Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

Characterization of an endoplasmic reticulum stress-associated lncRNA prognostic signature and the tumor-suppressive role of RP11-295G20.2 knockdown in lung adenocarcinoma

Endoplasmic reticulum stress (ERS) is commonly induced by accumulating misfolded or unfolded proteins in tumor microenvironment. Long non-coding RNAs (lncRNAs) play important roles in ERS response and lung adenocarcinoma (LUAD) progression. However, the role of ERS-related lncRNAs in LUAD remains unknown. In this study, we aimed to identify ERS-associated lncRNAs with prognostic value in LUAD and characterize their clinical implications. Cox and least absolute shrinkage and selection operator regression analyses identified nine ERS-related lncRNAs with independent prognostic abilities, including five protective factors (CROCCP2, KIAA0125, LINC0996, RPARP-AS1 and TBX5-AS1) and four risk factors (LINC0857, LINC116, RP11-21L23.2 and RP11-295G20.2). We developed an ERS-related lncRNA risk prediction model in predicting overall survival of LUAD patients, which classified TCGA cohorts into high-risk (HS) and low-risk (LS) groups. Comprehensive bioinformatic analyses revealed HS patients featured with late-stage tumors, greater mutation burdens, weaker anti-tumor immunity/responses, and lower sensitivity to targeted drugs compared to LS patients, contributing to tumor progression and a poor prognosis. Functional enrichment analysis implicated these ERS-related lncRNAs in cell migration, cell death, and immunity. Furthermore, expression of the most significantly upregulated risk lncRNA, RP11-295G20.2, was validated at the mRNA level using clinical LUAD samples. Knockdown of RP11-295G20.2 obviously reduced ERS and suppressed proliferation, invasion, and migration of LUAD cells. This novel ERS-related lncRNA signature provides a new biomarker for prognostic prediction, and ERS-associated RP11-295G20.2 serves as a potential therapeutic target in LUAD.

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Association of the Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) score with immune checkpoint inhibitor efficacy in patients with gastrointestinal and lung cancer

OBJECTIVE

This study aimed to conduct a comprehensive analysis, evaluating the prognostic significance of the baseline Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) Score in patients undergoing immune checkpoint inhibitor (ICI) therapy.

METHODS

A comprehensive search was performed across various databases, including PubMed, the Cochrane Library, EMBASE, and Google Scholar, until October 21, 2023, to compile relevant articles for analysis. The investigation encompassed diverse clinical outcomes, including overall survival (OS) and progression-free survival (PFS).

RESULTS

This analysis included a total of 15 articles, comprising 19 studies involving 3335 patients. Among the 19 studies, nine studies focused on NSCLC, and six studies were conducted on HCC. Pooled results revealed that patients with elevated ALI levels experienced prolonged OS (HR: 0.51, 95% CI: 0.37-0.70, p < 0.001) and extended PFS (HR: 0.61, 95% CI: 0.52-0.72, p < 0.001). Furthermore, a GRIm score > 1 was associated with reduced OS (HR: 2.07, 95% CI: 1.47-2.92, p < 0.001) and diminished PFS (HR: 1.78, 95% CI: 1.35-2.34, p < 0.001) in cancer patients receiving ICIs. Subgroup analysis indicated that ALI cutoff values of 18 exhibited enhanced predictive potential. Additionally, for HCC patients, those with HCC-GRIm score > 2 showed a substantially decreased risk of mortality compared to individuals with HCC-GRIm score ≤ 2 (HR: 2.63, 95% CI: 1.89-3.65, p < 0.001).

CONCLUSION

The ALI and GRIm score served as dependable prognostic indicators for patients undergoing ICI therapy in the context of cancer treatment.

Comprehensive analysis of m6A modification patterns and m6A-related lncRNAs as potential biomarkers in lung adenocarcinoma

BACKGROUND

N6-methyladenosine (m6A) methylation is considered to induce tumor cell proliferation, migration, and apoptosis. Understanding the mechanism of m6A-related lncRNAs in the development of lung adenocarcinoma (LUAD) may help predict prognosis.

METHODS

m6A-related lncRNAs related to lung cancer were identified and combined with the MeRIP-Seq dataset. The consensus clustering method was utilized to divide LUAD patients, and prognostic model was constructed using the Lasso Cox algorithm. The cluster profiler package was used for gene ontology and KEGG enrichment. The proportion of immune infiltration was estimated using the CIBERSORT algorithm. The decision tree was constructed by the rpart package, and nomograms were built by the rms package. The Connectivity Map database was analyzed for the therapeutic effects of small molecule drugs for LUAD. In addition, qPCR, colony formation and transwell assays were performed to validate functions of m6A-associated lncRNAs.

RESULTS

Nineteen m6A-modified lncRNAs in LUAD were identified. LUAD patients were divided into two categories based on the expression of 19 m6A-related lncRNAs. Cluster 2 patients had better antigen production and expression, while naive B cells, plasma cells, and activated NK cells were lower in cluster 1. Nine m6A-related lncRNAs were selected to establish a risk model for evaluating the prognosis of LUAD patients. The high-risk group had higher tumor mutational burden and lower TIDE scores with more gamma delta T cells and neutrophils. Nomograms showed that the prognostic model had predominant predictive ability for LUAD patients based on the risk score analyzed by the decision tree model. Benzo(a)pyrene and neurodazine might improve the prognosis of LUAD patients. The qRT-PCR results confirmed the reliability of the analytical results.

CONCLUSION

The establishment of a prognostic model of m6A-related lncRNAs can independently predict overall survival in LUAD and may help to develop personalized immunotherapy strategies.

CYP1B1-AS1 Delays the Malignant Progression of Colorectal Cancer by Binding with NOP58

BACKGROUND

Colorectal cancer (CRC) is a prevalent type of gastrointestinal cancer, and its poor prognosis is mainly attributed to the occurrence of invasion and metastasis. CYP1B1-AS1, as non-coding RNA, plays an important role in tumorigenesis and progression. However, the mechanism by which CYP1B1-AS1 acts in CRC is not yet understood.

AIMS

The objective of this study was to investigate how CYP1B1-AS1 contributes to the development of CRC, and provide a base for CRC diagnosis and treatment.

