Exonuclease 1 (EXO1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Lung Adenocarcinoma

Wemics: A Single-Base Resolution Methylation Quantification Method for Enhanced Prediction of Epigenetic Regulation

DNA methylation, an epigenetic mechanism that alters gene expression without changing DNA sequence, is essential for organism development and key biological processes like genomic imprinting and X-chromosome inactivation. Despite tremendous efforts in DNA methylation research, accurate quantification of cytosine methylation remains a challenge. Here, a single-base methylation quantification approach is introduced by weighting methylation of consecutive CpG sites (Wemics) in genomic regions. Wemics quantification of DNA methylation better predicts its regulatory impact on gene transcription and identifies differentially methylated regions (DMRs) with more biological relevance. Most Wemics-quantified DMRs in lung cancer are epigenetically conserved and recurrently occurred in other primary cancers from The Cancer Genome Atlas (TCGA), and their aberrant alterations can serve as promising pan-cancer diagnostic markers. It is further revealed that these detected DMRs are enriched in transcription factor (TF) binding motifs, and methylation of these TF binding motifs and TF expression synergistically regulate target gene expression. Using Wemics on epigenomic-transcriptomic data from the large lung cancer cohort, a dozen novel genes with oncogenic potential are discovered that are upregulated by hypomethylation but overlooked by other quantification methods. These findings increase the understanding of the epigenetic mechanism by which DNA methylation regulates gene expression.

Prognostic value of different radiation-related cell death genes in patients with lung adenocarcinoma

BACKGROUND AND AIMS

Radiotherapy is widely applied for lung adenocarcinoma (LUAD), while individualized differences led to different outcomes. This study aimed to establish a multi-gene risk scoring model to predict the benefits of LUAD patients from radiotherapy, based on different types of cell death respectively.

RESULTS

Other than autophagy, pyroptosis, ferroptosis and Immunogenic cell death (ICD), the LUAD prognostic model based on apoptosis had the best performance, and the area under curves (AUCs) of the receiver operating curve (ROC) for 1-, 3-, and 5-year OS were 0.700,0.736,0.723,respectively. Such genes were involved as SLC7A5, EXO1, ABAT, NLRP1 and GAR1. Then patients were divided into high and low risk groups by the median apoptosis-LUAD risk score. For patients in the high-risk group, i.e., the radiotherapy-tolerant group, we screened adjuvant chemotherapy and found that besides the conventional first-line chemotherapy regimen, drugs such as Fludarabine, Pevonedistat, and Podophyllotoxin Bromide may also have potential therapeutic value.

CONCLUSION

The multi-gene risk scoring model based on apoptosis might predict the radiotherapy benefits of LUAD patients and for those radioresistant patients classified by the model we also provided effective adjuvant chemicals, which would be used to guide clinical treatment.

Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis

BACKGROUND

Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis.

METHODS

The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes.

RESULTS

We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle.

CONCLUSION

Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

Genome-Wide Analysis of Sheep Artificially or Naturally Infected with Gastrointestinal Nematodes

The anthelmintic resistance of gastrointestinal nematodes (GINs) poses a significant threat to sheep worldwide, but genomic selection can serve as an alternative to the use of chemical treatment as a solution for parasitic infection. The objective of this study is to conduct genome-wide association studies (GWASs) to identify single nucleotide polymorphisms (SNPs) in Rambouillet (RA) and Dorper × White Dorper (DWD) lambs associated with the biological response to a GIN infection. All lambs were genotyped with a medium-density genomic panel with 40,598 markers used for analysis. Separate GWASs were conducted using fecal egg counts (FECs) from lambs (<1 year of age) that acquired their artificial infections via an oral inoculation of 10,000 Haemonchus contortus larvae (n = 145) or naturally while grazing on pasture (n = 184). A GWAS was also performed for packed cell volume (PCV) in artificially GIN-challenged lambs. A total of 26 SNPs exceeded significance and 21 SNPs were in or within 20 kb of genes such as SCUBE1, GALNT6, IGF1R, CAPZB and PTK2B. The ontology analysis of candidate genes signifies the importance of immune cell development, mucin production and cellular signaling for coagulation and wound healing following epithelial damage in the abomasal gastric pits via H. contortus during GIN infection in lambs. These results add to a growing body of the literature that promotes the use of genomic selection for increased sheep resistance to GINs.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

Background

Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

Methods

We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.

