The overexpression of FKBP4 in patients with lung adenocarcinoma predicts poor prognosis and tumor progression

Abstract Background: The FK506-binding protein 4 ( FKBP4 ), a tumor-related gene, plays a vital role in tumorigenesis and cancer progression. The study is aimed to clarify the effect of FKBP4 in lung adenocarcinoma (LUAD). Methods: Relying on The Cancer Genome Atlas (TCGA) cohort, the FKBP4 expression difference between LUAD tissues and non-tumor tissues was first detected, and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort and Gene Expression Omnibus (GEO) cohort. Then, logistic regression analysis and chi-square test were applied to detect the correlation between FKBP4 expression and clinicopathological parameters. Kaplan-Meier survival analysis and Cox regression model were utilized to evaluate the effect of FKBP4 expression on survival. Signaling pathways related to LUAD were obtained via employing Gene Set Enrichment Analysis (GSEA). Results: The FKBP4 expression level in LUAD samples was dramatically higher than that in non-tumor samples. High FKBP4 expression in LUAD is associated with gender, pathological stage, T classification, lymph node metastasis and distant metastasis. The Kaplan-Meier curve indicated a poor prognosis for LUAD patients with high FKBP4 expression. Multivariate analysis suggested that the high FKBP4 expression was a vital independent predictor of poor overall survival (OS). GSEA showed that a total of 15 signaling pathways were enriched in samples with high FKBP4 expression phenotype. Conclusions: FKBP4 may be an oncogene in LUAD, and is promised to become a prognostic indicator and therapeutic target for LUAD.

Download Full-text

Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.605754 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wenting Liu ◽

Kaiting Jiang ◽

Jingya Wang ◽

Ting Mei ◽

Min Zhao ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Poor Prognosis ◽

Cox Regression ◽

Clinical Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Value ◽

Cox Regression Analysis ◽

Normal Tissues

BackgroundGlucosamine 6-phosphate N-acetyltransferase (GNPNAT1) is a key enzyme in the hexosamine biosynthetic pathway (HBP), which functions as promoting proliferation in some tumors, yet its potential biological function and mechanism in lung adenocarcinoma (LUAD) have not been explored.MethodsThe mRNA differential expression of GNPNAT1 in LUAD and normal tissues was analyzed using the Cancer Genome Atlas (TCGA) database and validated by real-time PCR. The clinical value of GNPNAT1 in LUAD was investigated based on the data from the TCGA database. Then, immunohistochemistry (IHC) of GNPNAT1 was applied to verify the expression and clinical significance in LUAD from the protein level. The relationship between GNPNAT1 and epigenetics was explored using the cBioPortal database, and the miRNAs regulating GNPNAT1 were found using the miRNA database. The association between GNPNAT1 expression and tumor-infiltrating immune cells in LUAD was observed through the Tumor IMmune Estimation Resource (TIMER). Finally, Gene set enrichment analysis (GSEA) was used to explore the biological signaling pathways involved in GNPNAT1 in LUAD.ResultsGNPNAT1 was upregulated in LUAD compared with normal tissues, which was verified through qRT-PCR in different cell lines (P < 0.05), and associated with patients’ clinical stage, tumor size, and lymphatic metastasis status (all P < 0.01). Kaplan–Meier (KM) analysis suggested that patients with upregulated GNPNAT1 had a relatively poor prognosis (P < 0.0001). Furthermore, multivariate Cox regression analysis indicated that GNPNAT1 was an independent prognostic factor for LUAD (OS, TCGA dataset: HR = 1.028, 95% CI: 1.013–1.044, P < 0.001; OS, validation set: HR = 1.313, 95% CI: 1.130–1.526, P < 0.001). GNPNAT1 overexpression was correlated with DNA copy amplification (P < 0.0001), low DNA methylation (R = −0.52, P < 0.0001), and downregulation of hsa-miR-30d-3p (R = −0.17, P < 0.001). GNPNAT1 expression was linked to B cells (R = −0.304, P < 0.0001), CD4+T cells (R = −0.218, P < 0.0001), and dendritic cells (R = −0.137, P = 0.002). Eventually, GSEA showed that the signaling pathways of the cell cycle, ubiquitin-mediated proteolysis, mismatch repair and p53 were enriched in the GNPNAT1 overexpression group.ConclusionGNPNAT1 may be a potential prognostic biomarker and novel target for intervention in LUAD.