METHODS

RT-qPCR was used to detect the expression level of CYP1B1-AS1 in CRC and adjacent tissues. CCK-8, Edu, scratch healing, and transwell experiments were used to detect the changes of proliferation, migration, and invasion ability of CRC cells after overexpression or knockdown of CYP1B1-AS1 respectively. The RNA binding protein NOP58 combined with CYP1B1-AS1 was verified by RIP and RNA Pull-down experiments. Functional recovery experiments validated the interaction between CYP1B1-AS1 and NOP58 in CRC cells. The changes of EMT-related proteins were detected by Western blot, and the half-life of transcription factor SNAIL mRNA were detected by RT-qPCR after overexpression or knockdown of NOP58.

RESULTS

CYP1B1-AS1 was found to be significantly downregulated in CRC compared to adjacent noncancerous tissues. Experiments conducted in vitro and in vivo confirmed that upregulation of CYP1B1-AS1 significantly inhibited the proliferation, migration, and invasion of CRC cells. In addition, CYP1B1-AS1 can directly bind to NOP58 and negatively regulate NOP58. The effect of overexpression CYP1B1-AS1 was reversed by NOP58 overexpression. NOP58 regulates the EMT process of CRC cells by affecting the stability of EMT-related transcription factor SNAIL mRNA, and then affects the progress of CRC.

CONCLUSION

This research proves that CYP1B1-AS1 can inhibit the occurrence of EMT in CRC by binding with NOP58, thus delaying the progress of CRC. This finding indicates that CYP1B1-AS1 may be a novel biomarker to improve the diagnosis and treatment of CRC.

A novel sphingolipid metabolism-related long noncoding RNA signature predicts the prognosis, immune landscape and therapeutic response in pancreatic adenocarcinoma

Sphingolipid metabolism affects prognosis and resistance to immunotherapy in patients with cancer and is an emerging target in cancer therapy with promising diagnostic and prognostic value. Long noncoding ribonucleic acids (lncRNAs) broadly regulate tumour-associated metabolic reprogramming. However, the potential of sphingolipid metabolism-related lncRNAs in pancreatic adenocarcinoma (PAAD) is poorly understood. In this study, coexpression algorithms were employed to identify sphingolipid metabolism-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a sphingolipid metabolism-related lncRNA signature (SMLs). The prognostic predictive stability of the SMLs was validated using Kaplan-Meier. Univariate and multivariate Cox, receiver operating characteristic (ROC) and clinical stratification analyses were used to comprehensively assess the SMLs. Gene set variation analysis (GSVE), gene ontology (GO) and tumor mutation burden (TMB) analysis explored the potential mechanisms. Additionally, single sample gene set enrichment analysis (ssGSEA), ESTIMATE, immune checkpoints and drug sensitivity analysis were used to investigate the potential predictive function of the SMLs. Finally, an SMLs-based consensus clustering algorithm was utilized to differentiate patients and determine the suitable population for immunotherapy. The results showed that the SMLs consists of seven sphingolipid metabolism-related lncRNAs, which can well determine the clinical outcome of individuals with PAAD, with high stability and general applicability. In addition, the SMLs-based consensus clustering algorithm divided the TCGA-PAAD cohort into two clusters, with Cluster 1 showing better survival than Cluster 2. Additionally, Cluster 1 had a higher level of immune cell infiltration than Cluster 2, which combined with the higher levels of immune checkpoints in Cluster 1 suggests that Cluster 1 is more consistent with an immune 'hot tumor' profile and may respond better to immune checkpoint inhibitors (ICIs). This study offers new insights regarding the potential role of sphingolipid metabolism-related lncRNAs as biomarkers in PAAD. The constructed SMLs and the SMLs-based clustering are valuable tools for predicting clinical outcomes in PAAD and provide a basis for clinical selection of individualized treatments.

[Study on the Role and Mechanism of METTL3 Mediating the Up-regulation of m6A Modified Long Non-coding RNA THAP7-AS1 in Promoting the Occurrence of Lung Cancer]

BACKGROUND

Lung cancer is a major threat to human health. The molecular mechanisms related to the occurrence and development of lung cancer are complex and poorly known. Exploring molecular markers related to the development of lung cancer is helpful to improve the effect of early diagnosis and treatment. Long non-coding RNA (lncRNA) THAP7-AS1 is known to be highly expressed in gastric cancer, but has been less studied in other cancers. The aim of the study is to explore the role and mechanism of methyltransferase-like 3 (METTL3) mediated up-regulation of N6-methyladenosine (m6A) modified lncRNA THAP7-AS1 expression in promoting the development of lung cancer.

METHODS

Samples of 120 lung cancer and corresponding paracancerous tissues were collected. LncRNA microarrays were used to analyze differentially expressed lncRNAs. THAP7-AS1 levels were detected in lung cancer, adjacent normal tissues and lung cancer cell lines by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The diagnostic value of THAP7-AS1 in lung cancer and the relationship between THAP7-AS1 expression and survival rate and clinicopathological parameters were analyzed. Bioinformatics analysis, methylated RNA immunoprecipitation (meRIP), RNA pull-down and RNA-immunoprecipitation (RIP) assay were used to investigate the molecular regulation mechanism of THAP7-AS1. Cell proliferation, migration, invasion and tumorigenesis of SPC-A-1 and NCI-H1299 cells were determined by MTS, colony-formation, scratch, Transwell and xenotransplantation in vivo, respectively. Expression levels of phosphoinositide 3-kinase/protein kenase B (PI3K/AKT) signal pathway related protein were detected by Western blot.

