Establishment of a Prognostic Model Using Immune-Related Genes in Patients With Hepatocellular Carcinoma

A novel signature incorporating lipid metabolism- and immune-related genes to predict the prognosis and immune landscape in hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients.

Methods

We used the "DEseq2" R package and the "Venn" R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature's prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients' sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR).

Results

We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend.

Conclusions

We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.

A Ribosome-Related Prognostic Signature of Breast Cancer Subtypes Based on Changes in Breast Cancer Patients’ Immunological Activity

Background and Objectives. The prognostic role of adjacent nontumor tissue in patients with breast cancer (BC) is still unclear. The activity changes in immunologic and hallmark gene sets in normal tissues adjacent to BC may play a crucial role in predicting the prognosis of BC patients. The aim of this study was to identify BC subtypes and ribosome-associated prognostic genes based on activity changes of immunologic and hallmark gene sets in tumor and adjacent nontumor tissues to improve patient prognosis. Materials and Methods. Gene set variation analysis (GSVA) was applied to assess immunoreactivity changes in the overall sample and three immune-related BC subtypes were identified by non-negative matrix factorization (NMF). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses were after determining the prognostic gene set using the least absolute shrinkage and selection operator (LASSO) method. Ribosome-related genes were identified by PPI (protein-protein interaction) analysis, and finally a prognostic risk model was constructed based on the expression of five ribosomal genes (RPS18, RPL11, PRLP1, RPL27A, and RPL38). Results. A comprehensive analysis of immune and marker genomic activity changes in normal breast tissue and BC tissue identified three immune-related BC subtypes. BC subtype 1 has the best prognosis, and subtype 3 has the worst overall survival rate. We identified a prognostic gene set in nontumor tissue by the least absolute shrinkage and selection operator (LASSO) method. We found that the results of both KEGG and GO analyses were indistinguishable from those of ribosome-associated genes. Finally, we determined that genes associated with ribosomes exhibit potential as a reliable predictor of overall survival in breast cancer patients. Conclusions. Our research provides an important guidance for the treatment of BC. After a mastectomy, the changes in gene set activity of both BC tissues and the nontumor tissues adjacent to it should be thoroughly evaluated, with special attention to changes in ribosome-related genes in the nontumor tissues.

A novel risk score based on immune-related genes for hepatocellular carcinoma as a reliable prognostic biomarker and correlated with immune infiltration

BACKGROUND

Immunological-related genes (IRGs) play a critical role in the immune microenvironment of tumors. Our study aimed to develop an IRG-based survival prediction model for hepatocellular carcinoma (HCC) patients and to investigate the impact of IRGs on the immune microenvironment.

METHODS

Differentially expressed IRGs were obtained from The Genomic Data Commons Data Portal (TCGA) and the immunology database and analysis portal (ImmPort). The univariate Cox regression was used to identify the IRGs linked to overall survival (OS), and a Lasso-regularized Cox proportional hazard model was constructed. The International Cancer Genome Consortium (ICGC) database was used to verify the prediction model. ESTIMATE and CIBERSORT were used to estimate immune cell infiltration in the tumor immune microenvironment (TIME). RNA sequencing was performed on HCC tissue specimens to confirm mRNA expression.

RESULTS

A total of 401 differentially expressed IRGs were identified, and 63 IRGs were found related to OS on the 237 up-regulated IRGs by univariate Cox regression analyses. Finally, five IRGs were selected by the LASSO Cox model, including SPP1, BIRC5, STC2, GLP1R, and RAET1E. This prognostic model demonstrated satisfactory predictive value in the ICGC dataset. The risk score was an independent predictive predictor for OS in HCC patients. Immune-related analysis showed that the immune infiltration level in the high-risk group was higher, suggesting that the 5-IRG signature may play an important role in mediating immune escape and immune resistance in the TIME of HCC. Finally, we confirmed the 5-IRG signature is highly expressed in 65 HCC patients with good predictive power.

CONCLUSION

We established and verified a new prognosis model for HCC patients based on survival-related IRGs, and the signature could provide new insights into the prognosis of HCC.

Prognostic models for outcome prediction in patients with advanced hepatocellular carcinoma treated by systemic therapy: a systematic review and critical appraisal

Objective

To describe and analyze the predictive models of the prognosis of patients with hepatocellular carcinoma (HCC) undergoing systemic treatment.

Design

Systematic review.

Data sources

PubMed and Embase until December 2020 and manually searched references from eligible articles.

Eligibility criteria for study selection

The development, validation, or updating of prognostic models of patients with HCC after systemic treatment.