Download Full-text

Elevated Heterogeneous Nuclear Ribonucleoprotein C Expression Correlates With Poor Prognosis in Patients With Surgically Resected Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.598437 ◽

2021 ◽

Vol 10 ◽

Author(s):

Wei Guo ◽

Qilin Huai ◽

Guochao Zhang ◽

Lei Guo ◽

Peng Song ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Poor Prognosis ◽

Prognostic Value ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Normal Lung ◽

Heterogeneous Nuclear Ribonucleoprotein ◽

Kaplan Meier ◽

Nuclear Ribonucleoprotein

BackgroundLung adenocarcinoma (LUAD), as the most common histological subtype of lung cancer, is a high-grade malignancy and a leading cause of cancer-related death globally. Identification of biomarkers with prognostic value is of great significance for the diagnosis and treatment of LUAD. Heterogeneous nuclear ribonucleoprotein C (HNRNPC) is an RNA-binding protein “reader” of N6-methyladenosine (m6A) methylation, and is related to the progression of various cancers; however, its role in LUAD is unclear. The aims of this study aims were to study the expression and prognostic value of HNRNPC in LUAD.MethodsThe Oncomine database and gene expression profiling interactive analysis (GEPIA) were used for preliminary exploration of HNRNPC expression and prognostic value in LUAD. LUAD cases from The Cancer Genome Atlas (TCGA) (n = 416) and the Kaplan-Meier plotter database (n = 720) were extracted to study the differential expression and prognostic value of HNRNPC. HNRNPC expression in the National Cancer Center of China (NCC) cohort was analyzed by immunohistochemical staining, and the relationship between HNRNPC expression and survival rate evaluated using the Kaplan-Meier method and log-rank test. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Several pathways that were significantly enriched in the HNRNPC high expression group were identified by Gene Set Enrichment Analysis (GSEA).ResultsFive data sets from the Oncomine and GEPIA databases all supported that HNRNPC expression is significantly higher in LUAD than in normal lung tissue. In TCGA cohort, HNRNPC was highly expressed in LUAD tissues and significantly related to age, sex, smoking history, ethnicity, lymph node metastasis, and TNM staging (P < 0.001). High HNRNPC expression was significantly correlated with poor prognosis in the three cohorts (NCC, TCGA, and K-M plotter) (P < 0.05). Multivariate Cox regression analysis showed that HNRNPC expression was an independent prognostic factor in both TCGA and NCC cohorts (P < 0.05). Further, 10 significantly enriched pathways were identified from TCGA data and 118 lung cancer cell lines in CCLE, respectively.ConclusionsHigh HNRNPC expression is significantly related to poor overall survival in patients with LUAD, suggesting that HNRNPC may be a cancer-promoting factor and a potential prognostic biomarker in LUAD.

Download Full-text

Identification and Validation of a Ferroptosis-Related Long Non-coding RNA Signature for Predicting the Outcome of Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.690509 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhiyuan Zheng ◽

Qian Zhang ◽

Wei Wu ◽

Yan Xue ◽

Shuhan Liu ◽

...

Keyword(s):

Immune Response ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Information Criteria ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Kaplan Meier

BackgroundFerroptosis is a recently recognized type of programmed cell death that is involved in the biological processes of various cancers. However, the mechanism of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the role of ferroptosis-associated long non-coding RNAs (lncRNAs) in LUAD and to establish a prognostic model.MethodsWe downloaded ferroptosis-related genes from the FerrDb database and RNA sequencing data and clinicopathological characteristics from The Cancer Genome Atlas. We randomly divided the data into training and validation sets. Ferroptosis-associated lncRNA signatures with the lowest Akaike information criteria were determined using COX regression analysis and the least absolute shrinkage and selection operator. The risk scores of ferroptosis-related lncRNAs were calculated, and patients with LUAD were assigned to high- and low-risk groups based on the median risk score. The prognostic value of the risk scores was evaluated using Kaplan–Meier curves, Cox regression analyses, and nomograms. We then explored relationships between ferroptosis-related lncRNAs and the immune response using gene set enrichment analysis (GSEA).ResultsTen ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan–Meier and Cox regression analyses confirmed that the risk scores were independent predictors of LUAD outcome in the training and validation sets (all P < 0.05). The area under the curve confirmed that the signatures could determine the prognosis of LUAD. The predictive accuracy of the established nomogram model was verified using the concordance index and calibration curve. The GSEA showed that the 10 ferroptosis-related lncRNAs might be associated with tumor immune response.ConclusionWe established a novel signature involving 10 ferroptosis-related lncRNAs (LINC01843, MIR193BHG, AC091185.1, AC027031.2, AL021707.2, AL031667.3, AL606834.1, AC026355.1, AC124045.1, and AC025048.4) that can accurately predict the outcome of LUAD and are associated with the immune response. This will provide new insights into the development of new therapies for LUAD.

