DNA methylation-based lung adenocarcinoma subtypes can predict prognosis, recurrence, and immunotherapeutic implications

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD

Background

Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs.

Methods

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results.

Results

7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis.

Conclusion

The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma

AIMS

To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.

METHODS

We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated.

RESULTS

147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy.

CONCLUSION

The driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Identification of a novel m6A-related lncRNAs signature and immunotherapeutic drug sensitivity in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) ranks as the fourth leading cause for cancer-related deaths worldwide. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are closely related with poor prognosis and immunotherapeutic effect in PDAC. The aim of this study is to construct and validate a m6A-related lncRNAs signature and assess immunotherapeutic drug sensitivity in PDAC.

METHODS

RNA-seq data for 178 cases of PDAC patients and 167 cases of normal pancreatic tissue were obtained from TCGA and GTEx databases, respectively. A set of 21 m6A-related genes were downloaded based on the previous report. Co-expression network was conducted to identify m6A-related lncRNAs in PDAC. Cox analyses and least absolute shrinkage and selection operator (Lasso) regression model were used to construct a risk prognosis model. The relationship between signature genes and immune function was explored by single-sample GSEA (ssGSEA). The tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB) were utilized to evaluate the response to immunotherapy. Furthermore, the expression levels of 4 m6A-related lncRNAs on PDAC cell lines were measured by the quantitative real-time PCR (qPCR). The drug sensitivity between the high- and low-risk groups was validated using PDAC cell lines by Cell-Counting Kit 8 (CCK8).

RESULTS

The risk prognosis model was successfully constructed based on 4 m6A-related lncRNAs, and PDAC patients were divided into the high- and low-risk groups. The overall survival (OS) of the high-risk groups was more unfavorable compared with the low-risk groups. Receiver operating characteristic (ROC) curves demonstrated that the risk prognosis model reasonably predicted the 2-, 3- and 5-year OS of PDAC patients. qPCR analysis confirmed the decreased expression levels of 4 m6A-related lncRNAs in PDAC cells compared to the normal pancreatic cells. Furthermore, CCK8 assay revealed that Phenformin exhibited higher sensitivity in the high-risk groups, while Pyrimethamine exhibited higher sensitivity in the low-risk groups.

CONCLUSION

The prognosis of patients with PDAC were well predicted in the risk prognosis model based on m6A-related lncRNAs, and selected immunotherapy drugs have potential values for the treatment of pancreatic cancer.

Identification and prognostic biomarkers among ZDHHC4/12/18/24, and APT2 in lung adenocarcinoma

S-palmitoylases and S-depalmitoylases are differentially expressed in various cancers and several malignant tumors and show a strong prognostic ability. Notwithstanding, the potential clinical impact of S-palmitoylases and S-depalmitoylases, particularly in the prognosis and progression of lung adenocarcinoma (LUAD), has not been clarified. Expression levels of S-palmitoylases and S-depalmitoylases in LUAD were investigated using TCGA. GEPIA was used to evaluate the mRNA levels of S-palmitoylases and S-depalmitoylases at different pathological stages. Metascape was used to investigate the biological significance of S-palmitoylases and S-depalmitoylases. The Kaplan-Meier plotter was used to analyze the prognostic value of S-palmitoylases and S-depalmitoylases. CBioportal was used to analyze gene alterations in S-palmitoylases and S-depalmitoylases. UALCAN was used to examine DNA promoter methylation levels of S-palmitoylases and S-depalmitoylases. Finally, we investigated the relationship between S-palmitoylases, S-depalmitoylases, and tumor-infiltrating immune cells using TIMER. Correlations with immune checkpoint-related genes were determined using the R packages reshape2, ggpubr, ggplot2, and corrplot. PCR was also performed to assess the degree of ZDHHC4/12/18/24 and APT2 transcript expression in lung adenocarcinoma and adjacent normal lung tissues. HPA was utilized to investigate protein levels of S-palmitoylases and S-depalmitoylases in LUAD and normal lung tissue. Our study found that ZDHHC2/3/4/5/6/7/9/12/13/16/18/20/21/23/24, APT1/2, PPT1, LYPLAL1, ABHD4/10/11/12/13 and ABHD17C mRNA expression was significantly upregulated in LUAD, whereas ZDHHC1/8/11/11B/14/15/17/19/22, ABHD6/16A and ABHD17A mRNA expression was significantly downregulated. The functions of the differentially expressed S-palmitoylases and S-depalmitoylases were mainly associated with protein-cysteine S-palmitoyltransferase and protein-cysteine S-acyltransferase activities. Patients with high expression of ZDHHC4/12/18/24, APT2, ABHD4, ABHD11 and ABHD12 had a shorter overall survival. Infiltration of six immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) was closely associated with the expression of ZDHHC4/12/18/24 and APT2. ZDHHC4/12/18/24 and APT2 positively correlated with the immune checkpoint-related gene CD276. We assessed the mRNA levels of ZDHHC4/12/18/24 and APT2 using qRT-PCR and found increased expression of ZDHHC4/12/18/24 in LUAD compared with healty control lung tissues. ZDHHC4/12/18/24, and APT2 are potential prognostic biomarkers of LUAD. Their expression levels could be related to the tumor microenvironment in LUAD.