RESULTS

Expression levels of THAP7-AS1 were higher in lung cancer tissues and cell lines (P<0.05). THAP7-AS1 has certain diagnostic value in lung cancer [area under the curve (AUC)=0.737], and its expression associated with overall survival rate, tumor size, tumor-node-metastasis (TNM) stage and lymph node metastasis (P<0.05). METTL3-mediated m6A modification enhanced THAP7-AS1 expression. The cell proliferation, migration, invasion and the volume and mass of transplanted tumor were all higher in the THAP7-AS1 group compared with the NC group and sh-NC group of SPC-A-1 and NCI-H1299 cells, while the cell proliferation, migration and invasion were lower in the sh-THAP7-AS1 group (P<0.05). THAP7-AS1 binds specifically to Cullin 4B (CUL4B). The cell proliferation, migration, invasion, and expression levels of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), phosphoinositide-3 kinase, catalytic subunit delta (PIK3CD), phospho-phosphatidylinositol 3-kinase (p-PI3K), phospho-protein kinase B (p-AKT) and phospho-mammalian target of rapamycin (p-mTOR) were higher in the THAP7-AS1 group compared with the Vector group of SPC-A-1 and NCI-H1299 cells (P<0.05).

CONCLUSIONS

LncRNA THAP7-AS1 is stably expressed through m6A modification mediated by METTL3, and combines with CUL4B to activate PI3K/AKT signal pathway, which promotes the occurrence and development of lung cancer.

Sarcopenia affects the clinical efficacy of immune checkpoint inhibitors in patients with gastrointestinal cancers

OBJECTIVE

The impact of sarcopenia on the efficacy of immune checkpoint inhibitors (ICI) in gastrointestinal cancer (GIC) patients remains uncertain in clinical practice. Hence, this study aims to investigate the potential correlation between sarcopenia and the clinical outcomes of GIC patients treated with ICIs.

METHODS

To gather pertinent studies, a systematic literature search was implemented across multiple databases, including PubMed, Embase, the Cochrane Library, and Google Scholar. The primary outcomes of interest were overall survival (OS) and progression-free survival (PFS), measured with the hazard ratio (HR). And the secondary outcomes, including disease control rate (DCR), overall response rate (ORR), and adverse events (AE), were evaluated with the odd ratio (OR).

RESULTS

A total of 13 articles involving 1294 patients were collected for this analysis. The pooled results revealed that GIC patients with sarcopenia had significantly poorer OS (HR = 1.697, 95% CI = 1.367-2.106, p < 0.001) and PFS (HR: 1.551, 95% CI: 1.312-1.833, p < 0.001), and lower ORR (OR = 0.594, 95% CI = 0.388-0.909, p = 0.016) and DCR (OR: 0.553, 95% CI: 0.360-0.850, p = 0.007) compared to those without sarcopenia. However, sarcopenia did not increase the incidence of treatment-related adverse events compared with non-sarcopenia (OR = 1.377, 95% CI = 0.693-2.737, p = 0.361). According to subgroup analysis, the association between sarcopenia and the therapeutic effect of ICI on patients with primary liver cancer or gastric cancer was consistent with the above findings.

CONCLUSION

Sarcopenia is significantly correlated with poorer treatment response and worse long-term efficacy in GIC patients treated with ICIs. Moreover, sarcopenia does not increase the incidence of adverse events.

Construction of RNA Methylation Modification-immune-related lncRNA Molecular Subtypes and Prognostic Scoring System in Lung Adenocarcinoma

BACKGROUND

RNA methylation modification is not only intimately interrelated with cancer development and progression but also actively influences immune cell infiltration in the tumor microenvironment (TME). RNA methylation modification genes influence the therapeutic progression of lung adenocarcinoma (LUAD), and mining RNA methylation modification prognosis-related markers in LUAD is crucial for its precise prognosis.

METHODS

RNA-Seq data and Gene sets were collected from online databases or published literature. Genomic variation analysis was conducted by the Maftools package. RNA methylation-immune-related lncRNAs were obtained by Pearson correlation analysis. Then, Consistent clustering analysis was performed to obtain RNA methylation modification- immune molecular subtypes (RMM-I Molecular subtypes) in LUAD based on selected lncRNAs. COX and random survival forest analysis were carried out to construct the RMM-I Score. The receiver operating characteristic (ROC) curve and Kaplan Meier survival analysis were used to assess survival differences. Tumor immune microenvironment was assessed through related gene signatures and CIBERSORT algorithm. In addition, drug sensitivity analysis was executed by the pRRophetic package.

RESULTS

Four RNA methylation modified-immune molecular subtypes (RMM-I1, RMM- I2, RMM-I3, RMM-I4) were presented in LUAD. Patients in RMM-I4 exhibited excellent survival advantages and immune activity. HAVCR2, CD274, and CTLA-4 expression were activated in RMM-I4, which might be heat tumors and a potential beneficial group for immunotherapy. OGFRP1, LINC01116, DLGAP1-AS2, CRNDE, LINC01137, MIR210HG, and CYP1B1-AS1 comprised the RMM-I Score. The RMM-I Score exhibited excellent accuracy in the prognostic assessment of LUAD, as patients with a low RMM- I Score exhibited remarkable survival advantage. Patients with a low RMM-I score might be more sensitive to treatment with Docetaxel, Vinorelbine, Paclitaxel, Cisplatin, and immunotherapy.

CONCLUSION

The RMM-I molecular subtype constituted the novel molecular characteristic subtype of LUAD, which complemented the existing pathological typing. More refined and accurate molecular subtypes provide help to reveal the mechanism of LUAD development. In addition, the RMM-I score offers a reliable tool for accurate prognosis of LUAD.

Comprehensive landscape and future perspective of long noncoding RNAs in non-small cell lung cancer: it takes a village

Long noncoding RNAs (lncRNAs) are a distinct subtype of RNA that lack protein-coding capacity but exert significant influence on various cellular processes. In non-small cell lung cancer (NSCLC), dysregulated lncRNAs act as either oncogenes or tumor suppressors, contributing to tumorigenesis and tumor progression. LncRNAs directly modulate gene expression, act as competitive endogenous RNAs by interacting with microRNAs or proteins, and associate with RNA binding proteins. Moreover, lncRNAs can reshape the tumor immune microenvironment and influence cellular metabolism, cancer cell stemness, and angiogenesis by engaging various signaling pathways. Notably, lncRNAs have shown great potential as diagnostic or prognostic biomarkers in liquid biopsies and therapeutic strategies for NSCLC. This comprehensive review elucidates the significant roles and diverse mechanisms of lncRNAs in NSCLC. Furthermore, we provide insights into the clinical relevance, current research progress, limitations, innovative research approaches, and future perspectives for targeting lncRNAs in NSCLC. By summarizing the existing knowledge and advancements, we aim to enhance the understanding of the pivotal roles played by lncRNAs in NSCLC and stimulate further research in this field. Ultimately, unraveling the complex network of lncRNA-mediated regulatory mechanisms in NSCLC could potentially lead to the development of novel diagnostic tools and therapeutic strategies.

