Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma

Cuproptosis-related lncRNA signature as a prognostic tool and therapeutic target in diffuse large B cell lymphoma

Cuproptosis is a newly defined form of programmed cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis and metastasis. However, whether cuproptosis-related lncRNAs are involved in the pathogenesis of diffuse large B cell lymphoma (DLBCL) remains unclear. This study aimed to identify the prognostic signatures of cuproptosis-related lncRNAs in DLBCL and investigate their potential molecular functions. RNA-Seq data and clinical information for DLBCL were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Cuproptosis-related lncRNAs were screened out through Pearson correlation analysis. Utilizing univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis, we identified seven cuproptosis-related lncRNAs and developed a risk prediction model to evaluate its prognostic value across multiple groups. GO and KEGG functional analyses, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. Additionally, drug sensitivity analysis identified drugs with potential efficacy in DLBCL. Finally, the protein-protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). We identified a set of seven cuproptosis-related lncRNAs including LINC00294, RNF139-AS1, LINC00654, WWC2-AS2, LINC00661, LINC01165 and LINC01398, based on which we constructed a risk model for DLBCL. The high-risk group was associated with shorter survival time than the low-risk group, and the signature-based risk score demonstrated superior prognostic ability for DLBCL patients compared to traditional clinical features. By analyzing the immune landscapes between two groups, we found that immunosuppressive cell types were significantly increased in high-risk DLBCL group. Moreover, functional enrichment analysis highlighted the association of differentially expressed genes with metabolic, inflammatory and immune-related pathways in DLBCL patients. We also found that the high-risk group showed more sensitivity to vinorelbine and pyrimethamine. A cuproptosis-related lncRNA signature was established to predict the prognosis and provide insights into potential therapeutic strategies for DLBCL patients.

Pyroptosis-Derived Long Noncoding RNA Profiles Reveal a Novel Signature for Evaluating the Prognosis of Patients With Lung Adenocarcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms.

RESULTS

In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts.

CONCLUSION

We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets.

lncRNA CYTOR promotes lung adenocarcinoma gemcitabine resistance and epithelial-mesenchymal transition by sponging miR-125a-5p and upregulating ANLN and RRM2

Lung adenocarcinoma (LUAD) is one of the most aggressive types of lung cancer. The prognosis of LUAD patients remains poor, and the overall efficacy of gemcitabine-based chemotherapy is still unsatisfactory. Long noncoding RNAs (lncRNAs) play important roles in several cancer types by interacting with multiple proteins, RNA, and DNA. However, the relationship between lncRNA dysregulation and gemcitabine resistance in LUAD has not been fully elucidated. In this study, lncRNA CYTOR expression and its association with the prognosis of LUAD patients are assessed by quantitative RT-PCR and Kaplan-Meier survival analysis. In vitro and in vivo functional studies are conducted to evaluate the biological functions of CYTOR in LUAD. The underlying mechanism regarding the tumor-promoting effects of CYTOR is explored using RNA immunoprecipitation, biotin-labelled RNA pulldown, luciferase reporter assays, and western blot analysis. We identify that CYTOR is an oncogenic lncRNA and is apparently upregulated in LUAD by analysing TCGA-LUAD data. High CYTOR expression is a poor prognostic factor for LUAD. Functional studies reveal that CYTOR confers LUAD cells with stronger resistance to gemcitabine treatment and upregulates the expression levels of epithelial-mesenchymal transition (EMT)-related proteins. Mechanically, CYTOR acts as a competitive endogenous RNA (ceRNA) to absorb miR-125a-5p, weakens the antitumor function of miR-125a-5p, and ultimately upregulates ANLN and RRM2 expressions. Taken together, this study explains the mechanism of lncRNA in the gemcitabine resistance of LUAD and formulates a theoretical framework for the in depth study of LUAD.

FFAR4 activation inhibits lung adenocarcinoma via blocking respiratory chain complex assembly associated mitochondrial metabolism

Despite notable advancements in the investigation and management of lung adenocarcinoma (LUAD), the mortality rate for individuals afflicted with LUAD remains elevated, and attaining an accurate prognosis is challenging. LUAD exhibits intricate genetic and environmental components, and it is plausible that free fatty acid receptors (FFARs) may bridge the genetic and dietary aspects. The objective of this study is to ascertain whether a correlation exists between FFAR4, which functions as the primary receptor for dietary fatty acids, and various characteristics of LUAD, while also delving into the potential underlying mechanism. The findings of this study indicate a decrease in FFAR4 expression in LUAD, with a positive correlation (P < 0.01) between FFAR4 levels and overall patient survival (OS). Receiver operating characteristic (ROC) curve analysis demonstrated a significant diagnostic value [area under the curve (AUC) of 0.933] associated with FFAR4 expression. Functional investigations revealed that the FFAR4-specific agonist (TUG891) effectively suppressed cell proliferation and induced cell cycle arrest. Furthermore, FFAR4 activation resulted in significant metabolic shifts, including a decrease in oxygen consumption rate (OCR) and an increase in extracellular acidification rate (ECAR) in A549 cells. In detail, the activation of FFAR4 has been observed to impact the assembly process of the mitochondrial respiratory chain complex and the malate-aspartate shuttle process, resulting in a decrease in the transition of NAD+ to NADH and the inhibition of LUAD. These discoveries reveal a previously unrecognized function of FFAR4 in the negative regulation of mitochondrial metabolism and the inhibition of LUAD, indicating its potential as a promising therapeutic target for the treatment and diagnosis of LUAD.

Molecular subgroup establishment and signature creation of lncRNAs associated with acetylation in lung adenocarcinoma

BACKGROUND

The significance of long non-coding RNAs (lncRNAs) as pivotal mediators of histone acetylation and their influential role in predicting the prognosis of lung adenocarcinoma (LUAD) has been increasingly recognized. However, there remains uncertainty regarding the potential utility of acetylation-related lncRNAs (ARLs) in prognosticating the overall survival (OS) of LUAD specimens.

METHODS

The RNA-Seq and clinical information were downloaded from The Cancer Genome Atlas (TCGA). Through the differential analysis, weighted correlation network analysis (WGCNA), Pearson correlation test and univariate Cox regression, we found out the prognosis associated ARLs and divided LUAD specimens into two molecular subclasses. The ARLs were employed to construct a unique signature through the implementation of the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Subsequently, the predictive performance was evaluated using ROC analysis and Kaplan-Meier survival curve analysis. Finally, ARL expression in LUAD was confirmed by quantitative real-time PCR (qRT-PCR).

RESULTS

We triumphantly built a ARLs prognostic model with excellent predictive accuracy for LUAD. Univariate and multivariate Cox analysis illustrated that risk model served as an independent predictor for influencing the overall survival OS of LUAD. Furthermore, a nomogram exhibited strong prognostic validity. Additionally, variations were observed among subgroups in the field of immunity, biological functions, drug sensitivity and gene mutations within the field.

CONCLUSIONS

Nine ARLs were identified as promising indicators of personalized prognosis and drug selection for people suffering with LUAD.

Novel Insights on Ferroptosis Modulation as Potential Strategy for Cancer Treatment: When Nature Kills

Significance: The multifactorial nature of the mechanisms implicated in cancer development still represents a major issue for the success of established antitumor therapies. The discovery of ferroptosis, a novel form of programmed cell death distinct from apoptosis, along with the identification of the molecular pathways activated during its execution, has led to the uncovering of novel molecules characterized by ferroptosis-inducing properties. Recent advances: As of today, the ferroptosis-inducing properties of compounds derived from natural sources have been investigated and interesting findings have been reported both in vitro and in vivo. Critical Issues: Despite the efforts made so far, only a limited number of synthetic compounds have been identified as ferroptosis inducers, and their utilization is still limited to basic research. In this review, we analyzed the most important biochemical pathways involved in ferroptosis execution, with particular attention to the newest literature findings on canonical and non-canonical hallmarks, together with mechanisms of action of natural compounds identified as novel ferroptosis inducers. Compounds have been classified based on their chemical structure, and modulation of ferroptosis-related biochemical pathways has been reported. Future Directions: The outcomes herein collected represent a fascinating starting point from which to take hints for future drug discovery studies aimed at identifying ferroptosis-inducing natural compounds for anticancer therapies. Antioxid. Redox Signal. 40, 40-85.

