An integrated analysis of the competing endogenous RNA network associated of prognosis of stage I lung adenocarcinoma

Abstract Background: Accumulating evidence indicates that long non-coding RNAs (lncRNAs) are involving in the tumorigenesis and metastasis of lung cancer. The aim of the study is to systematically characterize the lncRNA-associated competing endogenous RNA (ceRNA) network and identify key lncRNAs in the development of stage I lung adenocarcinoma (LUAD). Methods: Totally, 1,955 DEmRNAs, 165 DEmiRNAs and 1,107 DElncRNAs were obtained in 10 paired normal and LUAD tissues. And a total of 8,912 paired lncRNA-miRNA-mRNA network was constructed. Using the Cancer Genome Atlas (TCGA) dataset, the module of ME turquoise was revealed to be most relevant to the progression of LUAD though Weighted Gene Co-expression Network Analysis (WGCNA). Results: Of the lncRNAs identified, LINC00639, RP4-676L2.1 and FENDRR were in ceRNA network established by our RNA-sequencing dataset. Using univariate Cox regression analysis, FENDRR was a risk factor of progression free survival (PFS) of stage I LUAD patients (HRs=1.69, 95%CI 1.07-2.68, P< .050). Subsequently, differential expression of FENDRR in paired normal and LUAD tissues was detected significant by real-time quantitative (qRT-PCR) (P <0.001). Conclusions: This study, for the first time, deciphered the regulatory role of FENDRR/miR-6815-5p axis in the progression of early-stage LUAD, which is needed to be established in vitro and in vivo.

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Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2021/4093426 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Lixian Chen ◽

Zhonglu Ren ◽

Yongming Cai

Keyword(s):

Prognostic Factors ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

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BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2022/3921095 ◽

2022 ◽

Vol 2022 ◽

pp. 1-16

Author(s):

Jin Zhou ◽

Zheming Liu ◽

Huibo Zhang ◽

Tianyu Lei ◽

Jiahui Liu ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Early Stage ◽

External Validation ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

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Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.602542 ◽

2020 ◽

Vol 11 ◽

Author(s):

Jian-Rong Sun ◽

Chen-Fan Kong ◽

Kun-Min Xiao ◽

Jia-Lu Yang ◽

Xiang-Ke Qu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Tnm Stage ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Risk Patients

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

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Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets

PeerJ ◽

10.7717/peerj.8961 ◽

2020 ◽

Vol 8 ◽

pp. e8961 ◽

Cited By ~ 6

Author(s):

Meijing Wu ◽

Xiaobin Shang ◽

Yue Sun ◽

Jing Wu ◽

Guoyan Liu

Keyword(s):

Ovarian Cancer ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Lymphocyte Infiltration ◽

Gene Expressions ◽

Gene Markers ◽

Cox Regression Analysis ◽

Cerna Network

Background Abnormal expression of long non-coding RNAs (lncRNA) play a significant role in the incidence and progression of high-grade serous ovarian cancer (HGSOC), which is a leading cause of mortality among gynecologic malignant tumor patients. In this study, our aim is to identify lncRNA-associated competing endogenous RNA (ceRNA ) axes that could define more reliable prognostic parameters of HGSOC, and to investigate the lncRNAs’ potential mechanism of in lymphocyte infiltration. Methods The RNA-seq and miRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database; while for obtaining the differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs), we used edgeR, limma and DESeq2. After validating the RNA, miRNA and gene expressions, using integrated three RNA expression profiles (GSE18520, GSE27651, GSE54388) and miRNA profile (GSE47841) from the Gene Expression Omnibus (GEO) database, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses through ClusterProfiler. The prognostic value of these genes was determined with Kaplan–Meier survival analysis and Cox regression analysis. The ceRNA network was constructed using Cytoscape. The correlation between lncRNAs in ceRNA network and immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource (TIMER), and gene markers of tumor-infiltrating immune cells were identified using Spearman’s correlation after removing the influence of tumor purity. Results A total of 33 DELs (25 upregulated and eight downregulated), 134 DEMs (76 upregulated and 58 downregulated), and 1,612 DEGs (949 upregulated and 663 downregulated) were detected that could be positively correlated with overall survival (OS) of HGSOC. With the 1,612 analyzed genes, we constructed a ceRNA network, which indicated a pre-dominant involvement of the immune-related pathways. Furthermore, our data revealed that LINC00665 influenced the infiltration level of macrophages and dendritic cells (DCs). On the other hand, FTX and LINC00665, which may play their possible roles through the ceRNA axis, demonstrated a potential to inhibit Tregs and prevent T-cell exhaustion. Conclusion We defined several prognostic biomarkers for the incidence and progression of HGSOC and constructed a network for ceRNA axes; among which three were indicated to have a positive correlation with lymphocyte infiltration, namely: FTX-hsa-miR-150-5p-STK11, LINC00665-hsa-miR449b-5p-VAV3 and LINC00665-hsa-miR449b-5p-RRAGD.