Hierarchical clustering identifies oxidative stress-related subgroups for the prediction of prognosis and immune microenvironment in gastric cancer

Background

Gastric cancer (GC) is a prevalent malignancy of the digestive tract globally, demonstrating a substantial occurrence of relapse and metastasis, alongside the absence of efficacious treatment. Tumor progression and the development of cancer are linked to oxidative stress. Our objective was twofold: first, to determine distinct subcategories based on oxidative stress in GC patients, and second, to establish oxidative stress-related genes that would aid in stratifying the risk for GC patients.

Methods

TCGA-STAD and GSE84437 datasets were utilized to obtain the mRNA expression profiles and corresponding clinical information of GC patients. Through consensus clustering analysis, distinct subgroups related to oxidative stress were identified. To uncover the underlying mechanisms, GSEA and GSVA were performed. xCell, CIBERSORT, MCPCounter, and TIMER algorithms were employed to evaluate the immune microenvironment and immune status of the different GC subtypes. A prognostic risk model was developed using the TCGA-STAD dataset and substantiated using the GSE84437 dataset. Furthermore, qRT-PCR was employed to validate the expression of genes associated with prognosis.

Results

Two distinct subtypes of oxidative stress were discovered, with markedly different survival rates. The C1 subtype demonstrated an activated immune signal pathway, a significant presence of immune cell infiltration, high immune score, and a high microenvironment score, indicating a poor prognosis. Moreover, a prognostic signature related to oxidative stress (IMPACT and PXDN) was able to accurately estimate the likelihood of survival for patients with gastric cancer. A nomogram incorporating the patients' gender, age, and risk score was able to predict survival in gastric cancer patients. Additionally, the expression of IMPACT and PXDN showed a strong correlation with overall survival and the infiltration of immune cells.

Conclusion

Based on signatures related to oxidative stress, we developed an innovative system for categorizing patients with GC. This stratification enables accurate prognostication of individuals with GC.

Exploring the regulatory role of lncRNA in cancer immunity

Imbalanced immune homeostasis in cancer microenvironment is a hallmark of cancer. Increasing evidence demonstrated that long non-coding RNAs (lncRNAs) have emerged as key regulatory molecules in directly blocking the cancer immunity cycle, apart from activating negative regulatory pathways for restraining tumor immunity. lncRNAs reshape the tumor microenvironment via the recruitment and activation of innate and adaptive lymphoid cells. In this review, we summarized the versatile mechanisms of lncRNAs implicated in cancer immunity cycle, including the inhibition of antitumor T cell activation, blockade of effector T cell recruitment, disruption of T cell homing, recruitment of immunosuppressive cells, and inducing an imbalance between antitumor effector cells (cytotoxic T lymphocytes, M1 macrophages, and T helper type 1 cells) versus immunosuppressive cells (M2 macrophages, T helper type 2 cells, myeloid derived suppressor cells, and regulatory T cells) that infiltrate in the tumor. As such, we would highlight the potential of lncRNAs as novel targets for immunotherapy.

Development and validation of two redox-related genes associated with prognosis and immune microenvironment in endometrial carcinoma

In recent studies, the tumourigenesis and development of endometrial carcinoma (EC) have been correlated significantly with redox. We aimed to develop and validate a redox-related prognostic model of patients with EC to predict the prognosis and the efficacy of immunotherapy. We downloaded gene expression profiles and clinical information of patients with EC from the Cancer Genome Atlas (TCGA) and the Gene Ontology (GO) dataset. We identified two key differentially expressed redox genes (CYBA and SMPD3) by univariate Cox regression and utilised them to calculate the risk score of all samples. Based on the median of risk scores, we composed low-and high-risk groups and performed correlation analysis with immune cell infiltration and immune checkpoints. Finally, we constructed a nomogram of the prognostic model based on clinical factors and the risk score. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. CYBA and SMPD3 were significantly related to the prognosis of patients with EC and used to construct a risk model. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low-and high-risk groups. The nomogram developed with clinical indicators and the risk scores was effective in predicting the prognosis of patients with EC. In this study, a prognostic model constructed based on two redox-related genes (CYBA and SMPD3) were proved to be independent prognostic factors of EC and associated with tumour immune microenvironment. The redox signature genes have the potential to predict the prognosis and the immunotherapy efficacy of patients with EC.

Costimulatory molecule-related lncRNA model as a potential prognostic biomarker in non-small cell lung cancer

OBJECTIVE

Costimulatory molecules have been demonstrated to exert essential roles in multiple cancers. However, their role in lung cancer remains elusive. Here, we sought to identify costimulatory molecule-related lncRNAs in non-small cell lung cancer (NSCLC) and establish a prognostic signature to predict the prognosis of patients with NSCLC.

METHODS

A total of 535 lung adenocarcinoma (LUAD) and 502 lung squamous cell carcinoma (LUSC) patients from the cancer genome atlas (TCGA) database were recruited. A novel costimulatory molecule-based lncRNA prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival. The Homo_sapiens.GRCh38 data set was set as a reference file for probe annotation.

RESULTS

A total of 593 costimulatory molecule-related lncRNAs were extracted. After analysis, six costimulatory molecule-related lncRNAs (AC084859.1, AC079949.2, HSPC324, LINC01150, LINC01150, and AC090617.5) were screened. A prognostic model based on the six lncRNAs was established using systematic bioinformatics analyses. The prognostic model had a prognostic value in NSCLC patients. Furthermore, a prognostic nomogram was established based on clinical parameters and a risk-score model. Patients with different risk scores had considerably different tumor-infiltrating immune cells, somatic mutational loading, clinical outcomes, signaling pathways, and immunotherapy efficacy. In addition, LINC01137 was associated with unfavorable disease outcomes and fueled tumor progression in NSCLC.