Download Full-text

Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.639254 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jixin Wang ◽

Xiangjun Yin ◽

Yin-Qiang Zhang ◽

Xuming Ji

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Kaplan Meier

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan–Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

Download Full-text

Reduced angiomotin p130 expression correlates with poor prognosis in lung adenocarcinoma

Journal of Clinical Pathology ◽

10.1136/jclinpath-2016-204071 ◽

2016 ◽

Vol 70 (7) ◽

pp. 625-630 ◽

Cited By ~ 2

Author(s):

Si-Hyong Jang ◽

Hyun Deuk Cho ◽

Ji-Hye Lee ◽

Hyun Ju Lee ◽

Soon Auck Hong ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Poor Prognosis ◽

Cox Regression ◽

Statistical Significance ◽

Disease Free Survival ◽

Clinicopathological Parameters ◽

Rank Test ◽

Log Rank Test ◽

Hazards Model ◽

Kaplan Meier

AimsLung cancer is the leading cause of cancer-related deaths worldwide, and it still results in a poor prognosis despite research and development of a treatment modality. Angiomotin (AMOT) was first described as a protein involved in angiogenesis, and although the oncogenic and tumour-suppressive roles of AMOT were recently reported, the biological function of AMOT has not yet been clarified. The aim of this study was thus to evaluate the relationship between reduced AMOT p130 expression and clinicopathological parameters, including patients' survival.MethodsWe enrolled 67 patients with lung adenocarcinoma in this study and measured the immunoreactivity of AMOT p130 in a tissue microarray. The data were analysed using a χ2 test, Cox regression hazards model and log-rank test with Kaplan–Meier curves.ResultsReduced AMOT p130 expression is related to lung adenocarcinoma developed at a young age with statistical significance, but there is no statistical significance for the other clinicopathological parameters. Kaplan–Meier curves with log-rank test showed that reduced AMOT p130 expression had significantly better survival rate compared with the retained group (p=0.002). Univariable and multivariable analyses of the disease free survival revealed that the decreased AMOT expression was an independent prognostic factor (p=0.004, p=0.008, respectively).ConclusionsDecreased AMOT p130 could be an independent indicator of poor survival in patients with lung adenocarcinoma.

Download Full-text

Increased Expression of KNSTRN in Lung Adenocarcinoma Predicts Poor Prognosis: A Bioinformatics Analysis Based on TCGA Data

10.21203/rs.3.rs-31066/v1 ◽

2020 ◽

Author(s):

Pengbo Deng ◽

Rongrong Zhou ◽

Jinghui Zhang ◽

Jian An ◽

Liming Cao

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Clinicopathological Characteristics ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Expression Level ◽

Th2 Cells ◽

Normal Lung ◽

Rank Test ◽

Kaplan Meier

Abstract Background: Available evidence indicates that kinetochore-localized astrin/SPAG5-binding protein (KNSTRN) is an oncogene in skin carcinoma. This study aimed to evaluate the prognostic value of KNSTRN in lung adenocarcinoma (LUAD) underlying the Cancer Genome Atlas (TCGA) database. Methods: The relationship between clinicopathological features and KNSTRN was analyzed with the Wilcoxon signed-rank test and logistic regression. The clinicopathological characteristics associated with overall survival (OS) were evaluated using Cox regression and the Kaplan–Meier method. Gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and single-sample GSEA (ssGSEA) were performed using TCGA data.Results: The KNSTRN expression level was found to be significantly higher in LUAD tissue than in normal lung tissue. Also, it correlated significantly with advanced clinicopathological characteristics. The Kaplan–Meier survival curve revealed a significant relationship of high expression of KNSTRN with poor OS in patients with LUAD. The multivariate Cox regression hazard model demonstrated the KNSTRN expression level as an independent prognostic factor for patients with LUAD. GO and GSEA analyses indicated the involvement of KNSTRN in cell cycle checkpoints, DNA replication, and G2-M checkpoint M phase. Based on ssGSEA analysis, KNSTRN had a positive relationship with Th2 cells and CD56dim natural killer cells. The KNSTRN expression levels in several types of immune cells were significantly different.Conclusion: The findings suggested that the increased expression level of KNSTRN was significantly associated with the progression of LUAD and could also serve as a novel prognostic biomarker for patients with LUAD.