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Reverse engineering a predictive signature characterized by proliferation, DNA damage, and immune escape from stage I lung adenocarcinoma recurrence

Acta Biochimica et Biophysica Sinica ◽

10.1093/abbs/gmaa036 ◽

2020 ◽

Vol 52 (6) ◽

pp. 638-653

Author(s):

Jiannan Yao ◽

Xinying Xue ◽

Dongfeng Qu ◽

C Benedikt Westphalen ◽

Yang Ge ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Immune Escape ◽

Early Stage ◽

Gene Signature ◽

Response To Therapy ◽

Stage I ◽

Cox Regression Analysis ◽

Patients At Risk

Abstract Identifying early-stage cancer patients at risk for progression is a major goal of biomarker research. This report describes a novel 19-gene signature (19-GCS) that predicts stage I lung adenocarcinoma (LAC) recurrence and response to therapy and performs comparably in pancreatic adenocarcinoma (PAC), which shares LAC molecular traits. Kaplan–Meier, Cox regression, and cross-validation analyses were used to build the signature from training, test, and validation sets comprising 831 stage I LAC transcriptomes from multiple independent data sets. A statistical analysis was performed using the R language. Pathway and gene set enrichment were used to identify underlying mechanisms. 19-GCS strongly predicts overall survival and recurrence-free survival in stage I LAC (P=0.002 and P<0.001, respectively) and in stage I–II PAC (P<0.0001 and P<0.0005, respectively). A multivariate cox regression analysis demonstrated the independence of 19-GCS from significant clinical factors. Pathway analyses revealed that 19-GCS high-risk LAC and PAC tumors are characterized by increased proliferation, enhanced stemness, DNA repair deficiency, and compromised MHC class I and II antigen presentation along with decreased immune infiltration. Importantly, high-risk LAC patients do not appear to benefit from adjuvant cisplatin while PAC patients derive additional benefit from FOLFIRINOX compared with gemcitabine-based regimens. When validated prospectively, this proof-of-concept biomarker may contribute to tailoring treatment, recurrence reduction, and survival improvements in early-stage lung and pancreatic cancers.

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Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in CESC

10.21203/rs.3.rs-352332/v1 ◽

2021 ◽

Author(s):

Lang Li ◽

Qiusheng Guo ◽

Gaochen Lan ◽

Fei Liu ◽

Wenwu Wang ◽

...

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Rank Test ◽

Prognostic Signature ◽

Competing Endogenous Rna ◽

Genetic Characteristics ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC.Methods: The RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs, lncRNAs and miRNAs were identified by edgeR package. Constructing prognostic model used the differentially expressed RNAs. The Kaplan-Meier method and log-rank test were performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed by multiMiR package and miRcode database. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) were used to identify the functional role of the ceRNA network in the prognosis of CESC.Results: Differentially expressed 298 mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. The prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was constructed. The prognostic ability was 0.726 for this model. Patients in the high-risk group were significantly associated with worse OS. The KEGG pathways were significantly enriched in the TGF-β and Cell adhesion molecules signaling pathways.Conclusion: This study identified several potential prognostic biomarkers to construct a multi-mRNA-based prognostic model for CESC.

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GRP75-mediated upregulation of HMGA1 stimulates stage I lung adenocarcinoma progression by activating the JNK/c-JUN signaling

10.21203/rs.3.rs-246900/v1 ◽

2021 ◽

Author(s):

Guo-Bing Qiao ◽

Ren-Tao Wang ◽

Shu-Nan Wang ◽

Shao-Lin Tao ◽

Qun-You Tan ◽

...

Keyword(s):

Cell Growth ◽

Lung Adenocarcinoma ◽

Early Stage ◽

Stage I ◽

The Cancer Genome Atlas ◽

Sequencing Analysis ◽

Expression Levels ◽

Hmga1 Expression

Abstract Background Recurrence is a major challenge in early-stage lung adenocarcinoma (LUAD) treatment. But the mechanism is largely unknown. Here, we investigated the role and mechanism of high-mobility group AT-hook 1 (HMGA1) and glucose-regulated protein 75-kDa (GRP75) in stage I LUAD and evaluated their potential as biomarkers for predicting the recurrence and prognosis of stage I LUAD. Methods The Cancer Genome Atlas (TCGA) dataset was used to investigate the clinical significance of HMGA1 and GRP75 in early-stage LUAD. Western blotting and immunohistochemistry were used to measure protein expression levels. The biological functions of HMGA1 and GRP75 in LUAD were investigated both in vitro and in vivo through overexpression and knockdown experiments. The interaction and regulation between HMGA1 and GRP75 were evaluated with coimmunoprecipitation and ubiquitination assays. The downstream signaling pathway of the GRP75/HMGA1 axis was investigated by mRNA-sequencing analysis. Results Both HMGA1 expression levels and GRP75 expression levels were associated with recurrence in stage I LUAD patients. In particular, HMGA1 had potential as an independent prognostic factor in stage I LUAD patients. Overexpression of GRP75 or HMGA1 significantly stimulated LUAD cell growth and metastasis, while silencing GRP75 or HMGA1 inhibited LUAD cell growth and metastasis in vitro and in vivo. Importantly, GRP75 inhibited ubiquitination-mediated HMGA1 degradation by directly binding to HMGA1, thereby causes HMGA1 upregulation in LUAD. In addition, the GRP75/HMGA1 axis played its role by activating JNK/c-JUN signaling in LUAD. Conclusions The activation of GRP75/HMGA1/JNK/c-JUN signaling is an important mechanism that promotes the progression of stage I LUAD, and a high level of HMGA1 is a novel biomarker for predicting recurrence and a poor prognosis in stage I LUAD patients.