CONCLUSION

Taken together, our study demonstrated that a costimulatory molecule-related lncRNA model could be a potential prognostic biomarker in NSCLC. Moreover, LINC01137 could facilitate the proliferation and invasion of lung cancer.

Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction

Cuproptosis is a newly form of cell death. Cuproptosis related lncRNA in lung adenocarcinoma (LUAD) has also not been fully elucidated. In the present study, we aimed to construct a prognostic signature based on cuproptosis-related lncRNA in LUAD and investigate its association with immunotherapy response. The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were obtained from TCGA database. The LASSO Cox regression was used to construct a prognostic signature. The CIBERSORT, ESTIMATE and ssGSEA algorithms were applied to assess the association between risk score and TME. TIDE score was applied to reflect the efficiency of immunotherapy response. The influence of overexpression of lncRNA TMPO-AS1 on A549 cell was also assessed by in vitro experiments. The lncRNA prognostic signature included AL606834.1, AL138778.1, AP000302.1, AC007384.1, AL161431.1, TMPO-AS1 and KIAA1671-AS1. Low-risk group exhibited much higher immune score, stromal score and ESTIMATE score, but lower tumor purity compared with high-risk groups. Also, low-risk group was associated with a much higher score of immune cells and immune related function sets, indicating an immune activation state. Low-risk patients had relative higher TIDE score and lower TMB. External validation using IMvigor210 immunotherapy cohort demonstrated that low-risk group had a better prognosis and might more easily benefit from immunotherapy. Overexpression of lncRNA TMPO-AS1 promoted the proliferation, migration and invasion of A549 cell line. The novel cuproptosis-related lncRNA signature could predict the prognosis of LUAD patients, and helped clinicians stratify patients appropriate for immunotherapy and determine individual therapeutic strategies.

A novel prognostic model related to oxidative stress for treatment prediction in lung adenocarcinoma

Background

The prognostic model based on oxidative stress for lung adenocarcinoma (LUAD) remains unclear.

Methods

The information of LUAD patients were acquired from TCGA dataset. We also collected two external datasets from GEO for verification. Oxidative stress-related genes (ORGs) were extracted from Genecards. We performed machine learning algorithms, including Univariate Cox regression, Random Survival Forest, and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ORGs to build the OS-score and OS-signature. We drew the Kaplan-Meier and time-dependent receiver operating characteristic curve (ROC) to evaluate the efficacy of the OS-signature in predicting the prognosis of LUAD. We used GISTIC 2.0 and maftool algorithms to explore Genomic mutation of OS-signature. To analyze characteristic of tumor infiltrating immune cells, ESTIMATE, TIMER2.0, MCPcounter and ssGSEA algorithms were applied, thus evaluating the immunotherapeutic strategies. Chemotherapeutics sensitivity analysis was based on pRRophetic package. Finally, PCR assays was also used to detect the expression values of related genes in the OS-signature in cell lines.

Results

Ten ORGs with prognostic value and the OS-signature containing three prognostic ORGs were identified. The significantly better prognosis of LUAD patients was observed in LUAD patients. The efficiency and accuracy of OS-signature in predicting prognosis for LUAD patients was confirmed by survival ROC curves and two external validation data sets. It was clearly observed that patients with high OS-scores had lower immunomodulators levels (with a few exceptions), stromal score, immune score, ESTIMATE score and infiltrating immune cell populations. On the contrary, patients with higher OS-scores were more likely to have higher tumor purity. PCR assays showed that, MRPL44 and CYCS were significantly higher expressed in LUAD cell lines, while CAT was significantly lower expressed.

Conclusion

The novel oxidative stress-related model we identified could be used for prognosis and treatment prediction in lung adenocarcinoma.

Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning

BACKGROUND

RNA-binding proteins (RBPs) are crucial factors that function in the posttranscriptional modification process and are significant in cancer.

OBJECTIVE

This research aimed for a multigene signature to predict the prognosis and immunotherapy response of patients with colon adenocarcinoma (COAD) based on the expression profile of RNA-binding proteins (RBPs).

METHODS

COAD samples retrieved from the TCGA and GEO datasets were utilized for a training dataset and a validation dataset. Totally, 14 shared RBP genes with prognostic significance were identified. Non-negative matrix factorization clusters defined by these RBPs could stratify COAD patients into two molecular subtypes. Cox regression analysis and identification of 8-gene signature categorized COAD patients into high- and low-risk populations with significantly different prognosis and immunotherapy responses.

RESULTS

Our prediction signature was superior to another five well-established prediction models. A nomogram was generated to quantificationally predict the overall survival (OS) rate, validated by calibration curves. Our findings also indicated that high-risk populations possessed an enhanced immune evasion capacity and low-risk populations might benefit immunotherapy, especially for the joint combination of PD-1 and CTLA4 immunosuppressants. DHX15 and LARS2 were detected with significantly different expressions in both datasets, which were further confirmed by qRTPCR and immunohistochemical staining.

CONCLUSION

Our observations supported an eight-RBP-related signature that could be applied for survival prediction and immunotherapy response of patients with COAD.

Development and validation of an endoplasmic reticulum stress long non-coding RNA signature for the prognosis and immune landscape prediction of patients with lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. This work investigated long non-coding RNA (lncRNA) related to endoplasmic reticulum stress (ERS) to predict the prognosis and immune landscape for LUAD patients. Methods: RNA data and clinical data from 497 LUAD patients were collected in the Cancer Genome Atlas database. Pearson correlation analysis, univariate Cox regression, least absolute shrinkage and selection operator regression analyses, as well as the Kaplan-Meier method, were used to screen for ERS-related lncRNAs associated with prognosis. The risk score model was developed using multivariate Cox analysis to separate patients into high- and low-risk groups and a nomogram was constructed and evaluated. Finally, we explore the potential functions and compared the immune landscapes of two groups. Quantitative real-time PCR was used to verify the expression of these lncRNAs. Results: Five ERS-related lncRNAs were shown to be strongly linked to patients' prognosis. A risk score model was built by using these lncRNAs to categorize patients based on their median risk scores. For LUAD patients, the model was found to be an independent prognostic predictor (p < 0.001). The signature and clinical variables were then used to construct a nomogram. With 3-year and 5-year OS' AUC of 0.725 and 0.740, respectively, the nomogram's prediction performance is excellent. The 5-lncRNA signature was associated with DNA replication, epithelial-mesenchymal transition, and the pathway of cell cycle, P53 signaling. Between the two risk groups, immune responses, immune cells, and immunological checkpoints were found to be considerably different. Conclusion: Overall, our findings indicate that the 5 ERS-related lncRNA signature was an excellent prognostic indicator and helped to predict the immunotherapy response for patients with LUAD.