m6A-related lncRNAs as potential biomarkers and the lncRNA ELFN1-AS1/miR-182-5p/BCL-2 regulatory axis in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid subtype. However, unsatisfactory survival outcomes remain a major challenge, and the underlying mechanisms are poorly understood. N6-methyladenosine (m6A), the most common internal modification of eukaryotic mRNA, participates in cancer pathogenesis. In this study, m6A-associated long non-coding RNAs (lncRNA) were retrieved from publicly available databases. Univariate, LASSO, and multivariate Cox regression analyses were performed to establish an m6A-associated lncRNA model specific to DLBCL. Kaplan-Meier curves, principal component analysis, functional enrichment analyses and nomographs were used to study the risk model. The underlying clinicopathological characteristics and drug sensitivity predictions against the model were identified. Risk modelling based on the three m6A-associated lncRNAs was an independent prognostic factor. By regrouping patients using our model-based method, we could differentiate patients more accurately for their response to immunotherapy. In addition, prospective compounds that can target DLBCL subtypes have been identified. The m6A-associated lncRNA risk-scoring model developed herein holds implications for DLBCL prognosis and clinical response prediction to immunotherapy. In addition, we used bioinformatic tools to identify and verify the ceRNA of the m6A-associated lncRNA ELFN1-AS1/miR-182-5p/BCL-2 regulatory axis. ELFN1-AS1 was highly expressed in DLBCL and DLBCL cell lines. ELFN1-AS1 inhibition significantly reduced the proliferation of DLBCL cells and promoted apoptosis. ABT-263 inhibits proliferation and promotes apoptosis in DLBCL cells. In vitro and in vivo studies have shown that ABT-263 combined with si-ELFN1-AS1 can inhibit DLBCL progression.

Construction and validation of a novel angiogenesis pattern to predict prognosis and immunotherapy efficacy in colorectal cancer

BACKGROUND

Evidence suggests that the tumor microenvironment (TME) affects the tumor active response to immunotherapy. Tumor angiogenesis is closely related to the TME. Nonetheless, the effects of angiogenesis on the TME of colorectal cancer (CRC) remain unknown.

METHODS

We comprehensively assessed the angiogenesis patterns in CRC based on 36 angiogenesis-related genes (ARGs). Subsequently, we evaluated the prognostic values and therapeutic sensitivities of angiogenesis patterns using multiple methods. We then performed the machine learning algorithm and functional experiments to identify the prognostic key ARGs. Ultimately, the regulation of gut microbiota on the expression of ARGs was further investigated by using whole genome sequencing.

RESULTS

Two angiogenesis clusters were identified and angiogenesis cluster B was characterized by increased stromal and immunity activation with unfavorable odds of survival. Further, an ARG_score including 9 ARGs to predict recurrence-free survival (RFS) was established and its predominant predictive ability was confirmed. The low ARG_score patients were characterized by a high mutation burden, high microsatellite instability, and immune activation with better prognosis. Moreover, patients with high KLK10 expression were associated with a hot tumor immune microenvironment, poorer immune checkpoint blocking treatment, and shorter survival. The in vitro experiments also indicated that Fusobacterium nucleatum (F.n) infection significantly induced KLK10 expression in CRC.