A novel neutrophil extracellular traps-related lncRNA signature predicts prognosis in patients with early-stage lung adenocarcinoma

BACKGROUND

Neutrophil extracellular traps (NETs) could entrap tumour cells and promote their dissemination and metastasis. Further analysis of NETs-related molecules is expected to provide a new strategy for prognosis prediction and treatment of lung adenocarcinoma (LUAD) patients.

METHODS

The model construction was established through co-expression analysis, Lasso Cox regression, univariate and multivariate COX regression, Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway. The potential drugs and analysed drug sensitivity were screened by pRRophetic packages.

RESULTS

In this study, we constructed a 15 NETs-related long non-coding RNAs (lncRNAs) prognostic prediction model (AC091057.1, SPART-AS1, AC023796.2, AL031600.2, AC084781.1, AC032011.1, FAM66C, C026355.2, AL096870.2, AC092718.5, PELATON, AC008635.1, AL162632.3, AC087501.4 and AC123768.3) for patients with early-stage LUAD based on public databases and datasets. The signature is associated with immune cell functions, tumour mutation burden and treatment sensitivity in LUAD patients. Additionally, we found that FAM66C is highly expressed in lung cancer patients for the first time, which is associated with poor prognosis. FAM66C knockdown significantly inhibited the proliferation and migration ability of the tumour cells.

CONCLUSIONS

In conclusion, this model is a new and effective prognostic and efficacy predictive biomarker, FAM66C plays an oncogene role in the process of LUAD development. It may provide a new theoretical basis for the clinical diagnosis and treatment in LUAD patients in early stage.

Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

OBJECTIVE

To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management.

METHODS

Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group.

RESULTS

We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4.

CONCLUSION

The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.

Identification of a novel intermittent hypoxia-related prognostic lncRNA signature and the ceRNA of lncRNA GSEC/miR-873-3p/EGLN3 regulatory axis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is still the most prevalent type of respiratory cancer. Intermittent hypoxia can increase the mortality and morbidity associated with lung cancer. Long non-coding RNAs (lncRNAs) are crucial in lung adenocarcinoma. However, the effects of intermittent hypoxia-related long non-coding RNAs (IHRLs) on lung adenocarcinoma are still unknown.

Method

In the current research, eight IHRLs were selected to create a prognostic model. The risk score of the prognostic model was evaluated using multivariate and univariate analyses, and its accuracy and reliability were validated using a nomogram and ROC. Additionally, we investigated the relationships between IHRLs and the immune microenvironment.

Result

Our analysis identified GSEC, AC099850.3, and AL391001.1 as risk lncRNAs, while AC010615.2, AC010654.1, AL513550.1, LINC00996, and LINC01150 were categorized as protective lncRNAs. We observed variances in the expression of seven immune cells and 15 immune-correlated pathways between the two risk groups. Furthermore, our results confirmed the ceRNA network associated with the intermittent hypoxia-related lncRNA GSEC/miR-873-3p/EGLN3 regulatory pathway. GSEC showed pronounced expression in lung adenocarcinoma tissues and specific cell lines, and its inhibition resulted in reduced proliferation and migration in A549 and PC9 cells. Intriguingly, GSEC manifested oncogenic properties by sponging miR-873-3p and demonstrated a tendency to modulate EGLN3 expression favorably.

Conclusion

GSEC acts as an oncogenic lncRNA by interacting with miR-873-3p, modulating EGLN3 expression. This observation underscores the potential of GSEC as a diagnostic and therapeutic target for LUAD.

Identification of novel lactate metabolism-related lncRNAs with prognostic value for bladder cancer

Background: Bladder cancer (BCA) has high recurrence and metastasis rates, and current treatment options show limited efficacy and significant adverse effects. It is crucial to find diagnostic markers and therapeutic targets with clinical value. This study aimed to identify lactate metabolism-related lncRNAs (LM_lncRNAs) to establish a model for evaluating bladder cancer prognosis. Method: A risk model consisting of lactate metabolism-related lncRNAs was developed to forecast bladder cancer patient prognosis using The Cancer Genome Atlas (TCGA) database. Kaplan‒Meier survival analysis, receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to evaluate the reliability of risk grouping for predictive analysis of bladder cancer patients. The results were also validated in the validation set. Chemotherapeutic agents sensitive to lactate metabolism were assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: As an independent prognostic factor for patients, lactate metabolism-related lncRNAs can be used as a nomogram chart that predicts overall survival time (OS). There were significant differences in survival rates between the high-risk and low-risk groups based on the Kaplan‒Meier survival curve. decision curve analysis and receiver operating characteristic curve analysis confirmed its good predictive capacity. As a result, 22 chemotherapeutic agents were predicted to positively affect the high-risk group. Conclusion: An lactate metabolism-related lncRNA prediction model was proposed to predict the prognosis for patients with bladder cancer and chemotherapeutic drug sensitivity in high-risk groups, which provided a new idea for the prognostic evaluation of the clinical treatment of bladder cancer.

Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma

OBJECTIVE

Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD).

METHODS

The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets.

RESULTS

A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients.

CONCLUSION

The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.

Construction of a two-gene prognostic model related to ferroptosis in renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a common and aggressive tumor. A newly discovered form of programmed cell death, ferroptosis, plays an important role in tumor development and progression. However, a clear prognostic correlation between Ferroptosis-related genes (FRGs) and RCC has not yet been established. In this study, prognostic markers associated with FRGs were investigated to improve the therapeutic, diagnostic, and preventive strategies available to patients with renal cancer.

METHODS

The present study analyzed the predictive value of 23 FRGs in RCC through bioinformatics techniques, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) tools, Kaplan-Meier survival analysis, Cox regression modeling, tumor mutational burden (TMB), CIBERSORT, and half maximal inhibitory concentration (IC50) difference analysis.

RESULTS

We screened FRGs by differentially expressed genes (DEGs) and overall survival (OS). Four candidate genes were obtained by hybridization. Then, we constructed a two-gene prognostic signature (NCOA4 and CDKN1A) via univariate Cox regression and multivariate stepwise Cox regression, which classified RCC patients into high- and low-risk groups, and patients in the high-risk group were found to have worse OS and progression-free survival (PFS). We also found that patients with higher TNM stage, T stage, and M stage had higher risk scores than those with lower TNM stage, T stage, and M stage (P<0.05). Males had higher risk scores than females. This signature was identified as an independent prognostic indicator for RCC. These results were validated in both the test cohort and the entire cohort. In addition, we also constructed a nomogram that predicted the OS in RCC patients, the consistency index (C-index) of the nomogram was 0.731 [95% confidence interval (CI): 0.672-0.790], the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.728, 0.704, and 0.898 at 1-, 3-, and 5-year, respectively, which shows that nomogram has good prediction ability. and we also analyzed the immune status and drug sensitivity between the high- and low-risk groups.

CONCLUSIONS

We constructed a prognostic model associated with ferroptosis, which may provide clinicians with a reliable predictive assessment tool and offer new perspectives for the future clinical management of RCC.

Immunogenic cell death-related long noncoding RNA influences immunotherapy against lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths, accounting for over a million deaths worldwide annually. Immunogenic cell death (ICD) elicits an adaptive immune response. However, the role of ICD-related long noncoding RNAs (lncRNAs) in LUAD is unknown. In this study, we investigated the characteristics of the tumor microenvironment in LUAD, the prognostic significance of ICD-related lncRNAs, and the half-maximal inhibitory concentration (IC50) of possible chemotherapeutic drugs. We sorted prognostic lncRNAs using univariate Cox regression and constructed a risk signature based on them. We then confirmed the model's accuracy and generated a nomogram. Additionally, we performed immune microenvironment analysis, somatic mutation calculation, Tumor Immune Dysfunction and Exclusion (TIDE) analysis, and anticancer pharmaceutical IC50 prediction. Least absolute shrinkage and selection operator Cox regression identified 27 prognostic lncRNAs related to ICD, and a unique risk signature using 10 ICD-related lncRNAs was constructed. The risk score was confirmed to be a reliable predictor of survival, with the highest c-index score. The signature had a remarkable predictive performance with clinical applicability and could accurately predict the overall survival in LUAD. Furthermore, the lncRNA signature was closely associated with immunocyte invasion. We also analyzed the correlation between the risk score, tumor-infiltrating immune cells, and prognosis and identified high immune and ESTIMATE scores in low-risk patients. Moreover, we observed elevated checkpoint gene expression and low TIDE scores in high-risk patients, indicating a good immunotherapy response. Finally, high-risk patients were shown to be susceptible to anticancer medications. Therefore, our unique risk signature comprising 10 ICD-related lncRNAs was demonstrated to indicate the characteristics of the tumor-immune microenvironment in LUAD, predict patients' overall survival, and guide individualized treatment.