Effects of immune inflammation in head and neck squamous cell carcinoma: Tumor microenvironment, drug resistance, and clinical outcomes

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with a very high mortality rate, and a large number of studies have confirmed the correlation between inflammation and malignant tumors and the involvement of inflammation-related regulators in the progression of HNSCC. However, a prognostic model for HNSCC based on genes involved in inflammatory factors has not been established. Methods: First, we downloaded transcriptome data and clinical information from patients with head and neck squamous cell carcinoma from TCGA and GEO (GSE41613) for data analysis, model construction, and differential gene expression analysis, respectively. Genes associated with inflammatory factors were screened from published papers and intersected with differentially expressed genes to identify differentially expressed inflammatory factor-related genes. Subgroups were then typed according to differentially expressed inflammatory factor-related genes. Univariate, LASSO and multivariate Cox regression algorithms were subsequently applied to identify prognostic genes associated with inflammatory factors and to construct prognostic prediction models. The predictive performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Subsequently, we analyzed differences in immune composition between patients in the high and low risk groups by immune infiltration. The correlation between model genes and drug sensitivity (GSDC and CTRP) was also analyzed based on the GSCALite database. Finally, we examined the expression of prognostic genes in pathological tissues, verifying that these genes can be used to predict prognosis. Results: Using univariate, LASSO, and multivariate cox regression analyses, we developed a prognostic risk model for HNSCC based on 13 genes associated with inflammatory factors (ITGA5, OLR1, CCL5, CXCL8, IL1A, SLC7A2, SCN1B, RGS16, TNFRSF9, PDE4B, NPFFR2, OSM, ROS1). Overall survival (OS) of HNSCC patients in the low-risk group was significantly better than that in the high-risk group in both the training and validation sets. By clustering, we identified three molecular subtypes of HNSCC carcinoma (C1, C2, and C3), with C1 subtype having significantly better OS than C2 and C3 subtypes. ROC analysis suggests that our model has precise predictive power for patients with HNSCC. Enrichment analysis showed that the high-risk and low-risk groups showed strong immune function differences. CIBERSORT immune infiltration score showed that 25 related and differentially expressed inflammatory factor genes were all associated with immune function. As the risk score increases, specific immune function activation decreases in tumor tissue, which is associated with poor prognosis. We also screened for susceptibility between the high-risk and low-risk groups and showed that patients in the high-risk group were more sensitive to talazoparib-1259, camptothecin-1003, vincristine-1818, Azd5991-1720, Teniposide-1809, and Nutlin-3a (-) -1047.Finally, we examined the expression of OLR1, SCN1B, and PDE4B genes in HNSCC pathological tissues and validated that these genes could be used to predict the prognosis of HNSCC. Conclusion: In this experiment, we propose a prognostic model for HNSCC based on inflammation-related factors. It is a non-invasive genomic characterization prediction method that has shown satisfactory and effective performance in predicting patient survival outcomes and treatment response. More interdisciplinary areas combining medicine and electronics will be explored in the future.

Establishment of a cell senescence related prognostic model for predicting prognosis in glioblastoma

Background: Glioblastoma (GBM) is highly malignant and has a worse prognosis with age, and next-generation sequencing (NGS) provides us with a huge amount of information about GBM. Materials and Methods: Through the enrichment scores of cell senescence-related pathways, we constructed a consensus matrix and mined molecular subtypes and explored the differences in pathological, immune/pathway and prognostic. Also we identified key genes related to cell senescence characteristics using least absolute shrinkage and selection operator (Lasso) regression and univariate COX regression analysis models. The use of risk factor formats to construct clinical prognostic models also explored the differences in immunotherapy/chemotherapy within the senescence-related signatures score (SRS.score) subgroups. Decision trees built with machine learning to identify the main factors affecting prognosis have further improved the prognosis model and survival prediction. Results: We obtained seven prognostic-related pathways related to cell senescence. We constructed four different molecular subtypes and found patients with subtype C1 had the worst prognosis. C4 had the highest proportion of patients with IDH mutations. 1005 differentially expressed genes (DEGs) were analyzed, and finally 194 Risk genes and 38 Protective genes were obtained. Eight key genes responsible for cell senescence were finally identified. The clinical prognosis model was established based on SRS.score, and the prognosis of patients with high SRS.score was worse. SRS.score and age were the vital risk factors for GBM patients through decision tree model mining. Conclusion: We constructed a clinical prognosis model that could provide high prediction accuracy and survival prediction ability for adjuvant treatment of patients with GBM.

Transcriptome analysis of adipocytokines and their-related LncRNAs in lung adenocarcinoma revealing the association with prognosis, immune infiltration, and metabolic characteristics

Lung adenocarcinoma (LUAD) is amongst the major contributors to cancer-related deaths on a global scale. Adipocytokines and long non-coding RNAs (lncRNAs) are indispensable participants in cancer. We performed a pan-cancer analysis of the mRNA expression, single nucleotide variation, copy number variation, and prognostic value of adipocytokines. LUAD samples were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Simultaneously, train, internal and external cohorts were grouped. After a stepwise screening of optimized genes through least absolute shrinkage and selection operator regression analysis, random forest algorithm,, and Cox regression analysis, an adipocytokine-related prognostic signature (ARPS) with superior performance compared with four additional well-established signatures for survival prediction was constructed. After determination of risk levels, the discrepancy of immune microenvironment, immune checkpoint gene expression, immune subtypes, and immune response in low- and high-risk cohorts were explored through multiple bioinformatics methods. Abnormal pathways underlying high- and low-risk subgroups were identified through gene set enrichment analysis (GSEA). Immune-and metabolism-related pathways that were correlated with risk score were selected through single sample GSEA. Finally, a nomogram with satisfied predictive survival probability was plotted. In summary, this study offers meaningful information for clinical treatment and scientific investigation.