CONCLUSIONS

The quantification of angiogenesis patterns could contribute to predict TME characteristics, prognosis, and individualized immunotherapy strategies. Furthermore, our findings suggest that F.n may influence CRC progression through ARGs, which could serve as a clinical biomarker and therapeutic target for F.n-infected CRC patients.

Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways

Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes.

MNX1 facilitates the malignant progress of lung adenocarcinoma through transcriptionally upregulating CCDC34

Lung adenocarcinoma (LUAD) represents the most prevalent subtype of lung cancer and typically has high incidence and fatality rates. Motor neuron and pancreas homeobox 1 (MNX1) and coiled-coil domain-containing 34 (CCDC34) serve as oncogenes in multiple types of cancer. However, their role in LUAD remains to be elucidated. In the present study, bioinformatics analysis and LUAD cell lines were adopted to examine the expression of MNX1 and CCDC34. The proliferation, migration and invasion abilities of A549 cells were determined using Cell Counting Kit-8, colony formation, wound-healing and Transwell assay, and flow cytometry was conducted to assess cell cycle distribution and apoptosis. The interaction between MNX1 and CCDC34 was verified by luciferase reporter and chromatin immunoprecipitation assays. In addition, an in vivo animal model of LUAD was established for validation. The results demonstrated that both MNX1 and CCDC34 were upregulated in LUAD cell lines. MNX1 knockdown significantly suppressed cell proliferation, migration and invasion, hindered cell cycle progression and promoted cell apoptosis in vitro and inhibited tumor growth in vivo. However, the antitumor effect of MNX1 knockdown was weakened by simultaneous CCDC34 overexpression in vitro. In terms of mechanism, MNX1 was demonstrated to directly bind to the CCDC34 promoter and transcriptionally activate CCDC34 expression. In conclusion, the present study highlighted a critical role of the MNX1/CCDC34 axis in regulating LUAD progression, providing novel therapeutic targets for LUAD treatment.

The influence of pyroptosis-related genes on the development of chronic obstructive pulmonary disease

Increasing evidences have demonstrated that pyroptosis exerts key roles in the occurrence, development of chronic obstructive pulmonary disease. However, the mechanisms of pyroptosis in COPD remain largely unknown. In our research, Statistics were performed using R software and related packages in this study. Series matrix files of small airway epithelium samples were downloaded from the GEO database. Differential expression analysis with FDR < 0.05 was performed to identify COPD-associated pyroptosis-related genes. 8 up-regulated genes (CASP4, CASP5, CHMP7, GZMB, IL1B, AIM2, CASP6, GSDMC) and 1 down-regulated genes (PLCG1) was identified as COPD-associated pyroptosis-related genes. Twenty-six COPD key genes was identified by WGCNA analysis. PPI analysis and gene correlation analysis showed their relationship clearly. KEGG and GO analysis have revealed the main pyroptosis-related mechanism of COPD. The expression of 9 COPD-associated pyroptosis-related genes in different grades was also depicted. The immune environment of COPD was also explored. Furthermore, the relationship of pyroptosis-related genes and the expression of immune cells was also be shown in the end. In the end, we concluded that pyroptosis influences the development of COPD. This study may provide new insight into the novel therapeutic targets for COPD clinical treatment.

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

Prognosis Prediction of Lung Adenocarcinoma Patients Based on Molecular Subgroups of DNA Methylation

Lung adenocarcinoma (LUAD) is a malignant tumor with high mortality. At present, the clinicopathologic feature is the main breakthrough to assess the prognosis of LUAD patients. However, in most cases, the results are less than satisfactory. Cox regression analysis was conducted in this study to obtain methylation sites with significant prognostic relevance based on mRNA expression, DNA methylation data, and clinical data of LUAD from The Cancer Genome Atlas Program database. LUAD patients were grouped into 4 subtypes according to different methylation levels using K-means consensus cluster analysis. By survival analysis, patients were grouped into high-methylation and low-methylation groups. Later, 895 differentially expressed genes (DEGs) were obtained. Eight optimal methylation signature genes associated with prognosis were screened by Cox regression analysis, and a risk assessment model was constructed based on these genes. Samples were then classified into high-risk and low-risk groups depending on the risk assessment model, and prognostic, predictive ability was assessed using survival and receiver operating characteristic (ROC) curves. The results showed that this risk model had a great efficacy in predicting the prognosis of patients, and it was, therefore, able to be an independent prognostic factor. At last, the enrichment analysis demonstrated that the signaling pathways, including cell cycle, homologous recombination, P53 signaling pathway, DNA replication, pentose phosphate pathway, and glycolysis gluconeogenesis were remarkably activated in the high-risk group. In general, we construct an 8-gene model based on DNA methylation molecular subtypes by a series of bioinformatics methods, which can provide new insights for predicting the prognosis of patients with LUAD.