Results

The systematic search yielded 42 eligible articles: 28 articles described the development of 28 prognostic models of patients with HCC treated with systemic therapy, and 14 articles described the external validation of 32 existing prognostic models of patients with HCC undergoing systemic treatment. Among the 28 prognostic models, six were developed based on genes, of which five were expressed in full equations; the other 22 prognostic models were developed based on common clinical factors. Of the 28 prognostic models, 11 were validated both internally and externally, nine were validated only internally, two were validated only externally, and the remaining six models did not undergo any type of validation. Among the 28 prognostic models, the most common systemic treatment was sorafenib (n = 19); the most prevalent endpoint was overall survival (n = 28); and the most commonly used predictors were alpha-fetoprotein (n = 15), bilirubin (n = 8), albumin (n = 8), Child–Pugh score (n = 8), extrahepatic metastasis (n = 7), and tumor size (n = 7). Further, among 32 externally validated prognostic models, 12 were externally validated > 3 times.

Conclusions

This study describes and analyzes the prognostic models developed and validated for patients with HCC who have undergone systemic treatment. The results show that there are some methodological flaws in the model development process, and that external validation is rarely performed. Future research should focus on validating and updating existing models, and evaluating the effects of these models in clinical practice.

Systematic review registration

PROSPERO CRD42020200187.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09841-5.

Development and validation of a prognostic model for esophageal carcinoma based on immune microenvironment using system bioinformatics

Esophageal cancer (EC) is an aggressive malignancy that accounts for numerous cancer-related deaths worldwide. The multimodal combination therapy approach can be potentially used to treat EC effectively. However, distinct biomarker of significant specificity are still needed to develop individualized treatment strategies and provide accurate prognostic predictions. Therefore, we aimed to investigate the associated genes subtypes identified were, IFN-γDominant, Inflammatory, Lymphocyte Depleted, etc. and construct a risk model based on these genes to predict the overall survival (OS) of patients suffering from EC. Three immune subtypes were defined in the Cancer Genome Atlas (TCGA) cohort with different tumor microenvironment (TME) and clinical outcomes based on radio-differentiated immune genes. Subsequently, a risk model of immune characteristics included the immune cell infiltration levels and pathway activity was developed based on the genomic changes between the subtypes. In the TCGA dataset, as well as in subgroup analysis with different stages, gender, age, and pathological type, a high-risk score was identified as an adverse factor for OS using the method of the univariate Cox regression analysis and tROC analysis. Furthermore, it was observed that the high-risk group was characterized by depleted immunophenotype, active cell metabolism, and a high tumor mutation burden (TMB). The low-risk group was characterized by high TME abundance and active immune function. Differences in the biological genotypes may account for the differences in the prognosis and treatment response. Extensive research was carried out, and the results revealed that the low-risk group exhibited a significant level of therapeutic advantage in the field of immunotherapy. A risk model was developed based on the immune characteristics. It can be used to optimize risk stratification for patients suffering from EC. The results can potentially help provide new perspectives on treatment methods.

Construction of AP003469.4-miRNAs-mRNAs ceRNA network to reveal potential biomarkers for hepatocellular carcinoma

Studies have reported that the competing endogenous RNA (ceRNA) networks are related to disease progression and prognosis in patients with hepatocellular carcinoma (HCC). The roles and mechanisms of long-chain non-coding RNA AP003469.4 in HCC have remained unclear. Here, we explored the roles of AP003469.4 in HCC progression using bioinformatics, CCK-8, Transwell assay, etc. AP003469.4 targets miRNAs and these target genes were predicted by the LncBase Predicted v.2, miRDB, miRTarBase, and TargetScan databases. Then, AP003469.4-associated ceRNA network was constructed. Biological functions and mechanisms of differentially expressed genes in the ceRNA network were explored using GO and KEGG. Survival analysis and Cox regression analysis were used to screen prognostic genes and construct a prognostic risk model. The results revealed that AP003469.4, with the area under the curve of 0.9048, was highly expressed in HCC tissues. Increased expression of AP003469.4 was an independent risk factor for the dismal prognosis of HCC patients and was associated with the short overall and disease-free survival. Downregulation of AP003469.4 expression inhibited cell proliferation, cycle transition, invasion, and migration, and promoted cell apoptosis. There were 489 differentially expressed target genes in the ceRNA network, which were involved in several pathways, such as the MAPK signaling pathway, cell cycle, and p53 signaling pathway. The risk model was based on the DTYMK, ZFC3H1, CBX2, PKM, TTC26, ATG10, TAGLN2, CD3EAP, SHISA9, SLC1A5, KPNA2, SCML2, E2F7, and SMARCD1, which were the independent risk factors for poor prognosis of HCC patients. In general, interference with AP003469.4 expression might delay the progression of HCC. AP003469.4 related network could help to identify the hub target molecules in HCC progression, which might be candidate biomarkers for evaluating the prognosis of HCC patients.

Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy

Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown.

Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort.

Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis.

Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.