Regulation of ncRNAs involved with ferroptosis in various cancers

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

Exosomal circ-ADRM1 promotes lung adenocarcinoma progression and induces macrophage M2 polarization through regulating MMP14 mRNA and protein

OBJECTIVE

Lung adenocarcinoma (LUAD) is one of the frequent subtypes of lung cancer, featuring high rates of incidence and mortality. Matrix metalloproteinase 14 (MMP14) is known as a regulator in multiple cancers, whereas its upstream molecular mechanism remains to be investigated. This study aims to reveal the upstream molecular mechanism of MMP14 in LUSC progression.

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were conducted to examine the levels of MMP14 mRNA and protein in LUAD cells, respectively. Cell counting kit-8 (CCK-8), transwell assay and wound healing assay were implemented to unveil LUAD cell proliferation, migration and invasion after indicated transfections. Flow cytometry analysis was applied to evaluate macrophage polarization. Mechanism experiments such as western blot, co-immunoprecipitation (Co-IP), RNA pulldown assay, luciferase reporter assay and RNA-binding protein immunoprecipitation (RIP) assay were used to explore relevant molecular mechanisms.

RESULTS

MMP14 facilitated LUAD cell proliferation, invasion and migration. MMP14 is the target gene of miR-1287-5p. Circ-ADRM1 upregulates MMP14 expression through sponging miR-1287-5p. Circ-ADRM1 recruits USP12 to impede the ubiquitination of MMP14 protein, thereby enhancing the stability of MMP14 protein. LUAD-derived exosomes induced macrophage M2 polarization by delivering circ-ADRM1.

CONCLUSIONS

Circ-ADRM1 promotes LUAD cell proliferation, invasion and migration through upregulating MMP14. Additionally, circ-ADRM1 induces macrophage M2 polarization in an exosome-dependent manner.

Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database

PURPOSE

Existing biomarkers for diagnosing and predicting metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models with multiple markers may provide better prognostic factors for accurate diagnosis and prediction of metastatic LUAD.

METHODS

An animal model of LUAD metastasis was constructed using CRISPR technology, and genes related to LUAD metastasis were screened by mRNA sequencing of normal and metastatic tissues. The immune characteristics of different subtypes were analyzed, and differentially expressed genes were subjected to survival and Cox regression analyses to identify the specific genes involved in metastasis for constructing a prediction model. The biological function of RFLNA was verified by analyzing CCK-8, migration, invasion, and apoptosis in LUAD cell lines.

RESULTS

We identified 108 differentially expressed genes related to metastasis and classified LUAD samples into two subtypes according to gene expression. Subsequently, a prediction model composed of eight metastasis-related genes (RHOBTB2, KIAA1524, CENPW, DEPDC1, RFLNA, COL7A1, MMP12, and HOXB9) was constructed. The areas under the curves of the logistic regression and neural network were 0.946 and 0.856, respectively. The model effectively classified patients into low- and high-risk groups. The low-risk group had a better prognosis in both the training and test cohorts, indicating that the prediction model had good diagnostic and predictive power. Upregulation of RFLNA successfully promoted cell proliferation, migration, invasion, and attenuated apoptosis, suggesting that RFLNA plays a role in promoting LUAD development and metastasis.

CONCLUSION

The model has important diagnostic and prognostic value for metastatic LUAD and may be useful in clinical applications.

Identification of platinum resistance-related gene signature for prognosis and immune analysis in bladder cancer

Purpose: Currently, there is limited knowledge about platinum resistance-related long non-coding RNAs (lncRNAs) in bladder cancer. We aim to identify platinum resistance-related lncRNAs and construct a risk model for accurate prognostic prediction of bladder cancer. Methods: Transcriptomic and clinical data were extracted from The Cancer Genome Atlas (TCGA) database, and platinum resistance-related genes were obtained from HGSOC-Platinum. The platinum resistance-related lncRNAs were obtained by the Spearman correlation analysis. Then, we constructed a risk score model through Cox regression analysis and the LASSO algorithm. The model was verified by analyzing the median risk score, Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and heatmap. We also developed a nomogram and examined the relationship between the risk score model, immune landscape, and drug sensitivity. Lastly, we assessed the differential expression of PRR-lncRNAs in the cisplatin-resistant bladder cancer cell line and the normal bladder cancer cell line using qRT-PCR. Results: We developed and validated an eight-platinum resistance-related lncRNA risk model for bladder cancer. The risk model showed independent prognostic significance in univariate and multivariate Cox analyses. Based on multivariate analysis, we developed a nomogram. The modified model is both good predictive and clinically relevant after evaluation. Furthermore, immune-related and drug-sensitivity analyses also showed significant differential expression between high and low-risk groups. The qRT-PCR demonstrated that most of the lncRNAs were upregulated in cisplatin-resistance cancerous tissues than in control tissues. Conclusion: We have developed a predictive model based on eight platinum resistance-related lncRNAs, which could add meaningful information to clinical decision-making.

Predictive Value of Serum Heat Shock Protein 90α on the Prognosis of Patients with Lung Adenocarcinoma

Purpose

Heat shock protein 90α (HSP90α) is highly expressed in tumors, and predicts tumor progression. This study analyzed the correlation between the expression level of HSP90α in the serum and the prognosis of patients with lung adenocarcinoma.

Patients and methods

The medical records of patients with 228 lung adenocarcinoma from September 2015 to December 2021 were analyzed. HSP90α expression in the patients' serum was detected by ELISA and the cut-off value (93.76 ng/mL) was determined according to the ROC curve, then the patients were divided into high- and low-level groups. The differences in the medical records of the two groups were compared using the X² test, and Univariate and multivariate Cox regression analyses showed that serum HSP90α level were independent risk factors for both PFS and OS (P < 0.05).

Results

HSP90α was positively correlated with TNM staging (P < 0.01) by One-way analysis of variance. The results of the correlation analysis and the Kaplan-Meier survival curve showed that the expression levels of HSP90α and CEA of patients were positively correlated (R=0.54, P < 0.001), and patients with high HSP90α and CEA levels had the worst OS (P < 0.001).

Conclusion

HSP90α expression is negatively correlated with the prognosis of patients with lung adenocarcinoma and is a potential prognostic marker.

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

Ferroptosis is a recently established type of iron-dependent programmed cell death. Growing studies have focused on the function of ferroptosis in cancers, including lung adenocarcinoma (LUAD). However, the factors involved in the regulation of ferroptosis-related genes are not fully understood. In this study, we collected data from lung adenocarcinoma datasets of the Cancer Genome Atlas (TCGA-LUAD). The expression profiles of 60 ferroptosis-related genes were screened, and two differentially expressed ferroptosis subtypes were identified. We found the two ferroptosis subtypes can predict clinical outcomes and therapeutic responses in LUAD patients. Furthermore, key long non-coding RNAs (lncRNAs) were screened by single factor Cox and least absolute shrinkage and selection operator (LASSO) based on which co-expressed with the 60 ferroptosis-related genes. We then established a risk score model which included 13 LUAD ferroptosis-related lncRNAs with a multi-factor Cox regression. The risk score model showed a good performance in evaluating the outcome of LUAD. What's more, we divided TCGA-LUAD tumor samples into two groups with high- and low-risk scores and further explored the differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration among different LUAD tumor risk score groups and evaluate the predictive ability of risk score for immunotherapy benefit. Our findings provide good support for immunotherapy in LUAD in the future.

Long non-coding RNA lnc-CHAF1B-3 promotes renal interstitial fibrosis by regulating EMT-related genes in renal proximal tubular cells

Renal interstitial fibrosis (RIF) is a common pathological manifestation of chronic kidney diseases. Epithelial-mesenchymal transition (EMT) of tubular epithelial cells is considered a major cause of RIF. Although long non-coding RNAs (lncRNAs) are reportedly involved in various pathophysiological processes, the roles and underlying molecular mechanisms of lncRNAs in the progression of RIF are poorly understood. In this study, we investigated the function of lncRNAs in RIF. Microarray assays showed that expression of the lncRNA lnc-CHAF1B-3 (also called claudin 14 antisense RNA 1) was significantly upregulated in human renal proximal tubular cells by both transforming growth factor-β1 (TGF-β1) and hypoxic stimulation, accompanied with increased expression of EMT-related genes. Knockdown of lnc-CHAF1B-3 significantly suppressed TGF-β1-induced upregulated expression of collagen type I alpha 1, cadherin-2, plasminogen activator inhibitor-1, snail family transcriptional repressor I (SNAI1) and SNAI2. Quantitative reverse transcriptase PCR analyses of paraffin-embedded kidney biopsy samples from IgA nephropathy patients revealed lnc-CHAF1B-3 expression was correlated positively with urinary protein levels and correlated negatively with estimated glomerular filtration rate. In situ hybridization demonstrated that lnc-CHAF1B-3 is expressed only in proximal tubules. These findings suggest lnc-CHAF1B-3 affects the progression of RIF by regulating EMT-related signaling. Thus, lnc-CHAF1B-3 is a potential target in the treatment of RIF.

A novel prognostic signature for lung adenocarcinoma based on cuproptosis-related lncRNAs: A Review

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease with complex pathogenesis, high mortality, and poor prognosis. Cuproptosis is a new type of programmed cell death triggered by copper accumulation that may play an important role in cancer. LncRNAs are becoming valuable prognostic factors in cancer patients. The effect of cuproptosis-related lncRNAs (CRlncRNAs) on LUAD has not been clarified. Based on the Cancer Genome Atlas database, CRlncRNAs were screened by co-expression analysis of cuproptosis- related genes and lncRNAs. Using CRlncRNAs, Cox and LASSO regression analyses constructed a risk prognostic model. The predictive efficacy of the model was assessed and validated using survival analysis, receiver operating characteristic curve, univariate and multifactor Cox regression analysis, and principal component analysis. A nomogram was constructed and calibration curves were applied to enhance the predictive efficacy of the model. Tumor Mutational Burden analysis and chemotherapeutic drug sensitivity prediction were performed to assess the clinical feasibility of the risk model. The novel prognostic signature consisted of 5 potentially high-risk CRlncRNAs, MAP3K20-AS1, CRIM1-DT, AC006213.3, AC008035.1, and NR2F2-AS1, and 5 potentially protective CRlncRNAs, AC090948.1, AL356481.1, AC011477.2, AL031600.2, and AC026355.2, which had accurate and robust predictive power for LUAD patients. Collectively, the novel prognostic signature constructed based on CRlncRNAs can effectively assess and predict the prognosis of patients and provide a new perspective for the diagnosis and treatment of LUAD.

An efficient five-lncRNA signature for lung adenocarcinoma prognosis, with AL606489.1 showing sexual dimorphism

Background: Lung adenocarcinoma (LUAD) is a sex-biased and easily metastatic malignant disease. A signature based on 5 long non-coding RNAs (lncRNAs) has been established to promote the overall survival (OS) prediction effect on LUAD.

Methods: The RNA expression profiles of LUAD patients were obtained from The Cancer Genome Atlas. OS-associated lncRNAs were identified based on the differential expression analysis between LUAD and normal samples followed by survival analysis, univariate and multivariate Cox proportional hazards regression analyses. OS-associated lncRNA with sex dimorphism was determined based on the analysis of expression between males and females. Functional enrichment analysis of the Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed to explore the possible mechanisms of 5-lncRNA signatures.

Results: A 5-lncRNA signature (composed of AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1) was found to be effective in predicting high-risk LUAD patients as well as applicable to female and male subgroups and &lt;65-year and ≥65-year age subgroups. The forecasted effect of the 5-lncRNA signature was more efficient and stable than the TNM stage and other clinical risk factors (such as sex and age). Functional enrichment analysis revealed that the mRNA co-expressed with these five OS-related lncRNAs was associated with RNA regulation within the nucleus. AL606489.1 demonstrated a sexual dimorphism that may be associated with microtubule activity.

Conclusion: Our 5-lncRNA signature could efficaciously predict the OS of LUAD patients. AL606489.1 demonstrated gender dimorphism, which provides a new direction for mechanistic studies on sexual dimorphism.

LncRNA LINC01537 Promotes Gastric Cancer Metastasis and Tumorigenesis by Stabilizing RIPK4 to Activate NF-κB Signaling

Many studies reported that long noncoding RNAs (lncRNAs) play a critical role in gastric cancer (GC) metastasis and tumorigenesis. However, the underlying mechanisms of lncRNAs in GC remain unexplored to a great extent. LINC01537 expression level was detected using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Its biological roles in GC were then investigated using functional experiments. In order to investigate the underlying mechanism of LINC01537 in GC, RNA pull-down, RNA immunoprecipitation, and ubiquitination assays were performed. LINC01537 was significantly overexpressed in GC tissues and associated with a poor prognosis. Functional experimental results revealed that LINC01537 promoted the proliferation, invasion, and migration of GC cells. The animal experiments revealed that LINC01537 promoted tumorigenesis and metastasis in vivo. Mechanistically, LINC01537 stabilizes RIPK4 by reducing the binding of RIPK4 to TRIM25 and reducing its ubiquitination degradation, thereby promoting the expression of the NF-κB signaling pathway. According to our findings, the LINC01537-RIPK4-NF-κB axis promoted GC metastasis and tumorigenesis.

Prognosis and immune response of a cuproptosis-related lncRNA signature in low grade glioma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in low grade glioma (LGG) at present. In this study, data on low grade glioma patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied univariate/multivariate, and LASSO regression algorithms, finally identified 11 lncRNAs for constructing prognosis prediction models, and constructed a risk scoring model. The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of LGG patients. Furthermore, the analyses of immunotherapy, immune microenvironment, as well as functional enrichment were also performed. Finally, we verified the expression of these six prognostic key lncRNAs using real-time polymerase chain reaction (RT-PCR). In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in LGG and aims to open up new directions for LGG therapy.

Necroptosis-related lncRNAs: Combination of bulk and single-cell sequencing reveals immune landscape alteration and a novel prognosis stratification approach in lung adenocarcinoma

Necroptosis, which is recently recognized as a form of programmed cell death, plays a critical role in cancer biology, including tumorigenesis and cancer immunology. It was recognized not only to defend against tumor progression by suppressing adaptive immune responses but also to promote tumorigenesis and cancer metastasis after recruiting inflammatory responses. Thus the crucial role of necrosis in tumorigenesis has attracted increasing attention. Due to the heterogeneity of the tumor immune microenvironment (TIME) in lung adenocarcinoma (LUAD), the prognosis and the response to immunotherapy vary distinctly across patients, underscoring the need for a stratification algorithm for clinical practice. Although previous studies have formulated the crucial role of lncRNAs in tumorigenicity, the relationship between necroptosis-related lncRNAs, TIME, and the prognosis of patients with LUAD was still elusive. In the current study, a robust and novel prognostic stratification model based on Necroptosis-related LncRNA Risk Scoring (NecroLRS) and clinicopathological parameters was constructed and systemically validated in both internal and external validation cohorts. The expression profile of four key lncRNAs was further validated by qRT-PCR in 4 human LUAD cell lines. And a novel immune landscape alteration was observed between NecroLRS-High and -Low patients. To further elucidate the mechanism of necroptosis in the prognosis of LUAD from a single-cell perspective, a novel stratification algorithm based on K-means clustering was introduced to extract both malignant and NecroLRS-High subsets from epithelial cells. And the necroptosis-related immune infiltration landscape and developmental trajectory were investigated respectively. Critically, NecroLRS was found to be positively correlated with neutrophil enrichment, inflammatory immune response, and malignant phenotypes of LUAD. In addition, novel ligand-receptor pairs between NecroLRS-High cells and other immunocytes were investigated and optimal therapeutic compounds were screened to provide potential targets for future studies. Taken together, our findings reveal emerging mechanisms of necroptosis-induced immune microenvironment alteration on the deteriorative prognosis and may contribute to improved prognosis and individualized precision therapy for patients with LUAD.