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.

Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.

Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.

Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.

SPOCK2 and SPRED1 function downstream of EZH2 to impede the malignant progression of lung adenocarcinoma in vitro and in vivo

Enhancer of zeste homolog 2 (EZH2) is an important epigenetic regulator, and is associated with the malignant progression of lung cancer. However, the mechanisms of EZH2 on lung adenocarcinoma (LUAD) remain unclear. The relationship between EZH2 and SPOCK2 or SPRED1 was confirmed by dual-luciferase reporter assay. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to examine the expression of SPOCK2 and SPRED1 and their prognostic values of LUAD. The effects of SPOCK2 and SPRED1 on the biological characters of LUAD cells were identified on functional assays in vitro and in vivo. Our results showed that EZH2 suppressed the expression and transcriptional activity of SPOCK2 and SPRED1, and these effects were reversed by the EZH2 inhibitor, Tazemetostat. SPOCK2 and SPRED1 were expressed at low levels in LUAD patients, and a high expression level of SPOCK2 or SPRED1 predicted better survival. Moreover, overexpression of SPOCK2 or SPRED1 could inhibit tumoral proliferation, migration ratio, and invasion activity in vitro as well as retard tumor growth in vivo. However, EZH2 elevation could rescue these impacts and accelerate LUAD progression. Our findings reveal that SPOCK2 and SPRED1 are epigenetically suppressed by EZH2 and may act as novel regulators to inhibit the proliferation, migration, and invasion of LUAD cells.

Identification of prognostic values of the transcription factor-CpG-gene triplets in lung adenocarcinoma: A narrative review

OBJECTIVE

Abnormal DNA methylation can regulate carcinogenesis in lung adenocarcinoma (LUAD), while transcription factors (TFs) mediate methylation in a site-specific manner to affect downstream transcriptional regulation and tumor progression. Therefore, this study aimed to explore the TF-methylation-gene regulatory relationships that influence LUAD prognosis.

METHODS

Differential analyses of methylation sites and genes were generated by integrating transcriptome and methylome profiles from public databases. Through target gene identification, motif enrichment in the promoter region, and TF prediction, TF-methylation and methylation-gene relation pairs were obtained. Then, the prognostic TF-methylation-gene network was constructed using univariate Cox regression analysis. Prognostic models were constructed based on the key regulatory axes. Finally, Kaplan-Meier curves were created to evaluate the model efficacy and the relationship between candidate genes and prognosis.

RESULTS

A total of 1878 differential expressed genes and 1233 differential methylation sites were screened between LUAD and normal samples. Then 10 TFs were predicted to bind 144 enriched motifs. After integrating TF-methylation and methylation-gene relations, a prognostic TF-methylation-gene network containing 4 TFs, 111 methylation sites, and 177 genes was constructed. In this network, ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were selected to construct the prognostic models, which showed robust abilities in predicting 1-, 3-, and 5-year survival probabilities. Finally, ERG and MTURN were downregulated in LUAD samples, whereas FOXM1 and PTPR were upregulated. Their expression levels were related to LUAD prognosis.

CONCLUSION

ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were proposed as potential biomarkers for predicting the prognosis of LUAD.

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Introduction: Molecular chaperones and long non-coding RNAs (lncRNAs) have been confirmed to be closely related to the occurrence and development of tumors, especially lung cancer. Our study aimed to construct a kind of molecular chaperone-related long non-coding RNAs (MCRLncs) marker to accurately predict the prognosis of lung adenocarcinoma (LUAD) patients and find new immunotherapy targets.