Systematic Construction and Validation of a Prognostic Model for Hepatocellular Carcinoma Based on Immune-Related Genes

Hepatocellular carcinoma (HCC), a highly aggressive tumor, has high incidence and mortality rates. Recently, immunotherapies have been shown to be a promising treatment in HCC. The results of either the CheckMate-040 or IMbrave 150 trials demonstrate the importance of immunotherapy in the systemic treatment of liver cancer. Thus, in this study, we tried to establish a reliable prognostic model for liver cancer based on immune-related genes (IRGs) and to provide a new insight for immunotherapy of HCC. In this study, we used four datasets that incorporated 851 HCC samples, including 340 samples with complete clinical information from the cancer genome atlas (TCGA) database, to establish an effective model for predicting the prognosis of HCC patients based on the differential expression of IRGs and validated the prognostic model using the data from International Cancer Genome Consortium (ICGC). The top 6 characteristic IRGs identified by protein-protein interaction (PPI) network analysis, MMP9, FOS, CAT, ESR1, ANGPTL3, and KLKB1, were selected for further study. In addition, we assessed the correlations of the six characteristic IRGs with the tumor immune microenvironment, clinical stage, and sensitivity to anti-cancer drugs. We also explored whether the differential expression of the characteristic IRGs was specific to HCC or present in pan-cancer. The expression levels of the six characteristic IRGs were significantly different between most tumor tissues and adjacent normal tissues. In addition, these characteristic IRGs showed a strong association with immune cell infiltration in HCC patients. We found that MMP9 and ESR1 were independent prognostic factors for HCC, while CAT, ESR1, and KLKB1 were associated with the clinical stage. We collected HCC paraffin sections from 24 patients from Xijing hospital to identify the differential expression of the five genes (MMP9, ESR1, CAT, FOS, and KLKB1). Finally, the results of decision curve analysis (DCA) and nomogram revealed that our models provided a prognostic benefit for most HCC patients and the predicted overall survival (OS) was consistent with the actual OS. In conclusion, we systemically constructed a novel prognostic model that provides new insights into HCC.

An immunogenomic signature for molecular classification in hepatocellular carcinoma

Immunity plays an important role in tumor development. In this study, we aimed to investigate molecular classification and its prognostic value in hepatocellular carcinoma (HCC) based on immune signature. Gene set enrichment analysis (GSEA) was used to calculate scores of immune pathways for HCC and hierarchical clustering in two databases (The Cancer Genome Atlas [TCGA], Liver Cancer-RIKEN, JP [LIRI_JP]). The scores of the immune microenvironment and the proportions of 22 immune cells were also calculated. Single-sample GSEA (ssGSEA) was used to screen survival prognosis-related immune pathways and calculate the hazard radio of differentially expressed immune-related genes (IRGs), which were validated in clinical samples and multiple datasets. Based on the immune characteristics, we identified three HCC subtypes, namely immunity high (Immunity_H), immunity medium (Immunity_M), and immunity low (Immunity_L), and confirmed that the classification was reliable and predictable. Immunity_H with a higher immune and stromal score indicated better survival rate. Cox regression analysis showed that IL18RAP and IL7R were the protective genes. Immune risk score was the independent risk factor of overall survival in HCC patients. These results indicated that immunogenomic classification could distinguish HCC patients with different immune status, which could impact the prognosis of the patients with HCC.

Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions

Background

Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described.

Methods

HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices.

Results

Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells.

Conclusions

The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02033-4.

Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer

Purpose: To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC).

Methods: Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and Immunology Database and Analysis Portal (ImmPort) were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452.

Results: In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic (ROC) curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group.

Conclusions: The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.

Novel nomograms based on immune and stromal scores for predicting the disease-free and overall survival of patients with hepatocellular carcinoma undergoing radical surgery

BACKGROUND

Stromal and immune cells play important roles in hepatocellular carcinoma (HCC) development and progression. However, tools for predicting the prognosis of patients with HCC based on stromal and immune scores are not well established. We aimed to develop nomograms that predicted the disease-free survival (DFS) and overall survival (OS) of patients after radical surgery.

METHODS

Basic information of 251 patients were retrieved from The Cancer Genome Atlas. Multivariate Cox analyses identified variables predicting the prognosis of patients. DFS and OS nomograms were constructed based on the stromal and immune scores of the training group and verified in the well-matched test group.

RESULTS

An intermediate stromal score (hazards ratio [HR] = 3.177; P < .001] was an independent risk factor for DFS. An intermediate immune score independently predicted a longer DFS (HR = 0.323; P = .002) and OS (HR = 0.305; P = .021); a high immune score predicted a longer DFS (HR = 0.289; P = .002). The concordance index (C-index) of nomograms was 0.729 for DFS and 0.696 for OS in the test group.

CONCLUSION

Nomograms based on the stromal and immune scores favorably predicted the DFS and OS of patients with HCC after radical surgery.

A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model.

METHODS

Based on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT.

RESULTS

A total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403.

CONCLUSION

The DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC.

A comprehensive study of construction and analysis of competitive endogenous RNA networks in lung adenocarcinoma

BACKGROUND

Long noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network.

METHODS

RNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors.

RESULTS

After compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively.

CONCLUSIONS

This study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches.