell carcinoma (ESCC), gastric cancer (GC), glioma, glioblastoma (GBM), hepatocellular carcinoma (HCC), lung cancer (LC), lung adenocarcinoma (LUAD), non-small cell lung cancer (NSCLC), osteosarcoma (OS), pancreatic cancer (PC), papillary thyroid cancer (PTC), small cell lung cancer (SCLC). Therefore, we summarized the underlying mechanisms about lncRNA SBF2-AS1 in various cancers to utilize its therapeutic function in target-selective treatment modality.

A Novel Prognostic Ferroptosis-Related lncRNA Signature Associated with Immune Landscape in Invasive Breast Cancer

Breast cancer (BC) represents the most common form of malignant tumors in women. However, the effectiveness of BC immunotherapy remains very low. Ferroptosis is a recently described form of programmed cell death which has unique characteristics, and associated long-chain noncoding RNAs (lncRNA) are thought to influence the occurrence and development of a variety of tumors. We identified 1,636 lncRNAs associated with ferroptosis in BC patients. 299 differentially expressed ferroptosis-related lncRNAs were subjected to univariate, LASSO regression, and multivariate Cox regression analyses to construct a ten ferroptosis-related lncRNA signature. This ten ferroptosis-related lncRNA signature performed very well in predicting survival of BC patients, and the risk score of the mRNA signature was identified as an independent prognostic factor in this cancer entity. In addition, the signature could be used to predict the immune landscape of BC patients. Low-risk patients had enriched immune-related pathways and more infiltration of most types of immune cells. The signature was also associated with the tumor mutation burden in BC. The results have allowed us to assess the potential for immunotherapy targets exposed by this model. The ferroptosis-related lncRNA risk model reported in the current study has clinical utility in BC prognosis and predicted immunotherapy response.

Gliomas with Downregulation of lncRNA SLC25A21-AS1 Carry a Dismal Prognosis and an Accelerated Progression in Cell Proliferation, Migration and Invasion

Glioma is one type of primary intracranial carcinoma with a relatively poor prognosis. We investigated the level of SLC25A21-AS1 in gliomas and the association with survival and progression in patients with glioma. Specimens of gliomas from patients were assessed by quantitative real-time polymerase chain reaction analysis of the SLC25A21-AS1 level (117 specimens). For prognostic value assessment, χ² test, Kaplan-Meier method with the log-rank test, and Multivariate survival analysis were performed. The direct targets for SLC25A21-AS1 were explored. The biological roles of SLC25A21-AS1 were investigated by manipulating the expression level of SLC25A21-AS1 in glioma cells. SLC25A21-AS1 was significantly downregulated in glioma specimens and cell lines compared to non-cancerous ones. Significant associations were found between SLC25A21-AS1 downregulation and WHO stage, IDH status, poor disease-free survival/overall survival. miR-221-3p/miR-222-3p were the target miRNAs for SLC25A21-AS1. Overexpression of SLC25A21-AS1 inhibited glioma cell growth, invasion, and migration while miR-221-3p/miR-222-3p-overexpressed groups could offset this effect. Downregulation of SLC25A21-AS1 in gliomas carries a universally poor prognosis. Overexpression of SLC25A21-AS1 inhibited glioma progression via miR-221-3p/miR-222-3p.

5-Methylcytosine Related LncRNAs Reveal Immune Characteristics, Predict Prognosis and Oncology Treatment Outcome in Lower-Grade Gliomas