Methods: In this study, we acquired molecular chaperone genes from two databases, Genecards and molecular signatures database (MsigDB). And then, we downloaded transcriptome data, clinical data, and mutation information of LUAD patients through the Cancer Genome Atlas (TCGA). MCRLncs were determined by Spearman correlation analysis. We used univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct risk models. Kaplan-meier (KM) analysis was used to understand the difference in survival between high and low-risk groups. Nomogram, calibration curve, concordance index (C-index) curve, and receiver operating characteristic (ROC) curve were used to evaluate the accuracy of the risk model prediction. In addition, we used gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses to explore the potential biological functions of MCRLncs. Immune microenvironmental landscapes were constructed by using single-sample gene set enrichment analysis (ssGSEA), tumor immune dysfunction and exclusion (TIDE) algorithm, “pRRophetic” R package, and “IMvigor210” dataset. The stem cell index based on mRNAsi expression was used to further evaluate the patient’s prognosis.

Results: Sixteen MCRLncs were identified as independent prognostic indicators in patients with LUAD. Patients in the high-risk group had significantly worse overall survival (OS). ROC curve suggested that the prognostic features of MCRLncs had a good predictive ability for OS. Immune system activation was more pronounced in the high-risk group. Prognostic features of the high-risk group were strongly associated with exclusion and cancer-associated fibroblasts (CAF). According to this prognostic model, a total of 15 potential chemotherapeutic agents were screened for the treatment of LUAD. Immunotherapy analysis showed that the selected chemotherapeutic drugs had potential application value. Stem cell index mRNAsi correlates with prognosis in patients with LUAD.

Conclusion: Our study established a kind of novel MCRLncs marker that can effectively predict OS in LUAD patients and provided a new model for the application of immunotherapy in clinical practice.

Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis

Background

As a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.

Methods

We obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.

Results

We identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.

Conclusion

In summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.

Screening and validation of platelet activation-related lncRNAs as potential biomarkers for prognosis and immunotherapy in gastric cancer patients

Background: Platelets (PLT) have a significant effect in promoting cancer progression and hematogenous metastasis. However, the effect of platelet activation-related lncRNAs (PLT-related lncRNAs) in gastric cancer (GC) is still poorly understood. In this study, we screened and validated PLT-related lncRNAs as potential biomarkers for prognosis and immunotherapy in GC patients.

Methods: We obtained relevant datasets from the Cancer Genome Atlas (TCGA) and Gene Ontology (GO) Resource Database. Pearson correlation analysis was used to identify PLT-related lncRNAs. By using the univariate, least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we constructed the PLT-related lncRNAs model. Kaplan-Meier survival analysis, univariate, multivariate Cox regression analysis, and nomogram were used to verify the model. The Gene Set Enrichment Analysis (GSEA), drug screening, tumor immune microenvironment analysis, epithelial-mesenchymal transition (EMT), and DNA methylation regulators correlation analysis were performed in the high- and low-risk groups. Patients were regrouped based on the risk model, and candidate compounds and immunotherapeutic responses aimed at GC subgroups were also identified. The expression of seven PLT-related lncRNAs was validated in clinical medical samples using quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

Results: In this study, a risk prediction model was established using seven PLT-related lncRNAs -(AL355574.1, LINC01697, AC002401.4, AC129507.1, AL513123.1, LINC01094, and AL356417.2), whose expression were validated in GC patients. Kaplan-Meier survival analysis, the receiver operating characteristic (ROC) curve analysis, univariate, multivariate Cox regression analysis verified the accuracy of the model. We screened multiple targeted drugs for the high-risk patients. Patients in the high-risk group had a poorer prognosis since low infiltration of immune killer cells, activation of immunosuppressive pathways, and poor response to immunotherapy. In addition, we revealed a close relationship between risk scores and EMT and DNA methylation regulators. The nomogram based on risk score suggested a good ability to predict prognosis and high clinical benefits.

Conclusion: Our findings provide new insights into how PLT-related lncRNAs biomarkers affect prognosis and immunotherapy. Also, these lncRNAs may become potential biomarkers and therapeutic targets for GC patients.

COVID-19–associated lncRNAs as predictors of survival in uterine corpus endometrial carcinoma: A prognostic model

Background: Patients with uterine corpus endometrial carcinoma (UCEC) may be susceptible to the coronavirus disease-2019 (COVID-19). Long non–coding RNAs take on a critical significance in UCEC occurrence, development, and prognosis. Accordingly, this study aimed to develop a novel model related to COVID-19–related lncRNAs for optimizing the prognosis of endometrial carcinoma.