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066). Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively. Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma

Background

Several cancers, including lung adenocarcinoma (LUAD), are caused by genes related to necroptosis. However, it is still unknown how necroptosis-related long noncoding RNAs (lncRNAs) may be involved in LUAD. In order to predict the prognosis of LUAD patients and personalize immunotherapy, this study set out to construct a necroptosis-related lncRNA prognostic signature (NLPS).

Methods

The Cancer Genome Atlas (TCGA) database was used to download the LUAD transcriptome data and the associated clinical data. lncRNAs associated with necroptosis were screened using coexpression analysis. An NLPS was built using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The Gene Expression Omnibus (GEO) database's GSE30219 was used to validate the NLPS. The prognostic value of the risk score was assessed using Kaplan-Meier survival, receiver operating characteristic (ROC) Cox regression, multivariate Cox regression, and nomogram analyses. Then, we looked into the differences between the low- and high-risk groups in the tumor immune microenvironment, immunotherapy response, and half-maximal inhibitory concentration (IC50).

Results

The 14 lncRNAs in a novel NLPS were created. With further validation in the GSE30219 dataset, the risk score according to the NLPS was an independent prognostic indicator for LUAD patients. Patients with better overall survival (OS) in the low-risk group, who were characterized by increased immune cell infiltration, tumor mutational burden (TMB), and immunophenoscore (IPS), may have hot tumors and higher immunotherapy response rates. In addition, the risk score was also closely linked to sensitivity to various anticancer medications.

Conclusions

We constructed a novel NLPS that could predict OS and sensitivity to immunotherapy in LUAD patients.

Exosomal microRNA panel as a diagnostic biomarker in patients with hepatocellular carcinoma

Background: Diagnostic tools for hepatocellular carcinoma (HCC) are critical for patient treatment and prognosis. Thus, this study explored the diagnostic value of the exosomal microRNA panel for HCC.

Methods: Expression profiles of microRNAs in exosomes and plasma of HCC and control groups were assessed using microRNA microarray analysis. Reverse transcription-quantitative PCR was applied to evaluate the expression of candidate microRNAs in blood samples from 50 HCC patients, 50 hepatic cirrhosis patients, and 50 healthy subjects. The area calculated the diagnostic accuracy of the microRNAs and microRNA panel under the receiver operating characteristic curve (AUC).

Results: MicroRNA microarray analysis revealed that there were more differentially expressed microRNAs in the exosome HCC group than plasma HCC group. Among the 43 differentially expressed microRNAs contained in both exosomes and plasma, we finally decided to testify the expression and diagnostic significance of microRNA-26a, microRNA-29c, and microRNA-199a. The results indicated that expression of the microRNA-26a, microRNA-29c, and microRNA-199a in both exosomes and plasma was significantly lower in HCC patients compared with hepatic cirrhosis and healthy group. Interestingly, exosomal microRNAs were substantially more accurate in diagnosing HCC than microRNAs and alpha-fetoprotein in plasma. Moreover, the exosomal microRNA panel containing microRNA-26a, microRNA-29c, and microRNA-199a showed high accuracy in discriminating HCC from healthy (AUC = 0.994; sensitivity 100%; specificity 96%) and hepatic cirrhosis group (AUC = 0.965; sensitivity 92%; specificity 90%).

Conclusion: This study revealed that the exosomal microRNA panel has high accuracy in diagnosing HCC and has important clinical significance.

Antioxidant Systems, lncRNAs, and Tunneling Nanotubes in Cell Death Rescue from Cigarette Smoke Exposure

Cigarette smoke is a rich source of carcinogens and reactive oxygen species (ROS) that can damage macromolecules including DNA. Repair systems can restore DNA integrity. Depending on the duration or intensity of stress signals, cells may utilize various survival and adaptive mechanisms. ROS levels are kept in check through redundant detoxification processes controlled largely by antioxidant systems. This review covers and expands on the mechanisms available to cigarette smoke-exposed cancer cells for restoring the redox balance. These include multiple layers of transcriptional control, each of which is posited to be activated upon reaching a particular stress threshold, among them the NRF2 pathway, the AP-1 and NF-kB pathways, and, finally, TP53, which triggers apoptosis if extreme toxicity is reached. The review also discusses long noncoding RNAs, which have been implicated recently in regulating oxidative stress—with roles in ROS detoxification, the inflammatory response, oxidative stress-induced apoptosis, and mitochondrial oxidative phosphorylation. Lastly, the emerging roles of tunneling nanotubes in providing additional mechanisms for metabolic rescue and the regulation of redox imbalance are considered, further highlighting the expanded redox reset arsenal available to cells.

Pan-Cancer Methylated Dysregulation of Long Non-coding RNAs Reveals Epigenetic Biomarkers

Different cancer types not only have common characteristics but also have their own characteristics respectively. The mechanism of these specific and common characteristics is still unclear. Pan-cancer analysis can help understand the similarities and differences among cancer types by systematically describing different patterns in cancers and identifying cancer-specific and cancer-common molecular biomarkers. While long non-coding RNAs (lncRNAs) are key cancer modulators, there is still a lack of pan-cancer analysis for lncRNA methylation dysregulation. In this study, we integrated lncRNA methylation, lncRNA expression and mRNA expression data to illuminate specific and common lncRNA methylation patterns in 23 cancer types. Then, we screened aberrantly methylated lncRNAs that negatively regulated lncRNA expression and mapped them to the ceRNA relationship for further validation. 29 lncRNAs were identified as diagnostic biomarkers for their corresponding cancer types, with lncRNA AC027601 was identified as a new KIRC-associated biomarker, and lncRNA ACTA2-AS1 was regarded as a carcinogenic factor of KIRP. Two lncRNAs HOXA-AS2 and AC007228 were identified as pan-cancer biomarkers. In general, the cancer-specific and cancer-common lncRNA biomarkers identified in this study may aid in cancer diagnosis and treatment.