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Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA–miRNA–mRNA networks and Cox regression models

Bioscience Reports ◽

10.1042/bsr20191554 ◽

2020 ◽

Vol 40 (1) ◽

Cited By ~ 3

Author(s):

Yan Yao ◽

Tingting Zhang ◽

Lingyu Qi ◽

Ruijuan Liu ◽

Gongxi Liu ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Prognostic Indicators ◽

Cox Regression Analysis ◽

Cerna Network ◽

A Genome

Abstract Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide, and its underlying mechanism remains unclear. Accumulating evidence has highlighted that long non-coding RNA (lncRNA) acts as competitive endogenous RNA (ceRNA) and plays an important role in the occurrence and development of LUAD. Here, we comprehensively analyzed and provided an overview of the lncRNAs, miRNAs, and mRNAs associated with LUAD from The Cancer Genome Atlas (TCGA) database. Then, differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA) were used to construct a lncRNA–miRNA–mRNA regulatory network according to interaction information from miRcode, TargetScan, miRTarBase, and miRDB. Finally, the RNAs of the network were analyzed for survival and submitted for Cox regression analysis to construct prognostic indicators. A total of 1123 DElncRNAs, 95 DEmiRNAs, and 2296 DEmRNAs were identified (|log2FoldChange| (FC) > 2 and false discovery rate (FDR) or adjusted P value < 0.01). The ceRNA network was established based on this and included 102 lncRNAs, 19 miRNAs, and 33 mRNAs. The DEmRNAs in the ceRNA network were found to be enriched in various cancer-related biological processes and pathways. We detected 22 lncRNAs, 12 mRNAs, and 1 miRNA in the ceRNA network that were significantly associated with the overall survival of patients with LUAD (P < 0.05). We established three prognostic prediction models and calculated the area under the 1,3,5-year curve (AUC) values of lncRNA, mRNA, and miRNA, respectively. Among them, the prognostic index (PI) of lncRNA showed good predictive ability which was 0.737, 0.702 and 0.671 respectively, and eight lncRNAs can be used as candidate prognostic biomarkers for LUAD. In conclusion, our study provides a new perspective on the prognosis and diagnosis of LUAD on a genome-wide basis, and develops independent prognostic biomarkers for LUAD.

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Identification of a novel lncRNA‑miRNA‑mRNA competing endogenous RNA network associated with prognosis of breast cancer

10.21203/rs.2.24821/v1 ◽

2020 ◽

Author(s):

Xiaohui Wan ◽

Shuhong Hao ◽

Chunmei Hu ◽

Rongfeng Qu

Keyword(s):

Breast Cancer ◽

Cancer Biology ◽

Cox Regression ◽

Malignant Tumors ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Target Mrna ◽

Cerna Network

Abstract Background: Breast cancer is one of the most common malignant tumors. Recently, the effects of competing endogenous RNA (ceRNA) on molecular biological mechanism of cancer has aroused great interext. However, research on the pathogenesis and biomarkers of breast cancer is still limited. Thus, this study is aimed to identify the competing endogenous RNA (ceRNA) network related to prognosis of patients with breast cancer. Methods: The RNA SEQ data and corresponding clinical information were downloaded from the Cancer Genome Atlas (TCGA) database, and the difference analysis was performed after data quality control. The similarity between two groups of genes with traits in the network was analyzed by weighted correlation network analysis (WGCNA) . Next, the interaction among lncRNA, miRNA, and mRNA was predicted using miRcode, TargetScan, miRDB, and miRTarBase database, and the lncRNA-miRNA-mRNA ceRNA network was constructed. Finally, the survival model of target mRNA was established by Cox regression analysis. Results: In total 5056 differentially expressed lncRNAs, 712 differentially expressed miRNAs, and 9878 differentially expressed mRNAs were identified. WGCNA predicted that 823 lncRNAs and 1813 mRNAs were closely related to the breast cancer. The lncRNA-miRNA-mRNA ceRNA network involved in breast cancer was constructed with 27 lncRNA, 14 miRNAs and 4 mRNAs. The AUC of four survival models of target mRNA (ZC3H12B + HRH1 + TMEM132C + PAG1) was 0.609, which suggested the sensitivity and specificity of prognosis prediction of breast cancer. Conclusion: This study provides insight into the ceRNA network involved in breast cancer biology, which significantly associated with gene regulation and prognosis of breast cancer.

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Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-90755-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wenjie Chen ◽

Wen Li ◽

Zhenkun Liu ◽

Guangzhi Ma ◽

Yunfu Deng ◽

...

Keyword(s):

Prognostic Factors ◽

Mast Cells ◽

Correlation Analysis ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Cerna Network ◽

Monocytes And Macrophages ◽

Competitive Endogenous Rna

AbstractTo identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

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