Integrated analysis of hypoxia-associated lncRNA signature to predict prognosis and immune microenvironment of lung adenocarcinoma patients

Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma

Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.

Isoflurane pretreatment protects against myocardial ischemia/reperfusion injury via mediating lncRNA CASC15/miR-542-3p axis

The protective effect of isoflurane on cardiomyocyte ischemia/reperfusion injury (I/RI) was explored in hypoxia and reoxygenation (H/R) induced cardiomyocyte injury model. In terms of mechanism, the participation of long non-coding RNA CASC15/microR-542-3p axis was further discussed. H9c2 cells received H/R treatment to mimic myocardial I/RI. RT-qPCR was performed to quantify mRNA levels. Cell viability and apoptosis were evaluated after isoflurane pretreatment and cell transfection. ELISA was performed to measure the concentrations of inflammatory/oxidative stress-related cytokines (TNF-α, IL-6, MDA, SOD). The target relationship between CASC12 and miR-542-3p was determined via dual-luciferase reporter assay. Isoflurane pretreatment alleviated H/R-induced cell viability suppression and cell apoptosis promotion, which was accompanied by CASC15 downregulation. CASC15 overexpression abolished the influence of isoflurane on cardiomyocytes' viability and apoptosis. H/R-induced excessive release of TNF-α and IL-6 was hold down by isoflurane, which was re-activated after CASC15 overexpression. The concentration changes of both MDA and SOD by isoflurane were reversed by CASC15 overexpression. CASC15 functioned as miR-542-3p sponger, and miR-542-3p overexpression attenuated the effect of isoflurane and CASC15 on H/R-induced cardiac I/RI. Isoflurane pretreatment was beneficial for the alleviation of cardiac I/RI by inhibiting oxidative stress and myocardial inflammatory response. CASC15/miR-542-3p axis was required for isoflurane to exhibit its protective activity against cardiac I/RI.

Signature identification based on immunogenic cell death-related lncRNAs to predict the prognosis and immune activity of patients with endometrial carcinoma

Background

Endometrial carcinoma (EC) is one of the most prevalent gynecologic malignancies and requires further classification for treatment and prognosis. Long non-coding RNAs (lncRNAs) and immunogenic cell death (ICD) play a critical role in tumor progression. Nevertheless, the role of lncRNAs in ICD in EC remains unclear. This study aimed to explore the role of ICD related-lncRNAs in EC via bioinformatics and establish a prognostic risk model based on the ICD-related lncRNAs. We also explored immune infiltration and immune cell function across prognostic groups and made treatment recommendations.

Methods

A total of 552 EC samples and clinical data of 548 EC patients were extracted from The Cancer Genome Atlas (TCGA) database and University of California Santa Cruz (UCSC) Xena, respectively. A prognostic-related feature and risk model was developed using the least absolute shrinkage and selection operator (LASSO). Subtypes were classified with consensus cluster analysis and validated with t-Distributed Stochastic Neighbor Embedding (tSNE). Kaplan-Meier analysis was conducted to assess differences in survival. Infiltration by immune cells was estimated by single sample gene set enrichment analysis (ssGSEA), Tumor IMmune Estimation Resource (TIMER) algorithm. Quantitative polymerase chain reaction (qPCR) was used to detect lncRNAs expression in clinical samples and cell lines. A series of studies was conducted in vitro and in vivo to examine the effects of knockdown or overexpression of lncRNAs on ICD.

Results

In total, 16 ICD-related lncRNAs with prognostic values were identified. Using SCARNA9, FAM198B-AS1, FKBP14-AS1, FBXO30-DT, LINC01943, and AL161431.1 as risk model, their predictive accuracy and discrimination were assessed. We divided EC patients into high-risk and low-risk groups. The analysis showed that the risk model was an independent prognostic factor. The prognosis of the high- and low-risk groups was different, and the overall survival (OS) of the high-risk group was lower. The low-risk group had higher immune cell infiltration and immune scores. Consensus clustering analysis divided the samples into four subtypes, of which cluster 4 had higher immune cell infiltration and immune scores.