5-Methylcytosine (m5C) methylation is an important RNA modification pattern that can participate in oncogenesis and progression of cancers by affecting RNA stability, expression of oncogenes, and the activity of cancer signaling pathways. Alterations in the expression pattern of long non-coding RNAs (lncRNAs) are potentially correlated with abnormalities in the m5C regulation features of cancers. Our aim was to reveal the mechanisms by which lncRNAs regulated the m5C process, to explore the impact of aberrant regulation of m5C on the biological properties of lower-grade gliomas (LGG), and to optimize current therapeutic. By searching 1017 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas, we first clarified the potential impact of m5C regulators on LGG prognosis in this study and used univariate Cox analysis and least absolute shrinkage and selection operator regression to explore clinically meaningful lncRNAs. Consequently, we identified four lncRNAs, including LINC00265, CIRBP-AS1, GDNF-AS1, and ZBTB20-AS4, and established a novel m5C-related lncRNAs signature (m5CrLS) that was effective in predicting prognosis. Notably, mutation rate, WHO class II, IDH mutation, 1p/19q co-deletion and MGMT promoter methylation were increased in the low m5CrLS score group. Patients with increased m5CrLS scores mostly showed activation of tumor malignancy-related pathways, increased immune infiltrating cells, and decreased anti-tumor immune function. Besides, the relatively high expression of immune checkpoints also revealed the immunosuppressed state of patients with high m5CrLS scores. In particular, m5CrLS stratification was sensitive to assess the efficacy of LGG to temozolomide and the responsiveness of immune checkpoint blockade. In conclusion, our results revealed the molecular basis of LGG, provided valuable clues for our understanding of m5C-related lncRNAs, and filled a gap between epigenetics and tumor microenvironment.

Ferroptosis, as the most enriched programmed cell death process in glioma, induces immunosuppression and immunotherapy resistance

BACKGROUND

Immunosuppressive microenvironment is a major cause of immunotherapeutic resistance in glioma. In addition to secreting compounds, tumor cells under programmed cell death (PCD) processes release abundant mediators to modify the neighboring microenvironment. However, the complex relationship among PCD status, immunosuppressive microenvironment, and immunotherapy is still poorly understood.

METHODS

Four independent glioma cohorts comprising 1,750 patients were enrolled for analysis. The relationships among PCD status, microenvironment cellular components, and biological phenotypes were fully explored. Tissues from our hospital and experiments in vitro and in vivo were used to confirm the role of ferroptosis in glioma.

RESULTS

Analyses to determine enriched PCD processes showed that ferroptosis was the main type of PCD in glioma. Enriched ferroptosis correlated with progressive malignancy, poor outcomes, and aggravated immunosuppression in glioblastoma (GBM) patients. Enhanced ferroptosis was shown to induce activation and infiltration of immune cells but attenuated antitumor cytotoxic killing. Tumor-associated macrophages (TAMs) were found to participate in ferroptosis-mediated immunosuppression. Preclinically, ferroptosis inhibition combined with Programmed Cell Death 1 (PD-1) and Programmed Cell Death Ligand-1 (PD-L1) blockade generated a synergistic therapeutic outcome in GBM murine models.

CONCLUSIONS

This work provides a molecular, clinical, and biological landscape of ferroptosis, suggesting a role of ferroptosis in glioma malignancy and a novel synergic immunotherapeutic strategy that combines immune checkpoint blockade treatment with ferroptosis inhibition.

Learning curve for robotic thyroidectomy using BABA: CUSUM analysis of a single surgeon's experience

BACKGROUND

This study assessed the safety and oncologic outcomes of robotic thyroidectomy via the bilateral axillary breast approach (BABA RT) for conventional open procedures. The learning curves of BABA RT were further evaluated.

METHODS

An exact 1:1 matching analysis was performed to compare the technical safety and oncologic outcomes between robotic thyroidectomy and conventional open surgery. Learning curves were assessed using cumulative summation analysis.

RESULTS

There was no significant difference in general characteristics, short time outcomes (including transient hypoparathyroidism, transient postoperative hoarseness, hematoma/seroma, mean postoperative hospital stay, and other complications), the number of retrieved central lymph nodes, and recurrence rates between robotic BABA and conventional groups. The mean number of retrieved lateral LNs in the robotic group was significantly less than those in the conventional group. The learning curve for working space making, robotic lobectomy, and total thyroidectomy are approximately 15, 30, and 20 cases, respectively. No differences except for operation time were found between the learning group and the proficient group.

CONCLUSIONS

Robotic thyroidectomy and neck dissection via BABA are feasible in terms of surgical completeness, surgical safety, and oncological safety. Our results provide a criterion for judging whether the surgeon has entered the stable stage of robotic thyroidectomy via BABA in terms of the operative time.

A Novel lncRNA Panel Related to Ferroptosis, Tumor Progression, and Microenvironment is a Robust Prognostic Indicator for Glioma Patients

Objective: To establish a lncRNA panel related to ferroptosis, tumor progression, and microenvironment for prognostic estimation in patients with glioma.