Download Full-text

Co-Expression of Coxsackievirus/Adenovirus Receptors and Desmoglein 2 in Lung Adenocarcinoma: A Comprehensive Analysis of Bioinformatics and Tissue Microarrays

Journal of Clinical Medicine ◽

10.3390/jcm9113693 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3693

Author(s):

Ching-Fu Weng ◽

Chi-Jung Huang ◽

Mei-Hsuan Wu ◽

Henry Hsin-Chung Lee ◽

Thai-Yen Ling

Keyword(s):

Overall Survival ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Early Stage ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Mesenchymal Transition ◽

Kaplan Meier

Introduction: Coxsackievirus/adenovirus receptors (CARs) and desmoglein-2 (DSG2) are similar molecules to adenovirus-based vectors in the cell membrane. They have been found to be associated with lung epithelial cell tumorigenesis and can be useful markers in predicting survival outcome in lung adenocarcinoma (LUAD). Methods: A gene ontology enrichment analysis disclosed that DSG2 was highly correlated with CAR. Survival analysis was then performed on 262 samples from the Cancer Genome Atlas, forming “Stage 1A” or “Stage 1B”. We therefore analyzed a tissue microarray (TMA) comprised of 108 lung samples and an immunohistochemical assay. Computer counting software was used to calculate the H-score of the immune intensity. Cox regression and Kaplan–Meier analyses were used to determine the prognostic value. Results: CAR and DSG2 genes are highly co-expressed in early stage LUAD and associated with significantly poorer survival (p = 0.0046). TMA also showed that CAR/DSG2 expressions were altered in lung cancer tissue. CAR in the TMA was correlated with proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), while DSG2 was associated with proliferation only. The Kaplan–Meier survival analysis revealed that CAR, DSG2, or a co-expression of CAR/DSG2 was associated with poorer overall survival. Conclusions: The co-expression of CAR/DSG2 predicted a worse overall survival in LUAD. CAR combined with DSG2 expression can predict prognosis.

Download Full-text

Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

Journal of Oncology ◽

10.1155/2020/5310793 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Xiaolin Yu ◽

Xiaomei Zhang ◽

Yanxia Zhang

Keyword(s):

Tumor Microenvironment ◽

Confidence Interval ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Lasso Regression ◽

The Status

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

Download Full-text

Identification of a nine ferroptosis-related lncRNA prognostic signature for lung adenocarcinoma

10.21203/rs.3.rs-210507/v1 ◽

2021 ◽

Author(s):

Xiwen Tong ◽

Yujiao Zhang ◽

Guodong Yang ◽

Guanghui Yi

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Rank Test ◽

Lymph Node Status ◽

Prognostic Signature ◽

Test Set

Abstract Background Recently, mounting of studies has shown that lncRNA affects tumor progression through the regulation of ferroptosis. The current study aims to construct a robust ferroptosis-related lncRNAs signature to increase the predicted value of lung adenocarcinoma (LUAD) by bioinformatics analysis. Methods The transcriptome data were abstracted from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened by comparing 535 LAUD tissues with 59 adjacent non-LAUD tissues. Univariate Cox regression, lasso regression, multivariate Cox regression were conducted to design a ferroptosis-related lncRNA signature. This signature’s prognosis was verified by the log-rank test of Kaplan-Meier curve and the area under curve (AUC) of receiver operating characteristic (ROC) in train set, test set, and entire set. Furthermore, univariate and multivariate Cox regression were used to analyze its independent prognostic ability. The relationship of the ferroptosis-linked lncRNAs' expression and clinical variables was demonstrated by Wilcoxon rank-sum test and Kruskal-Wallis test. Gene set enrichment analysis (GSEA) was performed to signaling pathways it may involve. Results 1224 differentially expressed lncRNAs were idendified, of which 195 are ferroptosis-related lncRNAs. A nine ferroptosis-related lncRNAs (AC099850.3, NAALADL2-AS2, AL844908.1, AL365181.2, SMIM25, FAM83A-AS1, LINC01116, AL049836.1, C20orf197) prognostic signature was constucted. This model's prognosis in the high-risk group is obviously worse than that of the low-risk group in train set, test set, and entire set. The AUC of ROC predicting the three years survival in the train set, test set, and entire set was 0.754, 0.716, and 0.738, respectively. Moreover, the designed molecular signature was found to be an independent prognostic variable. The expression of these lncRNAs and the lncRNA signature are related to clinical stage, T stage, Lymph-node status, distant metastasis. Finally, GSEA analysis results show that the signature is involved in eight tumor-related and metabolism-related signaling pathways Conclusion The current study constructed, validated, and evaluated a nine ferroptosis-related lncRNA signature which can independently be used to predict the prognosis of LAUD patients, and may become a new therapeutic target.

Download Full-text

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.

Download Full-text