Conclusions

A prognostic signature composed of six ICD related-lncRNAs in EC was established, and a risk model based on this signature can be used to predict the prognosis of patients with EC.

Linc00941 fuels ribogenesis and protein synthesis by supporting robust cMYC translation in malignant pleural mesothelioma

Malignant pleural mesothelioma is a rare and lethal cancer caused by exposure to asbestos. The highly inflammatory environment caused by fibers accumulation forces cells to undergo profound adaptation to gain survival advantages. Prioritizing the synthesis of essential transcripts is an efficient mechanism coordinated by multiple molecules, including long non-coding RNAs. Enhancing the knowledge about these mechanisms is an essential weapon in combating mesothelioma. Linc00941 correlates to bad prognosis in various cancers, but it is reported to partake in distinct and apparently irreconcilable processes. In this work, we report that linc00941 supports the survival and aggressiveness of mesothelioma cells by influencing protein synthesis and ribosome biogenesis. Linc00941 binds to the translation initiation factor eIF4G, promoting the selective protein synthesis of cMYC, which, in turn, enhances the expression of key genes involved in translation. We analyzed a retrospective cohort of 97 mesothelioma patients' samples from our institution, revealing that linc00941 expression strongly correlates with reduced survival probability. This discovery clarifies linc00941's role in mesothelioma and proposes a unified mechanism of action for this lncRNA involving the selective translation of essential oncogenes, reconciling the discrepancies about its function.

Deciphering disulfidptosis: Uncovering a lncRNA-based signature for prognostic assessment, personalized immunotherapy, and therapeutic agent selection in lung adenocarcinoma patients

BACKGROUND

Disulfidptosis, a recently identified type of regulated cell death, plays critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remains to be fully elucidated. We aimed to adopt this concept to develop and validate a lncRNA signature for LUAD prognostic prediction.

METHODS

For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Disulfidptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature DISULncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, nomogram predictor analysis, and functional assays in our validation process. We also compared DISULncSig with previous studies. We performed analyses to evaluate DISULncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of DISULncSig lncRNAs, and the ability of DISULncSig in pan-cancer was also assessed.

RESULTS

DISULncSig containing twelve lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, DISULncSig had more prognostic ability advantages. DISULncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high DISULncSig score population, we found ten drug candidates, among which epothilone-b may have the most potential. The pan-cancer analysis found that DISULncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the DISULncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic DISULncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.

LINC00941: a novel player involved in the progression of human cancers

LINC00941, also known as lncRNA-MUF, is an intergenic non-coding RNA located on chromosome 12p11.21. It actively participates in a complex competing endogenous RNA network, regulating the expression of microRNA and its downstream proteins. Through transcriptional and post-transcriptional regulation, LINC00941 plays a vital role in multiple signaling pathways, influencing cell behaviors such as tumor cell proliferation, epithelial-mesenchymal transition, migration, and invasion. Noteworthy is its consistently high expression in various tumor types, closely correlating with clinicopathological features and cancer prognoses. Elevated LINC00941 levels are associated with adverse clinical outcomes, including increased tumor size, extensive lymphatic metastasis, and distant metastasis, leading to poorer survival rates across different cancers. Additionally, LINC00941 and its associated genes are linked to various targeted drugs available in the market. In this comprehensive review, we systematically summarize existing studies, detailing LINC00941's differential expression, clinicopathological and prognostic implications, regulatory mechanisms, and associated therapeutic drugs. Our analysis includes relevant charts and incorporates bioinformatics analyses to verify LINC00941's differential expression in pan-cancer and explore potential transcriptional regulation patterns of downstream targets. This work not only establishes a robust data foundation but also guides future research directions. Given its potential as a significant cancer biomarker and therapeutic target, further investigation into LINC00941's differential expression and regulatory mechanisms is essential.

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.