Methods: The samples of endometrial carcinoma patients and the relevant clinical data were acquired in the Carcinoma Genome Atlas (TCGA) database. COVID-19–related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed to establish a COVID-19–related lncRNA risk model. Kaplan–Meier analysis, principal component analysis (PCA), and functional enrichment annotation were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed.

Results: The risk model comprising 10 COVID-19–associated lncRNAs was identified as a predictive ability for overall survival (OS) in UCEC patients. PCA analysis confirmed a reliable clustering ability of the risk model. By regrouping the patients with this model, different clinic-pathological characteristics, immunotherapeutic response, and chemotherapeutics sensitivity were also observed in different groups.

Conclusion: This risk model was developed based on COVID-19–associated lncRNAs which would be conducive to the precise treatment of patients with UCEC.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

Construction of a Prognostic Model for KIRC and Identification of Drugs Sensitive to Therapies - A Comprehensive Biological Analysis Based on m6A-Related LncRNAs

As one of the common malignancies in the urinary system, kidney cancer has been receiving explorations with respect to its pathogenesis, treatment and prognosis due to its high morbidity, high mortality and low drug efficiency. Such epigenetic modifications for RNA molecules as N6-methyladenosine (m6A) usher in another perspective for the research on tumor mechanisms, and an increasing number of biological processes and prognostic markers have been revealed. In this study, the transcriptome data, clinical data and mutation spectrum data of KIRC in the TCGA database were adopted to construct an m6A-related lncRNA prognostic model. Besides, the predictive ability of this model for clinical prognosis was evaluated, and some compounds sensitive to therapies for KIRC were screened. The findings of this study demonstrate that this effective and stable model has certain clinical application value.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

The Expression of Pyroptosis-Related Gene May Influence the Occurrence, Development, and Prognosis of Uterine Corpus Endometrial Carcinoma

BACKGROUND

Increasing evidence has demonstrated that pyroptosis exerts key roles in the occurrence, development, and prognosis of uterine corpus endometrial carcinoma (UCEC). However, the mechanism of pyroptosis and its predictive value for prognosis remain largely unknown.

METHODS

UCEC data were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes in UCEC vs. normal cases were selected to perform a weighted correlation network analysis (WGCNA). Forty-two UCEC-associated pyroptosis-related genes were identified via applying differential expression analysis. Protein-protein interaction (PPI) and gene correlation analyses were applied to explore the relationship between 21 UCEC key genes and 42 UCEC-associated pyroptosis-related genes. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also calculated. The immune environment of UCEC was evaluated. Furthermore, pyroptosis-related genes were filtered out by the co-expression. Univariate and a least absolute shrinkage and selection operator (LASSO) Cox analyses were implemented to yield a pyroptosis-related gene model. We also performed consensus classification to regroup UCEC samples into two clusters. A clinically relevant heatmap and survival analysis curve were implemented to explore the clinicopathological features and relationship between two clusters. Furthermore, a Kaplan-Meier survival analysis was implemented to analyze the risk model.

RESULTS

Twenty-one UCEC key genes and 42 UCEC-associated pyroptosis-related genes were identified. The PPI and gene correlation analysis showed a clear relationship. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also depicted. A risk model based on pyroptosis-related genes was then developed to forecast overall survival among UCEC patients. Finally, Cox regression analysis verified this model as an independent risk factor for UCEC patients.

CONCLUSIONS

The expression of pyroptosis-related gene may influence UCEC occurrence, development, and prognosis.

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

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

DNA Methylation Regulator-Meditated Modification Patterns Define the Distinct Tumor Microenvironment in Lung Adenocarcinoma

Background

Epigenetic changes of lung adenocarcinoma (LUAD) have been reported to be a relevant factor in tumorigenesis and cancer progression. However, the molecular mechanisms responsible for DNA methylation patterns in the tumor immune-infiltrating microenvironment and in cancer immunotherapy remain unclear.

Methods

We conducted a global analysis of the DNA methylation modification pattern (DMP) and immune cell-infiltrating characteristics of LUAD patients based on 21 DNA methylation regulators. A DNA methylation score (DMS) system was constructed to quantify the DMP model in each patient and estimate their potential benefit from immunotherapy.

Results

Two DNA methylation modification patterns able to distinctly characterize the immune microenvironment characterization were identified among 513 LUAD samples. A lower DMS, characterized by increased CTLA-4/PD-1/L1 gene expression, greater methylation modifications, and tumor mutation burden, characterized a noninflamed phenotype with worse survival. A higher DMS, characterized by decreased methylation modification, a greater stromal-relevant response, and immune hyperactivation, characterized an inflamed phenotype with better prognosis. Moreover, a lower DMS indicated an increased mutation load and exhibited a poor immunotherapeutic response in the anti-CTLA-4/PD-1/PD-L1 cohort.

Conclusion

Evaluating the DNA methylation modification pattern of LUAD patients could enhance our understanding of the features of tumor microenvironment characterization and may promote more favorable immunotherapy strategies.

Prognostic Analysis of Lung Adenocarcinoma Based on DNA Methylation Regulatory Factor Clustering

There is a known link between DNA methylation and cancer immunity/immunotherapy; however, the effect of DNA methylation on immunotherapy in lung adenocarcinoma (LUAD) remains to be elucidated. In the current study, we aimed to screen key markers for prognostic analysis of LUAD based on DNA methylation regulatory factor clustering. We classified LUAD using the NMF clustering method, and as a result, we obtained 20 DNA methylation regulatory genes. These 20 regulatory genes were used to determine the pattern of DNA methylation regulation, and patients were grouped for further analysis. The risk score model was analyzed in the TCGA dataset and an external validation set, and the correlation between the risk score and DNA methylation regulatory gene expression was explored. We analyzed the correlation between the prognostic model and immune infiltration and checkpoints. Finally, we analyzed the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functions of the prognosis model and established the nomogram model and decision tree model. The survival analyses of ClusterA and ClusterB were significantly different. Survival analysis showed that patients with a high risk score had a poor prognosis. Survival models (tobacco, T, N, M, stage, sex, age, status, and risk score) were abnormally correlated with T cells and macrophages. The higher the risk score associated with smoking was and the higher the stage was, the more severe the LUAD and the more maladjusted the immune system were. Immune infiltration and abnormal expression of immune checkpoint genes in the prognostic model of LUAD were associated with the risk score. The prognostic models were mainly enriched in the cell cycle and DNA replication. Characterization of DNA methylation regulatory patterns is helpful to improve our understanding of the immune microenvironment in LUAD and to guide the development of a more personalized immunotherapy strategy in the future.

m6A-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with LUAD

Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer worldwide. However, the survival rate of LUAD patients remains low. N6-methyladenosine (m⁶A) and long noncoding RNAs (lncRNAs) play vital roles in the prognostic value and the immunotherapeutic response of LUAD. Thus, discerning lncRNAs associated with m⁶A in LUAD patients is critical. In this study, m⁶A-related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were conducted to construct an m⁶A-related lncRNA model. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed. The risk model comprising 12 m⁶A-related lncRNAs was identified as an independent predictor of prognoses. By regrouping the patients with this model, we can distinguish between them more effectively in terms of the immunotherapeutic response. Finally, candidate compounds aimed at LUAD subtype differentiation were identified. This risk model based on the m⁶A-based lncRNAs may be promising for the clinical prediction of prognoses and immunotherapeutic responses in LUAD patients.

Mechanisms of Progression and Heterogeneity in Multiple Nodules of Lung Adenocarcinoma

Lung cancer remains the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) is thought to be caused by precursor lesions of atypical adenoma-like hyperplasia and may have extensive in situ growth before infiltration. To explore the relevant factors in heterogeneity and evolution of lung adenocarcinoma subtypes, the authors perform single-cell RNA sequencing (scRNA-seq) on tumor and normal tissue from five multiple nodules' LUAD patients and conduct a thorough gene expression profiling of cancer cells and cells in their microenvironment at single-cell level. This study gives a deep understanding of heterogeneity and evolution in early glandular neoplasia of the lung. This dataset leads to discovery of the changes in the immune microenvironment during the development of LUAD, and the development process from adenocarcinoma in situ (AIS) to invasive adenocarcinoma (IAC). This work sheds light on the direction of early tumor development and whether they are homologous.

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Background

CD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG).

Methods

The prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways.

Results

Pan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG.

Conclusions

Integrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.