Results

We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.

Conclusions

The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

Construction and validation of a hypoxia-related risk signature identified EXO1 as a prognostic biomarker based on 12 genes in lung adenocarcinoma

BACKGROUND

Increasing evidence has demonstrated the clinical importance of hypoxia and its related factors in lung adenocarcinoma (LUAD).

METHODS

RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analyzed using the differentially expressed genes in hypoxia pathway by the Least Absolute Shrinkage and Selection Operator (LASSO) model. Applying gene ontology (GO) and gene set enrichment analysis (GSEA), a risk signature associated with the survival of LUAD patients was constructed between LUAD and normal tissue.

RESULTS

In total, 166 hypoxia-related genes were identified. Based on the LASSO Cox regression, 12 genes were selected for the development of the risk signature. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.724. ROC curve showed better predictive ability using the nomogram (AUC = 0.811 for 5-year OS). Finally, the expressions of the 12 genes were validated in two external datasets and EXO1 was recognized as a potential biomarker in the progression of LUAD patients.

CONCLUSIONS

Overall, our data suggested that hypoxia is associated with the prognosis, and EXO1 acted as a promising biomarker in LUAD.

Exonuclease 1 genetic variant is associated with clinical outcomes of pemetrexed chemotherapy in lung adenocarcinoma

Pemetrexed is an anti-folate agent which is one of the most frequently used chemotherapy agents for non-squamous non-small cell lung cancer (NSCLC) patients. However, clinical response to pemetrexed chemotherapy and survival outcome of patients varies significantly. We evaluated whether the genetic variants in miRNA target sites may affect the treatment outcome of pemetrexed chemotherapy in lung adenocarcinoma patients. One hundred SNPs in miRNA binding regions in cancer-related genes were obtained from the crosslinking, ligation, and sequencing of hybrids (CLASH) and CancerGenes database, and the associations with the response to pemetrexed chemotherapy and survival outcomes were investigated in 314 lung adenocarcinoma patients. Two polymorphisms, EXO1 rs1047840G>A and CAMKK2 rs1653586G>T, were significantly associated with worse chemotherapy response (adjusted odds ratio [aOR] = 0.41, 95% CI = 0.24-0.68, P = 0.001, under dominant model; and aOR = 0.33, 95% CI = 0.16-0.67, P = 0.002, under dominant model, respectively) and worse OS (adjusted hazard ratio [aHR] = 1.34, 95% CI = 1.01-1.77, P = 0.04, under dominant model; and aHR = 1.50, 95% CI = 1.06-2.13, P = 0.02, under dominant model, respectively) in multivariate analyses. Significantly increased luciferase activity was noted in EXO1 rs1047840 A allele compared to G allele. In conclusion, two SNPs in miRNA binding sites, especially EXO1 rs1047840G>A, were associated with the chemotherapy response and survival outcome in lung adenocarcinoma patients treated with pemetrexed.

Breast cancer-associated SNP rs72755295 is a cis-regulatory variation for human EXO1

Breast cancer is the most common malignant tumor in women. A previous genome-wide association study reports that rs72755295, a SNP locating at intron of EXO1 (exonuclease 1), is associated with breast cancer. Due to the complete linkage disequilibrium between rs72755295 and rs4149909, a nonsynonymous mutation for EXO1, rs4149909 is supposed to be the causal SNP. Since EXO1 is overexpressed in breast carcinoma samples, we hypothesized that the genetic variations in this locus might confer breast cancer risk by regulating EXO1 expression. To substantiate this, a functional genomics study was performed. The dual luciferase assay indicated that G of rs72755295 presents significantly higher relative enhancer activity than A, thus verifying that this SNP can influence gene expression in breast cell. Through chromosome conformation capture it was disclosed that the enhancer containing rs72755295 can interact with the EXO1 promoter. RNA-seq analysis indicated that EXO1 expression is dependent on the rs72755295 genotype. By chromatin immunoprecipitation, the transcription factor PAX6 (paired box 6) was recognized to bind the region spanning rs72755295. In electrophoretic mobility shift assay, G of rs72755295 displays obviously higher binding affinity with nuclear protein than A. Our results indicated that rs72755295 is a cis-regulatory variation for EXO1 and might confer breast cancer risk besides rs4149909.

Immune infiltration landscape on prognosis and therapeutic response and relevant epigenetic and transcriptomic mechanisms in lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype, but its immune infiltration features are not comprehensively understood. To address the issue, the present study was initiated to describe the immune infiltrations across LUAD from cellular compositional, functional, and mechanism perspectives.

Methods

We adopted five LUAD datasets (GSE32863, GSE43458, GSE75037, TCGA-LUAD, and GSE72094). Differentially expressed genes between LUAD and controls were selected for co-expression network analysis. Risky immune cell types were determined for classifying LUAD patients as diverse subtypes, followed by a comparison of antitumor immunity and therapeutic response between subtypes. Then, LUAD- and subtype-related key module genes affected by DNA methylation were determined for quantifying a scoring scheme. EXO1 was chosen for functional analysis via in vitro assays.

Results

Two immune cell infiltration-based subtypes (C1 and C2) were established across LUAD, with poorer prognostic outcomes and lower infiltration of immune cell types in C1. Additionally, C1 presented higher responses to immune checkpoint blockade and targeted agents (JNK inhibitor VIII, BI-D1870, RO-3306, etc.). The scoring system (comprising GAPDH, EXO1, FYN, CFTR, and KLF4) possessed higher accuracy in estimating patients’ prognostic outcomes. EXO1 upregulation contributed to the growth, migration, and invasion of LUAD cells. In addition, EXO1 facilitated PD-L1 and sPD-L1 expression in LUAD cells.

Conclusion

Altogether, our findings offer a comprehensive understanding of the immune infiltration landscape on prognosis and therapeutic response of LUAD as well as unveil potential epigenetic and transcriptomic mechanisms, which might assist personalized treatment.

Development and validation of an epithelial–mesenchymal transition-related gene signature for predicting prognosis

BACKGROUND

Currently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial– mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear.

AIM

To further elucidate the value of EMT-related genes in LUAD prognosis.

METHODS

Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan–Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model.

RESULTS

Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group (P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5).

CONCLUSION

The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.

Exonucleases: Degrading DNA to Deal with Genome Damage, Cell Death, Inflammation and Cancer

Although DNA degradation might seem an unwanted event, it is essential in many cellular processes that are key to maintaining genomic stability and cell and organism homeostasis. The capacity to cut out nucleotides one at a time from the end of a DNA chain is present in enzymes called exonucleases. Exonuclease activity might come from enzymes with multiple other functions or specialized enzymes only dedicated to this function. Exonucleases are involved in central pathways of cell biology such as DNA replication, repair, and death, as well as tuning the immune response. Of note, malfunctioning of these enzymes is associated with immune disorders and cancer. In this review, we will dissect the impact of DNA degradation on the DNA damage response and its links with inflammation and cancer.

Upregulation of EXO1 caused by homology-dependent repair confers cisplatin resistance to gastric cancer cells

The homology-dependent repair (HDR) pathway is involved in DNA damage response (DDR), which is crucial to cancer cell survival after treatment with DNA damage agents, including cisplatin (CDDP). Here, we explored the interactions between EXO1, a core gene in the HDR pathway, and CDDP resistance in gastric cancer (GC). Using bioinformatics analysis, we identified the HDR pathway as the most amplified pathway in DDR in GC. In addition, EXO1 was the core gene in the HDR pathway and showed the most significant amplification in GC. The amplification of EXO1 resulted in higher EXO1 expression in cancerous tissues, with malignant prognostic effects. Moreover, we upregulated or downregulated EXO1 in GC cells to examine its effects on the cell malignant phenotype and CDDP resistance in vitro and in vivo. Depletion of EXO1 inhibited cell proliferatory, migratory and invasive activities, and provided apoptosis resistance to GC cells. EXO1 expression was elevated in CDDP-resistant cells. Ectopic expression of EXO1 increased the resistance of GC cells to CDDP, while downregulation of EXO1 increased the sensitivity of GC cells. Taken together, our study indicates that the HDR pathway is an important player in CDDP resistance in GC through the regulation of EXO1.