Methods: LncRNAs associated with tumor progression and microenvironment were screened via the weighted gene co-expression network analysis (WGCNA). Overlapped lncRNAs highlighted in WGCNA, related to ferroptosis, and incorporated in Chinese Glioma Genome Atlas (CGGA) were identified as hub lncRNAs. With expression profiles of the hub lncRNA, we conducted the least absolute shrinkage and selection operator (LASSO) regression and built a ferroptosis-related lncRNA signature to separate glioma patients with distinct survival outcomes. The lncRNA signature was validated in TCGA, the CGGA_693, and CGGA_325 cohorts using Kaplan-Meier survival analysis and ROC curves. The ferroptosis-related lncRNA panel was validated with 15 glioma samples using quantitative real-time PCR (qRT-PCR). Multivariate Cox regression was performed, and a nomogram was mapped and validated. Immune infiltration correlated to the signature was explored using TIMER and CIBERSORT algorithms.

Results: The present study identified 30 hub lncRNAs related to ferroptosis, tumor progression, and microenvironment. With the 30 hub lncRNAs, we developed a lncRNA signature with distinct stratification of survival chance in patients with glioma in two independent cohorts (HRs>1, p < 0.05). The lncRNA signature revealed a panel of 14 lncRNAs, i.e., APCDD1L-AS1, H19, LINC00205, LINC00346, LINC00475, LINC00484, LINC00601, LINC00664, LINC00886, LUCAT1, MIR155HG, NEAT1, PVT1, and SNHG18. These lncRNA expressions were validated in clinical specimens using qRT-PCR. Robust predictive accuracies of the signature were present across different datasets at multiple timepoints. With univariate and multivariate regressions, we demonstrated that the risk score based on the lncRNA signature is an independent prognostic indicator after clinical factors were adjusted. A nomogram was constructed with these prognostic factors, and it has demonstrated decent classification and accuracy. Additionally, the signature-based classification was observed to be correlated with multiple clinical characteristics and molecular subtypes. Further, extensive immune cells were upregulated in the high-risk group, such as CD8⁺ T cell, neutrophil, macrophage, and myeloid dendritic cell, indicating increased immune infiltrations.

Conclusion: We established a novel ferroptosis-related lncRNA signature that could effectively stratify the prognosis of glioma patients with adequate predictive performance.

Genetic Profiles of Ferroptosis in Malignant Brain Tumors and Off-Target Effects of Ferroptosis Induction

Glioblastoma represents the most devastating form of human brain cancer, associated with a very poor survival rate of patients. Unfortunately, treatment options are currently limited and the gold standard pharmacological treatment with the chemotherapeutic drug temozolomide only slightly increases the survival rate. Experimental studies have shown that the efficiency of temozolomide can be improved by inducing ferroptosis – a recently discovered form of cell death, which is different from apoptosis, necrosis, or necroptosis and, which is characterized by lipid peroxidation and reactive oxygen species accumulation. Ferroptosis can also be activated to improve treatment of malignant stages of neuroblastoma, meningioma, and glioma. Due to their role in cancer treatment, ferroptosis-gene signatures have recently been evaluated for their ability to predict survival of patients. Despite positive effects during chemotherapy, the drugs used to induce ferroptosis – such as erastin and sorafenib – as well as genetic manipulation of key players in ferroptosis – such as the cystine-glutamate exchanger xCT and the glutathione peroxidase GPx4 – also impact neuronal function and cognitive capabilities. In this review, we give an update on ferroptosis in different brain tumors and summarize the impact of ferroptosis on healthy tissues.