Iron metabolism, ferroptosis, and lncRNA in cancer: knowns and unknowns

Cancer cells undergo substantial metabolic alterations to sustain increased energy supply and uncontrolled proliferation. As an essential trace element, iron is vital for many biological processes. Evidence has revealed that cancer cells deploy various mechanisms to elevate the cellular iron concentration to accelerate proliferation. Ferroptosis, a form of cell death caused by iron-catalyzed excessive peroxidation of polyunsaturated fatty acids (PUFAs), is a promising therapeutic target for therapy-resistant cancers. Previous studies have reported that long noncoding RNA (lncRNA) is a group of critical regulators involved in modulating cell metabolism, proliferation, apoptosis, and ferroptosis. In this review, we summarize the associations among iron metabolism, ferroptosis, and ferroptosis-related lncRNA in tumorigenesis. This information will help deepen understanding of the role of lncRNA in iron metabolism and raise the possibility of targeting lncRNA and ferroptosis in cancer combination therapy.

Identifying the EMT-related signature to stratify prognosis and evaluate the tumor microenvironment in lung adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is a critical process in tumor invasion and metastasis. EMT has been shown to significantly influence the invasion, metastasis, and poor prognosis in lung adenocarcinoma (LUAD). This study aimed to develop a novel EMT-related prognostic model capable of predicting overall survival (OS) in patients with LUAD.

Methods: A total of 283 LUAD patients from TCGA RNA-seq dataset were assigned to a training cohort for model building, and 310 LUAD patients from GEO RNA-seq dataset were assigned to a validation cohort. EMT genes were acquired from MsigDB database and then prognosis-related EMT genes were identified by univariate Cox regression. Lasso regression was then performed to determine the genes and the corresponding variables to construct a prognosis risk model from the training cohort. Furthermore, characteristics of the tumor microenvironment (TME), mutation status and chemotherapy responses were analyzed to assess the differences between the two risk groups based on the prognostic model. In addition, RT-qPCR was employed to validate the expression patterns of the 6 genes derived from the risk model.

Results: A six-gene EMT signature (PMEPA1, LOXL2, PLOD2, MMP14, SPOCK1 and DCN) was successfully constructed and validated. The signature assigned the LUAD patients into high-risk and low-risk groups. In comparison with the low-risk group, patients in the high-risk group had a significantly lower survival rate. ROC curves and calibration curves for the risk model demonstrated reliable stratification and predictive ability. The risk model was robustly correlated with multiple TME characteristics. Besides, the data showed that patients in the low-risk group had more immune activities, higher stemness scores and cytolytic activity scores and higher TMB. In addition, RT-qPCR results revealed that PMEPA1, LOXL2, PLOD2, MMP14, and SPOCK1 were notably upregulated in LUAD tissues, while DCN was downregulated.

Conclusion: Our study successfully developed a novel EMT-related signature to predict prognosis of LUAD patients and guide treatment strategies. The six genes derived from the prediction signature might play a potential role in antitumor immunity and serve as promising therapeutic targets in LUAD.

Identification of a apoptosis-related LncRNA signature to improve prognosis prediction and immunotherapy response in lung adenocarcinoma patients

Apoptosis is closely associated with the development of various cancers, including lung adenocarcinoma (LUAD). However, the prognostic value of apoptosis-related lncRNAs (ApoRLs) in LUAD has not been fully elucidated. In the present study, we screened 2, 960 ApoRLs by constructing a co-expression network of mRNAs-lncRNAs associated with apoptosis, and identified 421 ApoRLs that were differentially expressed between LUAD samples and normal lung samples. Sixteen differentially expressed apoptosis-related lncRNAs (DE-ApoRLs) with prognostic relevance to LUAD patients were screened using univariate Cox regression analysis. An apoptosis-related lncRNA signature (ApoRLSig ) containing 10 ApoRLs was constructed by applying the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression method, and all LUAD patients in the TCGA cohort were divided into high or low risk groups. Moreover, patients in the high-risk group had a worse prognosis (p < 0.05). When analyzed in conjunction with clinical features, we found ApoRLSig to be an independent predictor of LUAD patients and established a prognostic nomogram combining ApoRLSig and clinical features. Gene set enrichment analysis (GSEA) revealed that ApoRLSig is involved in many malignancy-associated immunomodulatory pathways. In addition, there were significant differences in the immune microenvironment and immune cells between the high-risk and low-risk groups. Further analysis revealed that the expression levels of most immune checkpoint genes (ICGs) were higher in the high-risk group, which suggested that the immunotherapy effect was better in the high-risk group than in the low-risk group. And we found that the high-risk group was also better than the low-risk group in terms of chemotherapy effect. In conclusion, we successfully constructed an ApoRLSig which could predict the prognosis of LUAD patients and provide a novel strategy for the antitumor treatment of LUAD patients.

Necroptosis-related lncRNA in lung adenocarcinoma: A comprehensive analysis based on a prognosis model and a competing endogenous RNA network

Background: Necroptosis, an innovative type of programmed cell death, involves the formation of necrosomes and eventually mediates necrosis. Multiple lines of evidence suggest that necroptosis plays a major role in the development of human cancer. However, the role of necroptosis in lung adenocarcinoma (LUAD) remains unclear. In this study, we aimed to construct an NRL-related prognostic model and comprehensively analyze the role of NRL in LUAD.

Methods: A necroptosis-related lncRNA (NRL) signature was constructed in the training cohort and verified in the validation and all cohorts based on The Cancer Genome Atlas database. In addition, a nomogram was developed. The tumor microenvironment (TME), checkpoint, human leukocyte antigen, and m6A methylation levels were compared between low-risk and high-risk groups. Then, we identified five truly prognostic lncRNAs (AC107021.2, AC027117.1, FAM30A, FAM83A-AS1, and MED4-AS1) and constructed a ceRNA network, and four hub genes of downstream genes were identified and analyzed using immune, pan-cancer, and survival analyses.

Results: The NRL signature could accurately predict the prognosis of patients with LUAD, and patients with low risk scores were identified with an obvious “hot” immune infiltration level, which was strongly associated with better prognosis. Based on the ceRNA network, we postulated that NRLs regulated the TME of patients with LUAD via cyclin-dependent kinase (CDK) family proteins.

Conclusion: We constructed an NRL signature and a ceRNA network in LUAD and found that NRLs may modulate the immune microenvironment of LUAD via CDK family proteins.

ATP6AP1 is a potential prognostic biomarker and is associated with iron metabolism in breast cancer

Cancer occurrence and progression may be facilitated by aberrant expression of ATPase H+ transporting accessory protein 1 (ATP6AP1). However, the clinical relevance of ATP6AP1 in breast cancer remains unclear. In this study, we investigated the association between ATP6AP1 and breast cancer. Data collected from patients with breast cancer from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were used in this study. To determine the relationship between ATP6AP1 and breast cancer survival rates, Kaplan-Meier analysis was used. To determine the prognostic value of ATP6AP1, a receiver operating characteristic (ROC) curve was constructed. To identify the major pathways involving ATP6AP1, we performed functional enrichment analysis using gene set enrichment analysis (GSEA). We analyzed the association between ATP6AP1 expression and tumor immunity using the ESTIMATE algorithm and single-sample GSEA (ssGSEA). A nomogram based on a Cox regression analysis was constructed to predict the impact of ATP6AP1 on prognosis. ATP6AP1 expression was significantly upregulated in breast cancer tissues. Moreover, patients with elevated ATP6AP1 expression had shorter total survival rates than those with lower expression levels (p = 0.032). The area under the receiver operating characteristic curve for ATP6AP1 was 0.939. Gene set enrichment analysis revealed that reaction iron uptake and transport, proteasome degradation, glutathione metabolism, and pyruvate metabolism were enriched in the ATP6AP1 high expression phenotype. The relationship between immune infiltration cells and ATP6AP1 expression, including macrophages, B cells, dendritic cells, cytotoxic cells, NK cells, and T cells, was found to be negative, suggesting that ATP6AP1 overexpression results in immunosuppression. Based on the Cox regression analyses, the calibration plot of the nomogram demonstrated effective performance in predicting breast cancer patients. ATP6AP1 may facilitate breast cancer progression by inhibiting antitumor immunity and promoting iron metabolism and may be a biomarker for breast cancer prognosis.