Long non-coding RNA FAM230B is a novel prognostic and diagnostic biomarker for lung adenocarcinoma

Long non-coding RNA (lncRNA) FAM230B has been reported to participate in gastric cancer and papillary thyroid cancer, while its role in other cancers has not been reported. We then explored the role of FAM230B in lung adenocarcinoma (LA). This study enrolled a total of 60 LA patients, 60 patients with gastric reflux disease (GRD), 60 cases of chronic obstructive pulmonary disease (COPD), 60 cases of asthma and 60 cases of healthy controls. LA and paired non-tumor tissues were donated by all LA patients. Plasma samples were donated by all participants. Expression of FAM230B in these samples was determined by RT-qPCR. The 60 LA patients were followed up for 5 years to evaluate the prognostic value of plasma FAM230B for LA. Diagnostic value of FAM230B for LA was analyzed with ROC curve analysis. FAM230B was highly expressed in LA tissues compared to that in non-tumor samples. In addition, plasma FAM230B was specifically upregulated in LA patients, but not in GRD, COPD and asthma patients. High expression levels of FAM230B in plasma samples were closely correlated with poor survival. Plasma FAM230B effectively separated LA patients from GRD, COPD, asthma and control groups. Plasma FAM230B was closely correlated with tumor size, but not other clinical factors of LA patients. Therefore, FAM230B is highly upregulated in LA and may serve as a potential diagnostic and prognostic biomarker for LA.

CYP1B1-AS1 Is a Novel Biomarker in Glioblastoma by Comprehensive Analysis

Objective

Growing evidence shows that enhancer RNAs (eRNAs) are pivotal for tumor progression. In this research, our team aimed to identify the survival-related eRNAs and further explore their potential function in glioblastoma (GBM).

Methods

RNA-sequencing data in 31 tumor types were acquired from TCGA datasets. The survival-related eRNAs were identified by the use of Kaplan-Meier survival analyses and Spearman's correlation analyses. KEGG pathway enrichment analysis was completed to investigate the underlying signal paths of the critical eRNA. Pancancer assays were applied to explore the association between CYP1B1-AS1 and CYP1B1.

Results

We identified 74 survival-related eRNAs and focused on CYP1B1-AS1 which displayed the greatest cor value. CYP1B1 was identified as a regulatory target of CYP1B1-AS1. KEGG analyses suggested that CYP1B1-AS1 might play an essential role through CK-CKR mutual effect, complement and coagulation cascades, TNF signal path, and JAK-STAT signal path. The pancancer verification outcomes revealed that CYP1B1-AS1 was related to survival in 4 cancers, i.e., LIHC, KIRP, KICH, and KIRC. Association was discovered between CYP1B1-AS1 and the targeted gene, CYP1B1, in 29 cancer types.

Conclusion

The outcomes herein provided the first evidence that overexpression of CYP1B1-AS1 might be a potential molecular biomarker for predicting the prognosis of patients with GBM.

Identification and validation of a prognostic risk-scoring model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma

Colon adenocarcinoma (COAD) is the primary factor responsible for cancer-related mortalities in western countries, and its development and progression are affected by altered sphingolipid metabolism. The current study aimed at investigating the effects of sphingolipid metabolism-related (SLP) genes on multiple human cancers, especially on COAD. We obtained 1287 SLP genes from the GeneCard and MsigDb databases along with the public transcriptome data and the related clinical information. The univariate Cox regression analysis suggested that 26 SLP genes were substantially related to the prognosis of COAD, and a majority of SLP genes served as the risk genes for the tumor, insinuating a potential pathogenic effect of SLP in COAD development. Pan-cancer characterization of SLP genes summarized their expression traits, mutation traits, and methylation levels. Subsequently, we focused on the thorough research of COAD. With the help of unsupervised clustering, 1008 COAD patients were successfully divided into two distinct subtypes (C1 and C2). C1 subtype is characterized by a poor prognosis, activation of SLP pathways, high expression of SLP genes, disordered carcinogenic pathways, and immune microenvironment. Based on the clusters of SLP, we developed and validated a novel prognostic model, consisting of ANO1, C2CD4A, EEF1A2, GRP, HEYL, IGF1, LAMA2, LSAMP, RBP1, and TCEAL2, to quantitatively evaluate the clinical outcomes of COAD. The Kaplain-Meier survival curves and ROC curves highlighted the accuracy of our SLP model in both internal and external cohorts. Compared to normal colon tissues, expression of C2CD4A was detected to be significantly higher in COAD; whereas, expression levels of EEF1A2, IGF1, and TCEAL2 were detected to be significantly lower in COAD. Overall, our research emphasized the pathogenic role of SLP in COAD and found that targeting SLP might help improve the clinical outcomes of COAD. The risk model based on SLP metabolism provided a new horizon for prognosis assessment and customized patient intervention.

Seven autophagy-related lncRNAs are associated with the tumor immune microenvironment in predicting survival risk of nonsmall cell lung cancer

BACKGROUND

Nonsmall cell lung cancer (NSCLC) ranks first among global cancer-related deaths. Despite the emergence of various immunological and targeted therapies, immune tolerance remains a barrier to treatment.

METHODS

It has been found that this obstacle can be overcome by targeting autophagy-related genes (ATGs). ATGs were screened by coexpression analysis and the genes related to the prognosis of lung cancer were screened using Kaplan-Meier (K-M) survival analysis, univariate Cox regression and multivariate Cox regression. The prognostic risk model of ATGs was constructed and verified using K-M survival analysis and receiver operating characteristic (ROC) curve analysis.

RESULTS

The prognostic risk model of ATGs was constructed. Gene set enrichment analysis (GSEA) showed that the function and pathway of ATG enrichment were closely related to immune cell function. CIBERSORT, LM22 matrix and Pearson correlation analysis showed that risk signals were significantly correlated with immune cell infiltration and immune checkpoint genes.

CONCLUSIONS

We identified and independently verified the ATG (AL691432.2, MMP2-AS1, AC124067.2, CRNDE, ABALON, AL161431.1, NKILA) in NSCLC patients and found that immune regulation in the tumor microenvironment is closely related to this gene.

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.