The hypoxia-associated genes in immune infiltration and treatment options of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a common lung cancer with a poor prognosis under standard chemotherapy. Hypoxia is a crucial factor in the development of solid tumors, and hypoxia-related genes (HRGs) are closely associated with the proliferation of LUAD cells.

Methods

In this study, LUAD HRGs were screened, and bioinformatics analysis and experimental validation were conducted. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to gather LUAD RNA-seq data and accompanying clinical information. LUAD subtypes were identified by unsupervised cluster analysis, and immune infiltration analysis of subtypes was conducted by GSVA and ssGSEA. Cox regression and LASSO regression analyses were used to obtain prognosis-related HRGs. Prognostic analysis was used to evaluate HRGs. Differences in enrichment pathways and immunotherapy were observed between risk groups based on GSEA and the TIDE method. Finally, RT-PCR and in vitro experiments were used to confirm prognosis-related HRG expression in LUAD cells.

Results

Two hypoxia-associated subtypes of LUAD were distinguished, demonstrating significant differences in prognostic analysis and immunological characteristics between subtypes. A prognostic model based on six HRGs (HK1, PDK3, PFKL, SLC2A1, STC1, and XPNPEP1) was developed for LUAD. HK1, SLC2A1, STC1, and XPNPEP1 were found to be risk factors for LUAD. PDK3 and PFKL were protective factors in LUAD patients.

Conclusion

This study demonstrates the effect of hypoxia-associated genes on immune infiltration in LUAD and provides options for immunotherapy and therapeutic strategies in LUAD.

LncRNA AL161431.1 predicts prognosis and drug response in head and neck squamous cell carcinoma

Background

Long non-coding RNAs (lncRNAs) are increasingly recognized as essential players in various biological processes due to their interactions with DNA, RNA, and protein. Emerging studies have demonstrated lncRNAs as prognostic biomarkers in multiple cancers. However, the prognostic effect of lncRNA AL161431.1 in head and neck squamous cell carcinoma (HNSCC) patients has not been reported.

Methods

In the present study, we conducted a series of analyses to identify and validate the prognostic value of lncRNA AL161431.1 in HNSCC, which included differential lncRNAs screening, survival analysis, Cox regression analysis, time ROCanalysis, nomogram prediction, enrichment analysis, tumor infiltration of immune cells, drug sensitivity analysis, and quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In this study, we performed a comprehensive survival and predictive analysis and demonstrated that AL161431.1 was an independent prognostic factor of HNSCC, for which a high AL161431.1 level indicated poor survival in HNSCC. Functional enrichment analyses found that cell growth and immune-related pathways were significantly enriched in HNSCC, suggesting that AL161431.1 may play a role in tumor development and tumor microenvironment (TME). AL161431.1-related immune cells infiltration analysis demonstrated that AL161431.1 expression is significantly positively associated with M0 macrophages in HNSCC (P<0.001). Using "OncoPredict", we recognized chemotherapy drugs sensitive to the high expression group. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to identify the expression level of AL161431.1 in HNSCC, and the results further validated our findings.

Conclusions

Our findings suggest that AL161431.1 is a reliable prognostic marker for HNSCC and can potentially be an effective therapeutic target.

Identification of a cuproptosis-related lncRNA prognostic signature in lung adenocarcinoma

PURPOSE

Cuproptosis-related long non-coding RNA (lncRNA) diseases are associated with the occurrence and development of tumors. This study aimed to investigate whether cuproptosis-related lncRNA can predict the prognosis of patients with lung adenocarcinoma (LUAD).

METHODS

Cuproptosis-related lncRNA prognosis (CLPS) model was successfully constructed through cox regression and lasso regression analyses. Then, the prognostic value of CLPS model was tested through the survival analysis, the ROC curve and the nomogram. Finally, the correlation of CLPS model with tumor immunity and tumor mutation burden was analyzed, and the potential susceptibility of drugs for LUAD were predicted.

RESULTS

CLPS model for LUAD (AC090948.1, CRIM1-DT, AC026356.2, AC004832.5, AL161431.1) was successfully constructed, which has an independent prognostic value. Furthermore, the risk score of CLPS model was correlated with tumor immune characteristics and immune escape, which can predict the sensitivity of drugs including Cisplatin, Etoposide, Gemcitabine, and Erlotinib.

CONCLUSIONS

In conclusion, it was found that CLPS model was associated with tumor immunity and tumor mutation load, which also predicted four potentially sensitive drugs for LUAD patients at different risks.

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

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.

LncRNA ARAP1-AS1 contributes to lung adenocarcinoma development by targeting miR-8068 to upregulate CEACAM5

BACKGROUND

It has been discovered that lncRNA ARAP1-AS1 is upregulated and operates as a tumor promoter in many cancers. However, its pattern of expression and potential mechanism in lung adenocarcinoma (LUAD) is still unknown.

METHODS

The levels of lncRNA ARAP1-AS1, miR-8068, and CEACAM5 expressions in LUAD cell lines and tissues were assessed by conducting western blot and RT-qPCR analyses. MiR-8068's potential targeting relationships with lncRNA ARAP1-AS1 and CEACAM5 were ascertained by performing bioinformatics analysis. The interaction of lncRNA ARAP1-AS1 with miR-8068 was validated by means of by RIP and luciferase reporter experiments. CCK-8, cell adhesion, and Transwell migration experiments were conducted to study how lncRNA ARAP1-AS1 affects LUAD cell migration, adhesion, and proliferation. To confirm the function of lncRNA ARAP1-AS1 in vivo, a tumor formation experiment was executed.

RESULTS

An elevated expression of lncRNA ARAP1-AS1 was observed among the LUAD cells and tissues. The overexpression of lncRNA ARAP1-AS boosted cell proliferation, adhesion, and migration in LUAD and also favored in vivo tumor growth. MiR-8068 was found to be lncRNA ARAP1-AS1's target gene. MiR-8068 overexpression partially antagonized lncRNA ARAP1-AS1's promotive effect on proliferation, viability, and adhesion. Meanwhile CEACAM5 could alleviate the miR-8068-induced inhibition of tumor growth. The negative correlation of miR-8068 with lncRNA ARAP1-AS1 or CEACAM5 was also revealed.

CONCLUSION

To upregulate CEACAM5 expression lncRNA ARAP1-AS1 targeted miR-8068, thus promoting the progression of LUAD. This indicates that the lncRNA ARAP1-AS1/miR-8068/CEACAM5 axis has potential as a therapeutic target in LUAD treatment.

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

Definition of a Novel Cuproptosis-Relevant lncRNA Signature for Uncovering Distinct Survival, Genomic Alterations, and Treatment Implications in Lung Adenocarcinoma

Objective

Cuproptosis is a newly discovered copper-independent cell death modality, and limited evidence suggests the critical implications in human cancers. Nonetheless, the clinical impacts of cuproptosis-relevant lncRNAs in lung adenocarcinoma (LUAD) remain largely ill-defined. The present study was aimed at defining a cuproptosis-relevant lncRNA signature for LUAD and discuss the clinical utility.

Methods

We collected transcriptome expression profiling, clinical information, somatic mutation, and copy number variations from TCGA-LUAD cohort retrospectively. The genetic alterations of cuproptosis genes were systematically assessed across LUAD, and cuproptosis-relevant lncRNAs were screened for defining a LASSO prognostic model. Genomic alterations, immunological and stemness features, and therapeutic sensitivity were studied with a series of computational approaches.

Results

Cuproptosis genes displayed aberrant expression and widespread genomic alterations across LUAD, potentially modulated by m6A/m5C/m1A RNA modification mechanisms. We defined a cuproptosis-relevant lncRNA signature, with a reliable efficacy in predicting clinical outcomes. High-risk subset displayed higher somatic mutations, CNVs, TMB, SNV neoantigens, aneuploidy score, CTA score, homologous recombination defects, and intratumor heterogeneity, cytolytic activity, CD8+ T effector, and antigen processing machinery, proving that this subset might benefit from immunotherapy. Increased stemness indexes and activity of oncogenic pathways might contribute to undesirable prognostic outcomes for high-risk subset. Additionally, high-risk patients generally exhibited higher response to chemotherapeutic agents (cisplatin, etc.). We also predicted several small molecule compounds (GSK461364, KX2-391, etc.) for treating this subset.

Conclusion

Accordingly, this cuproptosis-relevant lncRNA signature offers an efficient approach to identify and characterize diverse prognosis, genomic alterations, and treatment outcomes in LUAD, thus potentially assisting personalized therapy.

Bioinformatics algorithm for lung adenocarcinoma based on macropinocytosis-related long noncoding RNAs as a reliable indicator for predicting survival outcomes and selecting suitable anti-tumor drugs

As a highly conserved endocytic mechanism during evolution, macropinocytosis is enhanced in several malignant tumors, which promotes tumor growth by ingesting extracellular nutrients. Recent research has emphasized the crucial role of macropinocytosis in tumor immunity. In the present study, we established a new macropinocytosis-related algorithm comprising molecular subtypes and a prognostic signature, in which patients with lung adenocarcinoma (LUAD) were classified into different clusters and risk groups based on the expression of 16 macropinocytosis-related long noncoding RNAs. According to the molecular subtypes, we discovered that patients with LUAD in cluster1 had a higher content of stromal cells and immune cells, stronger intensity of immune activities, higher expression of PD1, PDL1, and HAVCR2, and a higher tumor mutational burden, while patients in cluster2 exhibited better survival advantages. Furthermore, the constructed prognostic signature revealed that low-risk patients showed better survival outcomes, earlier tumor stage, higher abundance of stromal cells and immune cells, higher immune activities, higher expression of PD1, PDL1, CTLA4, and HAVCR2, and more sensitivity to Paclitaxel and Erlotinib. By contrast, patients with high scores were more suitable for Gefitinib treatment. In conclusion, the novel algorithm that divided patients with LUAD into different groups according to their clusters and risk groups, which could provide theoretical support for predicting their survival outcomes and selecting drugs for chemotherapy, targeted therapy, and immunotherapy.

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.

Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.

Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.

Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

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.

Long non-coding RNAs and exosomal lncRNAs: Potential functions in lung cancer progression, drug resistance and tumor microenvironment remodeling

Among the different kinds of tumors threatening human life, lung cancer is one that is commonly observed in both males and females. The aggressive behavior of lung cancer and interactions occurring in tumor microenvironment enhances the malignancy of this tumor. The lung tumor cells have demonstrated capacity in developing chemo- and radio-resistance. LncRNAs are a category of non-coding RNAs that do not encode proteins, but their aberrant expression is responsible for tumor development, especially lung cancer. In the present review, we focus on both lncRNAs and exosomal lncRNAs in lung cancer, and their ability in regulating proliferation and metastasis. Cell cycle progression and molecular mechanisms related to lung cancer metastasis such as EMT and MMPs are regulated by lncRNAs. LncRNAs interact with miRNAs, STAT, Wnt, EZH2, PTEN and PI3K/Akt signaling pathways to affect progression of lung cancer cells. LncRNAs demonstrate both tumor-suppressor and tumor-promoting functions in lung cancer. They can be considered as biomarkers in lung cancer and especially exosomal lncRNAs present in body fluids are potential tools for minimally invasive diagnosis. Furthermore, we discuss regulation of lncRNAs by anti-cancer drugs and genetic tools as well as the role of these factors in therapy response of lung cancer cells.

Construction of a Prognostic Model for Hypoxia-Related LncRNAs and Prediction of the Immune Landscape in the Digestive System Pan-Cancer

Digestive system pan-cancer is a general term for digestive system tumors including colorectal carcinoma (CRC), esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), and liver hepatocellular carcinoma (LIHC). Since the anatomical location, function and metabolism are closely related, there may be similarities in development and progression of these tumors. Hypoxia is the consequence of an imbalance between oxygen demand and supply, and intracellular hypoxia is associated with malignant progression, treatment resistance, and poor prognosis in tumors. Therefore, an urgent and challenging task is to investigate the molecular mechanisms associated with hypoxia in digestive system pan-cancer for the prognosis and treatment of digestive tract tumors. In this study, we identified 18 hypoxia-related lncRNAs (HRlncRNAs) by co-expression analysis between hypoxia genes and lncRNAs from digestive system pan-cancer. Six HRlncRNAs were then obtained using lasso regression and multivariate cox analysis to construct a prognostic model. Next, the Akaike information criterion (AIC) values for 3-year receiver operating curve (ROC) were counted to determine the cut-off point and establish an optimal model to distinguish between high- or low-risk groups among patients with digestive system pan-cancer. To evaluate the stability of the prognosis model, we validated it in terms of survival outcomes, clinicopathological stage, tumor-infiltrating immune cells, immune checkpoint inhibitors (ICIs) and anticancer drugs sensitivity. The results suggested that high- risk group had a worse prognosis and a more positive association with tumor-infiltrating immune cells such as B cells, cancer-associated fibroblasts, endothelial cells, monocytes, macrophages and bone marrow dendritic cells in digestive system pan-cancer. Immune checkpoint inhibitors (ICIs) related biomarkers discovered that high-risk group was positively correlated with high expression of HAVCR2 in digestive system pan-cancer. The anticancer drugs sensitivity analysis showed that the high-risk group was associated with the lower half-inhibitory centration (IC50) of Imatinib in digestive system pan-cancer. In conclusion, the prognostic model of HRlncRNAs showed a promising clinical prediction value and may provide a useful reference for the diagnosis and treatment of the digestive system tumors.

Construction of an algorithm based on oncosis-related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

Background

As an important non‐apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer.

Methods

In this study, we established an oncosis‐based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis‐related lncRNAs associated with the prognosis (CARD8‐AS1, LINC00941, LINC01137, LINC01116, AC010980.2, LINC00324, AL365203.2, AL606489.1, AC004687.1, HLA‐DQB1‐AS1, and AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low‐risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low‐risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD‐1, CTLA‐4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD‐1/CTLA‐4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis‐based algorithm suggested that low‐risk patients in cluster2 have the most obvious survival advantage.

Conclusion

The novel oncosis‐based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD.

Methods

GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively.

Results

We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores.

Conclusions

Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

Review of LINC00707: A Novel LncRNA and Promising Biomarker for Human Diseases

Long noncoding RNAs (lncRNAs) are a major type of noncoding RNA greater than 200 nucleotides in length involved in important regulatory processes. Abnormal expression of certain lncRNAs contributes to the pathogenesis of multiple diseases, including cancers. The lncRNA LINC00707 is located on chromosome 10p14 and is abnormally expressed in numerous disease types, and particularly in several types of cancer. High LINC00707 levels mediate a series of biological functions, including cell proliferation, apoptosis, metastasis, invasion, cell cycle arrest, inflammation, and even osteogenic differentiation. In this review, we discuss the main functions and underlying mechanisms of LINC00707 in different diseases and describe promising applications of LINC00707 in clinical settings.

Development and Validation of Lactate Metabolism-Related lncRNA Signature as a Prognostic Model for Lung Adenocarcinoma

BACKGROUND

Lung cancer has been a prominent research focus in recent years due to its role in cancer-related fatalities globally, with lung adenocarcinoma (LUAD) being the most prevalent histological form. Nonetheless, no signature of lactate metabolism-related long non-coding RNAs (LMR-lncRNAs) has been developed for patients with LUAD. Accordingly, we aimed to develop a unique LMR-lncRNA signature to determine the prognosis of patients with LUAD.

METHOD

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to derive the lncRNA expression patterns. Identification of LMR-lncRNAs was accomplished by analyzing the co-expression patterns between lncRNAs and LMR genes. Subsequently, the association between lncRNA levels and survival outcomes was determined to develop an effective signature. In the TCGA cohort, Cox regression was enlisted to build an innovative signature consisting of three LMR-lncRNAs, which was validated in the GEO validation cohort. GSEA and immune infiltration analysis were conducted to investigate the functional annotation of the signature and the function of each type of immune cell.

RESULTS

Fourteen differentially expressed LMR-lncRNAs were strongly correlated with the prognosis of patients with LUAD and collectively formed a new LMR-lncRNA signature. The patients could be categorized into two cohorts based on their LMR-lncRNA signatures: a low-risk and high-risk group. The overall survival of patients with LUAD in the high-risk group was considerably lower than those in the low-risk group. Using Cox regression, this signature was shown to have substantial potential as an independent prognostic factor, which was further confirmed in the GEO cohort. Moreover, the signature could anticipate survival across different groups based on stage, age, and gender, among other variables. This signature also correlated with immune cell infiltration (including B cells, neutrophils, CD4⁺ T cells, CD8⁺ T cells, etc.) as well as the immune checkpoint blockade target CTLA-4.

CONCLUSION

We developed and verified a new LMR-lncRNA signature useful for anticipating the survival of patients with LUAD. This signature could give potentially critical insight for immunotherapy interventions in patients with LUAD.

A Novel Assessment Model Based on Molecular Subtypes of Hypoxia-Related LncRNAs for Prognosis of Bladder Cancer

Hypoxia is a common feature in various tumors that regulates aggressiveness. Previous studies have demonstrated that some dysregulated long non-coding RNAs (lncRNAs) are correlated with tumor progression, including bladder cancer (BCa). However, the prognostic effect of hypoxia-related lncRNAs (HRLs) and their clinical relevance, as well as their regulatory effect on the tumor immune microenvironment, are largely unknown in BCa. A co-expression analysis between hypoxia genes and lncRNA expression, which was downloaded from the TCGA database, was performed to identify HRLs. Univariate Cox regression analysis was performed to select the most desirable lncRNAs for molecular subtype, and further LASSO analysis was performed to develop a prognostic model. This molecular subtype based on four HRLs (AC104653, AL136084, AL139393, and LINC00892) showed good performance in the tumor microenvironment and tumor mutation burden. The prognostic risk model suggested better performance in predicting BCa patients' prognosis and obtained a close correlation with clinicopathologic features. Furthermore, four of five first-line clinical chemotherapies showed different sensitivities to this model, and nine immune checkpoints showed different expression in the molecular subtypes or the risk model. In conclusion, this study indicates that this molecular subtype and risk model based on HRLs may be useful in improving the prognostic prediction of BCa patients with different clinical situations and may help to find a useful target for tumor therapy.

Expression changes of serum LINC00941 and LINC00514 in HBV infection-related liver diseases and their potential application values

BACKGROUND

Long non-coding RNAs (LncRNAs) are considered as potential diagnostic markers for a variety of tumors. Here, we aimed to explore the changes of LINC00941 and LINC00514 expression in hepatitis B virus (HBV) infection-related liver disease and evaluate their application value in disease diagnosis.

METHODS

Serum levels of LINC00941 and LINC00514 were detected by qRT-PCR. Potential diagnostic values were evaluated by receiver operating characteristic curve (ROC) analysis.

RESULTS

Serum LINC00941 and LINC00514 levels were elevated in patients with chronic hepatitis B (CHB), liver cirrhosis (LC), and hepatocellular carcinoma (HCC) compared with controls. When distinguishing HCC from controls, serum LINC00941 and LINC00514 had diagnostic parameters of an AUC of 0.919 and 0.808, sensitivity of 85% and 90%, and specificity of 86.67% and 56.67%, which were higher than parameters for alpha fetal protein (AFP) (all p < 0.0001). When distinguishing HCC from LC, CHB, or LC from controls, the combined detection of LINC00941 or LINC00514 can significantly improve the accuracy of AFP test alone (all p < 0.05).

CONCLUSIONS

LINC00941 and LINC00514 were increased in the serum of HBV infection-associated liver diseases and might be independent markers for the detection of liver diseases.