Ferroptosis-related lncRNAs signature to predict the survival and immune evasion for lung squamous cell carcinoma

Introduction: the investigation on the interactions between ferroptosis and lncRNAs for lung squamous cell carcinoma (LUSC) has been scare, and its impact on tumor immune microenvironment remained unknown. We aim to not only identify a ferroptosis-related lncRNAs signature for LUSC prognosis, but also evaluate its correlation to tumor immune evasion.

Methods: RNA sequencing data and survival information were obtained from The Cancer Genome Atlas database. A ferroptosis-related lncRNAs signature (FerRLSig) was developed and validated by univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression and multivariate Cox regression. The tumor immune microenvironment and immune evasion were subsequently evaluated based on the FerRLSig stratification.

Results: the FerRLSig consisted of 10 ferroptosis-related lncRNAs and significantly associated with overall survival with satisfactory area under curve (HR = 2.240, 95% CI: 1.845–2.720, p < 0.001, 5-years AUC: 0.756). Based on the FerRLSig stratification, the high-risk group demonstrated not only significantly higher immune infiltration, but also more profound T cell dysfunction and immune evasion, which might ultimately lead to the resistance to current immune checkpoint inhibitors.

Conclusion: a robust prognostic FerRLSig for LUSC has been developed and validated, demonstrating a close association not only with tumor immune cell infiltration, but also with T cell dysfunction and immune evasion. Further investigation is warranted to better improve the survival of LUSC patients based on the FerRLSig stratification.

Characterization and validation of a ferroptosis-related LncRNA signature as a novel prognostic model for lung adenocarcinoma in tumor microenvironment

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

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.

Identification and Validation of a Three Pyroptosis-Related lncRNA Signature for Prognosis Prediction in Lung Adenocarcinoma

Pyroptosis, defined as programmed cell death, results in the release of inflammatory mediators. Recent studies have revealed that pyroptosis plays essential roles in antitumor immunity and immunotherapy efficacy. Long noncoding RNAs (lncRNAs) are involved in a variety of biological behaviors in tumor cells, although the roles and mechanisms of lncRNAs in pyroptosis are rarely studied. Our study aimed to establish a novel pyroptosis-related lncRNA signature as a forecasting tool for predicting prognosis and ascertaining immune value. Based on lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA), we performed Pearson’s correlation analysis to identify pyroptosis-related lncRNAs. After differentially expressed gene analysis and univariate Cox regression analysis, we selected prognosis-related and differentially expressed lncRNAs. Finally, we performed multivariate Cox regression analysis to establish the three pyroptosis-related lncRNA signature. Kaplan–Meier (KM) survival analyses and receiver operating characteristic (ROC) curves indicated the excellent performance for predicting the prognosis of LUAD patients. At the same time, we applied multidimensional approaches to further explore the functional enrichment, tumor microenvironment (TME) landscape, and immunotherapy efficacy among the different risk groups. A nomogram was constructed by integrating risk scores and clinical characteristics, which was validated using calibrations and ROC curves. Three lncRNAs, namely, AC090559.1, AC034102.8, and AC026355.2, were involved in this signature and used to classify LUAD patients into low- and high-risk groups. Overall survival time (OS) was higher in the low-risk group than in the high-risk group, which was also validated in our LUAD cohort from Shandong Provincial Hospital. TME landscape analyses revealed that a higher abundance of infiltrating immune cells and a greater prevalence of immune-related events existed in the low-risk group. Meanwhile, higher expression of immune checkpoint (ICP) genes, higher immunophenoscore (IPSs), and greater T cell dysfunction in the low-risk group demonstrated a better response to immunotherapy than the high-risk group. Combined with predictions from the Tumor Immune Dysfunction and Exclusion (TIDE) website, we found that LUAD patients in the low-risk group significantly benefited from programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte–associated protein 4 (CTLA4) immune checkpoint blockade (ICB) therapy compared with those in the high-risk group. Furthermore, drug susceptibility analysis identified potential sensitive chemotherapeutic drugs for each risk group. In this study, a novel three pyroptosis-related lncRNA signature was constructed, which could accurately predict the immunotherapy efficacy and prognosis in LUAD patients.

A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma

Increased intracellular toxicity due to an imbalance in copper homeostasis caused by copper ion accumulation could regulate the rate of cancer cell growth and proliferation. The goal of this study was to create a novel Cuproptosis-related lncRNA signature that may be utilized to predict survival and immunotherapy in HCC patients. Cuproptosis-associated lncRNAs and differentially expressed lncRNAs between HCC tumor tissue and normal tissue were discovered first. By LASSO-Cox analysis, the overlapping lncRNAs were then utilized to build a Cuproptosis-associated lncRNA signature, which might be used to predict patient prognosis and responsiveness to immune checkpoint blockade (ICB) therapy. Differences in the infiltration of immune cell subpopulations between high and low-risk score subgroups were also analyzed. Moreover, a nomogram based on the Cuproptosis-associated lncRNA signature and clinical features was developed and demonstrated to have good predictive potential. Finally, qRT-PCR was performed in HerpG2 and MHCC-97H cell lines to explore whether these lncRNAs were indeed involved in the process of Cuproptosis. In summary, we created a prognostic lncRNA profile linked to Cuproptosis to forecast response to immunotherapy, which may provide a new potential non-apoptotic therapeutic perspective for HCC patients.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.

Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.

Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.

Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics

Existing studies suggest that m⁶A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m⁶A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m⁶A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m⁶A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/.

Long non-coding RNA DEPDC1-AS1 promotes proliferation and migration of human gastric cancer cells HGC-27 via the human antigen R-F11R pathway

Objective

Long non-coding (lnc) RNAs are critical regulators in carcinogenesis. The novel lncRNA DEPDC1 antisense RNA 1 (DEPDC1-AS1) was recently associated with poor prognosis in triple-negative breast cancer and lung adenocarcinoma. However, its role in regulating the malignant progression of gastric cancer (GC) and its molecular mechanism are unclear. We herein explored the functions of DEPDC1-AS1 in GC progression.

Methods

DEPDC1-AS1 expression and prognosis in GC tissues were examined by bioinformatics analysis and quantitative reverse transcription polymerase chain reaction. The DEPDC1-AS1 function in GC cells was explored by the cell counting kit-8 assay, colony formation assay, Transwell assay, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling, 5-ethynyl-2′-deoxyuridine-incorporation, and the xenograft tumor model. The DEPDC1-AS1 and human antigen (Hu)R interaction was determined by RNA pull-down and RNA immunoprecipitation.

Results

DEPDC1-AS1 was overexpressed in GC tissues and cell lines, and associated with a worse prognosis in GC patients. In vitro and in vivo assays showed that DEPDC1-AS1 promoted HGC-27 cell proliferation and migration. Mechanistically, DEPDC1-AS1 served as a scaffold by combining with HuR to target the specific mRNA F11R.

Conclusion

DEPDC1-AS1 plays a crucial role in GC development and progression and is a potential biomarker for the early detection or prognosis of GC.

A Comprehensive Prognostic and Immune Analysis of Ferroptosis-Related Genes Identifies SLC7A11 as a Novel Prognostic Biomarker in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is still one of the illnesses with the greatest mortality and morbidity. As a recently identified mode of cellular death, the activation of ferroptosis may promote the effectiveness of antitumor therapies in several types of tumors. However, the expression and clinical significance of Ferroptosis-associated genes in LUAD are still elusive. The RNA sequencing data of LUAD and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) datasets. Subsequently, potential prognostic biomarkers were determined by the use of biological information technology. The R software package “ggalluvial” was applied to structure Sanguini diagram. Herein, our team screened 14 dysregulated ferroptosis-associated genes in LUAD. Among them, only four genes were associated with clinical outcome of LUAD patients, including ATP5MC3, FANCD2, GLS2, and SLC7A11. In addition, we found that high SLC7A11 expression predicted an advanced clinical progression in LUAD patients. Additionally, 8 immune checkpoint genes and 7 immune cells for LUAD were recognized to be related to the expression of SLC7A11. KEGG assays indicated that high expression of SLC7A11 might participate in the modulation of intestinal immune network for IgA generation and Staphylococcus aureus infection. Overall, our findings revealed that SLC7A11 might become a potentially diagnostic biomarker and SLC7A11 might serve as an independent prognosis indicator for LUAD.