A Prognostic Pyroptosis-Related lncRNAs Risk Model Correlates With the Immune Microenvironment in Colon Adenocarcinoma

Recent studies have indicated that long non-coding RNAs (lncRNAs) may participate in the regulation of tumor cell proptosis. However, the connection between lncRNA expression and pyroptosis remains unclear in colon adenocarcinoma (COAD). This study aims to explore and establish a prognostic signature of COAD based on the pyroptosis-related lncRNAs. We identify 15 prognostic pyroptosis-related lncRNAs (ZNF667-AS1, OIP5-AS1, AL118506.1, AF117829.1, POC1B-AS1, CCDC18-AS1, THUMPD3-AS1, FLNB-AS1, SNHG11, HCG18, AL021707.2, UGDH-AS1, LINC00641, FGD5-AS1 and AC245452.1) from the TCGA-COAD dataset and use them to construct the risk model. After then, this pyroptosis-related lncRNA signature is validated in patients from the GSE17536 dataset. The COAD patients are divided into low-risk and high-risk groups by setting the median risk score as the cut-off point and represented differences in the immune microenvironment. Hence, we construct the immune risk model based on the infiltration levels of ssGSEA immune cells. Interestingly, the risk model and immune risk model are both independent prognostic risk factors. Therefore, a nomogram combined risk score, immune risk score with clinical information which is meaningful in univariate and multivariate Cox regression analysis is established to predict the overall survival (OS) of COAD patients. In general, the signature consisted of 15 pyroptosis-related lncRNAs and was proved to be associated with the immune landscape of COAD patients.

Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma

Background

m6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients.

Materials and Methods

RNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate and multivariate Cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan–Meier survival analysis and receiver operating characteristics (ROC). Immune estimation of LUSC was downloaded from TIMER, and the correlations between the risk score and various immune cells infiltration were analyzed using various methods. Differences in immune functions and expression of immune checkpoint inhibitors and m6A regulators between high-risk and low-risk groups were further explored. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to explore the biological functions of AL122125.1.

Results

A total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. A further multivariate Cox regression assay constructed a risk model consisting of two lncRNAs (AL122125.1 and HORMAD2-AS1). The Kaplan–Meier analysis and area under the curve indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-related lncRNAs were immune-associated. There were significant correlations between risk score and immune cell infiltration, immune functions, and expression of immune checkpoint inhibitors. Meanwhile, there were significant differences in the expression of m6A regulators between the high- and low-risk groups. Moreover, GO and KEGG analyses revealed that the upregulated expression of AL122125.1 was tumor-related.

Conclusion

In this study, we constructed an m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide a novel insight to the prognosis and treatment of LUSC patients.

A Promising Esophageal Cancer Prognostic Signature of Ferroptosis-Related LncRNA to Predict Immune Scenery and Immunotherapy Response

Purpose

Ferroptosis and long non-coding RNA (lncRNA) expression signatures have been associated with the clinical progression and immune-contexture of different solid tumors. The study aimed to identify a prognostic signature of ferroptosis-related lncRNAs (falncRNAs) to forecast the immune scenery and immunotherapy response in esophageal cancer (EC).

Patients and Methods

Gene expression profiles of EC were extracted from The Cancer Genome Atlas (TCGA) database, and ferroptosis-related genes were downloaded from the FerrDb database, which identified differentially expressed falncRNAs (DEfalncRNAs) via differential analysis. DEfalncRNA pairs associated with prognosis were identified by constructing a matrix, univariate and least absolute shrinkage and selection operator (LASSO) analysis. The prognostic signature was constructed by multivariate analysis. We appraised the forecasting capability of prognostic signature in survival, clinicopathological features, immune landscape, efficacy of immunotherapy, and drug sensitivity. The potential molecular mechanism of signature was investigated by gene set enrichment analysis (GSEA).

Results

We obtained 18 DEfalncRNA pairs to define a novel prognostic signature that was determined on a discovery cohort of 158 tumor samples and 11 adjacent normal tissues from TCGA and internally validated, with the definition of high- vs low-risk groups based on 3 years overall survival. We demonstrated that the high- vs low-risk groups differed for clinical parameters and computationally predicted drug sensitivity and tumor immune contexture, with the high-risk group having worse survival, more aggressive disease (node involvement, metastasis), reduced drug sensitivity, higher tumor mutation load, and gene signatures of infiltration of pro-tumoral immune cell subsets. The GSEA results revealed that ferroptosis and immunoregulatory pathways were significantly enriched in the high-risk group.

Conclusion

The prognostic signature based on falncRNAs has the potential to forecast the survival, immune scenery, efficacy of immunotherapy, and drug sensitivity of EC, which is helpful for clinical prediction and individualized treatment.