Construction and Validation of a Novel Pyroptosis-Related Four-lncRNA Prognostic Signature Related to Gastric Cancer and Immune Infiltration

Increasing evidence has demonstrated that pyroptosis, a type of inflammatory programmed cell death, plays an important role in the pathogenesis and progression of gastric cancer. However, it remains unclear whether pyroptosis-related long non-coding RNAs (lncRNAs) can be used to predict the diagnosis and prognosis of gastric adenocarcinoma. This study aimed to evaluate and test the role of the lncRNA signature associated with pyroptosis as a prognostic tool for stomach adenocarcinoma (STAD) and to ascertain their immune value. Relative RNA-sequencing data were extracted from The Cancer Genome Atlas database (TCGA), and data preprocessing was performed for STAD. Pearson correlation analysis was used to determine whether lncRNAs were significantly correlated with pyroptosis based on 23 genes related to pyroptosis. Univariate Cox regression and least absolute shrinkage and selection operator(LASSO) analyses were both adopted to select features and establish the pyroptosis-related lncRNA (PRL) prognostic signature. Kaplan–Meier(KM) survival analysis of the different risk groups was conducted according to the risk scores. We further examined the functional enrichment, tumor microenvironment, and landscape of mutation status among the different risk groups, and these analyses further explained the reasons for the differences in the prediction as well as survival value of the different risk groups. Four lncRNAs, including HAND2-AS1, LINC01354, RP11-276H19.1, and PGM5-AS1, were involved in the PRL signature and used to split STAD patients into two risk groups. Overall survival time(OS) was significantly higher in the low-risk group than in the high-risk group in both the training and validation groups. Functional enrichment analysis was further employed to analyze differentially expressed genes in high- and low-risk groups to identify potential molecular functions and pathways associated with pyroptosis in the gastric cancer microenvironment. Protein-protein interaction (PPI) and Friends analysis identified hub genes that may play a key role in differentially expressed genes in high- and low-risk groups. In addition, there were remarkable discrepancies between the different risk groups in the tumor stage (P < 0.01) and histologic grade (P < 0.05). Furthermore, drug-susceptibility testing indicated potential sensitive chemotherapeutic drugs for each risk group. This study is the first to establish and validate STAD-associated PRLs that can effectively guide the prognosis and the immune microenvironment in STAD patients and provide evidence for the development of molecularly targeted therapies related to pyroptosis.

Development and Validation of a Ferroptosis-Related lncRNAs Prognosis Model in Oral Squamous Cell Carcinoma

Objectives: Ferroptosis is an iron-dependent form of programmed cell death, which affects the prognosis of many cancers. Some long non-coding RNA (lncRNA) can affect the prognosis of cancer by regulating the process of ferroptosis. However, the role of ferroptosis-related lncRNA (frlncRNA) in oral squamous cell carcinoma (OSCC) is not yet clear.

Materials and Methods: The data of OSCC patients were downed from The Cancer Genome Atlas (TCGA). After univariate and multivariate Cox regression analysis, the prognosis-related ferroptosis-related lncRNAs were obtained to construct a prognostic model. Calculated the risk score to divide patients into high and low risk groups, and evaluated the predictive ability of the model and the differential expression of immunity in the high and low risk groups.

Results: The prognostic model for OSCC was constructed based on 8 prognostic-related frlncRNAs which co-expressed with 25 mRNAs. Kaplan-Meier analyses displayed that the risk score is inversely proportional to patient survival. Receiver operating characteristic (ROC) and decision curve analysis (DCA) indicated that the risk score is superior to other clinical characteristics, and independent prognostic analysis demonstated that risk score is independent factor for the overall survival (OS) rate. The results of immunological analysis showed differences in immune cells, functions, immune checkpoints, and m6A expression between high and low risk groups.

Conclusion: We constructed an OSCC patients prognosis model based on 8 frlncRNAs, which can provide prognostic evaluation and immune analysis for OSCC patients, and provided new direction for OSCC targeted therapy.

Construction of a Co-Expression Network for lncRNAs and mRNAs Related to Urothelial Carcinoma of the Bladder Progression

Carcinoma of urinary bladder is the most familiar cancer of the urinary tract, with the highest incidence in men. However, its prognosis and treatment have not improved significantly in the last 30 years. The main reason for this may be related to the alteration and regulation of genes. These alterations in genes that play a crucial role in cell cycle regulation may result in high-grade tumors and may alter drug sensitivity. Notably, the role of lncRNA in bladder cancer, especially the lncRNA-mRNA regulatory network, has not been fully elucidated. In this manuscript, we compared RNA sequencing (RNA-seq) data from 19 normal bladder tissues and 411 primary bladder tumor tissues using The Cancer Genome Atlas (TCGA) data bank, subjected differentially expressed mRNAs and lncRNAs to weighted gene co-expression network analysis, and screened out modules highly correlated with tumor progression. Subsequently, a lncRNA-mRNA co-expression network was built, and two key mRNAs were identified via COX regression analysis. Kaplan-Meier curve analysis revealed that the overall survival of sick people in the high-risk section was significantly shorter than those in the low-risk section. Therefore, this lncRNA-mRNA-based co-expression pattern may be used clinically to predict the prognosis of carcinoma of urinary bladder people. Our study not only provides a genetic target for carcinoma of urinary bladder therapy but also provides new ideas for people in the medical profession to discover the treatment of various tumors.

Identification of Differentially Expressed and Prognostic lncRNAs for the Construction of ceRNA Networks in Lung Adenocarcinoma

Background

Long noncoding RNAs (lncRNAs) could function as competitive endogenous RNAs (ceRNAs) to competitively adsorb microRNAs (miRNAs), thereby regulating the expression of their target protein-coding mRNAs. In this study, we aim to identify more effective diagnostic and prognostic markers for lung adenocarcinoma (LUAD).

Methods

We obtained differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) for LUAD by using The Cancer Genomes Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) was performed to unveil core gene modules associated with LUAD. The Cox proportional hazards model was performed to determine the prognostic significance of DElncRNAs. The diagnostic and prognostic significance of DElncRNAs was further verified based on the receiver operating characteristic curve (ROC). Cytoscape was used to construct the ceRNA networks comprising the lncRNAs-miRNAs-mRNAs axis based on the correlation obtained from the miRcode, miRDB, and TargetScan.

Results

Compared with normal lung tissues, 2355 DElncRNAs, 820 DEmiRNAs, and 17289 DEmRNAs were identified in LUAD tissues. We generated 8 WGCNA core modules in the lncRNAs coexpression network, 5 modules in the miRNAs, and 12 modules in the mRNAs coexpression network, respectively. One lncRNA module (blue) consisting of 441 lncRNAs, two miRNA modules (blue and turquoise) containing 563 miRNAs, and one mRNA module (turquoise), which consisted of 15162 mRNAs, were mostly significantly related to LUAD status. Furthermore, 67 DEmRNAs were found to be tumor-associated as well as the target genes of the DElncRNAs-DEmiRNAs axis. Survival analyses showed that 6 lncRNAs (LINC01447, WWC2-AS2, OGFRP1, LINC00942, LINC01168, and AC005863.1) were significantly correlated with the prognosis of LUAD patients. Ultimately, the potential ceRNA networks including 6 DElncRNAs, 4 DEmiRNAs, and 22 DEmRNAs were constructed.

Conclusion

Our study indicated that 6 DElncRNAs had the possibilities as diagnostic and prognostic biomarkers for LUAD. The lncRNA-mediated ceRNA networks might provide novel insights into the molecular mechanisms of LUAD progression.

Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcinomas

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor, which makes the prognosis prediction challenging. Ferroptosis is an iron-dependent form of non-apoptotic regulated cell death, which could affect cancer development. However, the prognostic value of ferroptosis-related long non-coding RNA (lncRNA) in HNSCC is still limited.

Methods: In the current study, we employed the DESeq2 method to characterize the differentially expressed ferroptosis-related genes (FEGs) between cancer and normal samples. Next, the FEG-related lncRNAs (FElncRNAs) were identified using Spearman’s correlation analysis and multiple permutation hypotheses. Subsequently, LASSO and stepwise multivariate Cox regression analyses were undertaken to recognize the prognosis-related FElncRNA signature (PFLS) and risk scores.

Results: Herein, we first identified 60 dysregulated FEGs and their co-expressed FElncRNAs in HNSCC. Then, we recognized a set of six FElncRNAs PFLS (SLCO4A1-AS1, C1RL-AS1, PCED1B-AS1, HOXB-AS3, MIR9-3HG, and SFTA1P) for predicting patients’ prognostic risks and survival outcomes. We also assessed the efficiency of PFLS in the test set and an external validation cohort. Further parsing of the tumor immune microenvironment showed the PFLS was closely associated with immune cell infiltration abundances. Notably, the low-risk group of the PFLS showed a higher MHC score and cytolytic activity (CYT) score than the high-risk group, implying the low-risk group may have greater tumor surveillance and killing ability. In addition, we observed that the expression levels of two immune checkpoints (ICPs), i.e., programmed cell death protein 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1), showed significant associations with patients’ risk score, prompting the role of the PFLS in ICP blockade therapy. Finally, we also constructed a drug–PFLS network to reinforce the clinical utilities of the PFLS.

Conclusion: In summary, our study indicated that FElncRNAs played an important role in HNSCC survival prediction. Identification of PFLS will contribute to the development of novel anticancer therapeutic strategies.

Ferroptosis in Lung Cancer: From Molecular Mechanisms to Prognostic and Therapeutic Opportunities

Lung cancer is the second commonly diagnosed malignancy worldwide and has the highest mortality rate among all cancers. Tremendous efforts have been made to develop novel strategies against lung cancer; however, the overall survival of patients still is low. Uncovering underlying molecular mechanisms of this disease can open up new horizons for its treatment. Ferroptosis is a newly discovered type of programmed cell death that, in an iron-dependent manner, peroxidizes unsaturated phospholipids and results in the accumulation of radical oxygen species. Subsequent oxidative damage caused by ferroptosis contributes to cell death in tumor cells. Therefore, understanding its molecular mechanisms in lung cancer appears as a promising strategy to induce ferroptosis selectively. According to evidence published up to now, significant numbers of research have been done to identify ferroptosis regulators in lung cancer. Therefore, this review aims to provide a comprehensive standpoint of molecular mechanisms of ferroptosis in lung cancer and address these molecules’ prognostic and therapeutic values, hoping that the road for future studies in this field will be paved more efficiently.

A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma

Purpose

Long noncoding RNAs (lncRNAs) and glycolysis regulate multiple types of cancer. However, the prognostic roles and biological functions of glycolysis-related lncRNAs in lung adenocarcinoma (LUAD) remain unclear. In this study, we investigated the role of glycolysis-related lncRNAs in LUAD.

Patients and Methods

We retrieved glycolysis-related genes from the Molecular Signatures Database and screened for prognostic glycolysis-related lncRNAs from The Cancer Genome Atlas.

Results

We identified three LUAD subtypes (clusters 1–3) by univariate Cox regression analysis and consensus clustering. Patients in cluster 1 had the best overall survival rates. Immune, stromal, and cytolytic-activity scores were the highest in cluster 1. The expression of immune checkpoint molecules (programmed cell death protein 1 and cytotoxic T-lymphocyte-associated protein 4) and other immune-related indicators was the highest in cluster 1, whereas that of epithelial cell biomarkers (Cadherin 1, Cadherin 2, and MET) was the lowest. Therefore, patients in cluster 1 may benefit from immunotherapy. Lasso–Cox regression and multivariate Cox regression analyses were used to select nine lncRNAs to build a robust prognostic model of LUAD. The area under the curve classifier values and a nomogram performed well in predicting survival times for patients with LUAD. The expression levels of nine lncRNAs were validated by quantitative reverse transcriptase-polymerase chain reaction analysis, and most of these lncRNAs were significantly related to immune-related mRNAs. Gene set enrichment analysis revealed that the high-risk group was enriched for cell cycle-related pathways and the low-risk group was enriched for pathways associated with immunity or immune-related diseases.

Conclusion

The LUAD subtypes and prognostic model developed here may help in clinical risk stratification, prognosis management, and treatment decisions for patients with LUAD.

The Ferroptosis-Related Noncoding RNA Signature as a Novel Prognostic Biomarker in the Tumor Microenvironment, Immunotherapy, and Drug Screening of Gastric Adenocarcinoma

Background

Ferroptosis is a new type of cell death different from apoptosis, necrosis, autophagy, and pyroptosis. This study aimed to explore the relationship between ferroptosis-related noncoding RNA (ncRNA) and gastric adenocarcinoma with regard to immunity and prognosis.

Methods

Ferroptosis-related ncRNA expression profiles and clinical pathology and overall survival information were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. The ferroptosis-related ncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The survival analysis, receiver operating characteristic (ROC) analysis, and decision curve analysis were adopted to evaluate the prognostic prediction performance of the signature. The correlation between risk and multiple clinical characteristics was analyzed using the chi-square test. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for mining functions and pathways. The CIBERSORT, ssGSEA, and ESTIMATE algorithms were used to assess immune infiltration and the tumor microenvironment. The response of immunotherapy was predicted using the Submap algorithm, and the Connectivity Map and the ridge regression model were used to screen and evaluate drugs.

Results

A carcinogenic risk signature was constructed using five ferroptosis-related ncRNAs. It showed an extraordinary ability to predict the prognoses of patients with gastric adenocarcinoma [area under the ROC curve (AUC) after 6 years = 0.689; GSE84426, AUC after 6 years = 0.747]. The lower ferroptosis potential level and lower tumor mutation burden were related to the poor prognoses of patients. The high-risk group had more immune cell recruitment, and the overall effect of the anti-immune checkpoint immunotherapy was not as good as that of the low-risk group. The high- and low-risk groups were enriched in tumor- and immune-related pathways, respectively. The screened antitumor drugs, such as genistein, guanabenz, and betulinic acid, improved the survival of the patients.

Conclusions

The ferroptosis-related ncRNA signature is a potential carcinogenic prognostic biomarker of gastric adenocarcinoma.

Relationships of N6-Methyladenosine-Related Long Non-Coding RNAs With Tumor Immune Microenvironment and Clinical Prognosis in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and is associated with very high mortality. Emerging studies have shown that N6-methyladenosine (m6A)-related long non-coding (lnc) RNAs play crucial roles in tumor prognosis and the tumor immune microenvironment (TME). We aimed to explore the expression patterns of different m6A-related lncRNAs concerning patient prognosis and construct an m6A-related lncRNA prognostic model for LUAD. Methods: The prognostic value of m6A-related lncRNAs was investigated in LUAD samples from The Cancer Genome Atlas (TCGA). Potential prognostic m6A-related lncRNAs were selected by Pearson's correlation and univariate Cox regression analysis. Patients were divided into clusters using principal component analysis and the m6A-related lncRNA prognostic signature was calculated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results: Based on 91 prognostic m6A-related lncRNAs, we identified two m6A-related-lncRNA pattern clusters with different overall survival (OS) and different TMEs. We subsequently verified our findings multidimensionally by constructing a 13 m6A-related lncRNA prognostic signature (m6A-LPS) to calculate the risk score, which was robust in different subgroups. The receiver operating characteristic (ROC) curves and concordance index demonstrated that m6A-LPS harbored a promising ability to predict OS in TCGA data set and independent GSE11969 cohort. The risk score was also related to OS, TME, and clinical stage, and the risk score calculated by our model was also identified as independent prognostic predictive factors for LUAD patients after adjustment for age, smoking, gender, and stage. Enrichment analysis indicated that malignancy and drug resistance-associated pathways were more common in cluster2 (LUAD-unfavorable m6A-LPS). Furthermore, the results indicated that the signaling pathway enriched by the target gene of 13 m6A-related lncRNAs may be associated with metastasis and progression of cancer according to current studies. Conclusion: The current results indicated that different m6A-related-lncRNA patterns could affect OS and TME in patients with LUAD, and the prognostic signature based on 13 m6A-related lncRNAs may help to predict the prognosis in LUAD patients.