A TP53 Related Immune Prognostic Model for the Prediction of Clinical Outcomes and Therapeutic Responses in Lung Adenocarcinoma

TP53 is the most frequently mutated gene in lung adenocarcinoma (LUAD). The tumor immune microenvironment (TIM) is considered a vital factor that influences tumor progression and survival rate. The influence of TP53 mutation on TIM in LUAD has not been fully studied. Here we systematically investigated the relationship and potential mechanisms between TP53 mutation status and immune response in LUAD. We constructed an immune prognostic model (IPM) using immune associated genes, which were expressed differentially between the TP53 mutant and wild type LUAD patients. We discovered that TP53 mutations were significantly associated with 5 immune related biological processes. Thirty-six immune genes were expressed differentially between TP53 mutant and wild type LUAD patients. An IPM was constructed using 3 immune genes to differentiate the prognostic survival in LUAD. The high-risk LUAD group displayed significantly higher proportions of dendritic cell resting, T cell CD4 memory resting and mast cell resting, and significantly low proportions of dendritic cell activated, T cell CD4 memory activated, and mast cell activated. Moreover, IPM was found to be an independent clinical feature and can be used to predict immunotherapy responses. In summary, we constructed and validated an IPM using 3 immune related genes, which provides a better understanding of the mechanism from an immunological perspectives.

Exploring Potential Regulatory Anesthetic Drugs Based on RNA Binding Protein and Constructing CESC Prognosis Model: A Study Based on TCGA Database

Objective

To investigate the differential expression of RBPs in cervical squamous cell carcinoma (CESC), analyze the regulatory effect of narcotic drugs on RBPs, and establish the prognostic risk model of CESC patients.

Methods

RNA-SEQ data and clinical case data of cancer and normal samples from CESC patients were obtained from the Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) database. Differentially expressed RBPs were screened by R language and enriched. The CMAP database is used to predict the anesthetic drugs that regulate the differential expression of RBPs. The prognostic risk score model was constructed by COX regression analysis. Risk score of each CESC patient was calculated and divided into high-risk group and low-risk group according to the median risk score. The prediction efficiency of prognostic risk model was evaluated by Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve, and the correlation between prognostic risk model and clinical characteristics was analyzed. Immunohistochemistry was used to detect the expression of RNASEH2A and HENMT1 in tissues.

Results

There were 65 differentially expressed RBPs in CESC. Five anesthetics, including benzocaine, procaine, pentoxyverine, and tetracaine were obtained to regulate RBPs. Survival analysis showed that seven genes were related to the prognosis of patients, and the CESC risk score model was constructed by COX regression. The risk score can be used as an independent prognostic factor. RNASEH2A and HENMT1 are up-regulated in tumors, which can effectively distinguish normal tissues from tumor tissues.

Conclusion

It is found that different anesthetic drugs have different regulatory effects on the differential expression of RBPs. Based on the differentially expressed RBPs, the prognostic risk score model of CESC patients was constructed. To provide ideas for the formulation of individualized precise anesthesia scheme and cancer pain analgesia scheme, which is helpful to improve the perioperative survival rate of cancer patients.

Methylation- and homologous recombination deficiency-related mutant genes predict the prognosis of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a lung cancer subtype with poor prognosis. We investigated the prognostic value of methylation‐ and homologous recombination deficiency (HRD)‐associated gene signatures in LUAD.

Methods

Data on RNA sequencing, somatic mutations, and methylation were obtained from TCGA database. HRD scores were used to stratify patients with LUAD into high and low HRD groups and identify differentially mutated and expressed genes (DMEGs). Pearson correlation analysis between DMEGs and methylation yielded methylation‐associated DMEGs. Cox regression analysis was used to construct a prognostic model, and the distribution of clinical features in the high‐ and low‐risk groups was compared.

Results

Patients with different HRD scores showed different DNA mutation patterns. There were 272 differentially mutated genes and 6294 differentially expressed genes. Fifty‐seven DMEGs were obtained; the top 10 upregulated genes were COL11A1, EXO1, ASPM, COL12A1, COL2A1, COL3A1, COL5A2, DIAPH3, CAD, and SLC25A13, while the top 10 downregulated genes were C7, ERN2, DLC1, SCN7A, SMARCA2, CARD11, LAMA2, ITIH5, FRY, and EPHB6. Forty‐two DMEGs were negatively correlated with 259 methylation sites. Gene ontology and pathway enrichment analysis of the DMEGs revealed enrichment of loci involved in extracellular matrix‐related remodeling and signaling. Six out of the 42 methylation‐associated DMEGs were significantly associated with LUAD prognosis and included in the prognostic model. The model effectively stratified high‐ and low‐risk patients, with the high‐risk group having more patients with advanced stage disease.

Conclusion

We developed a novel prognostic model for LUAD based on methylation and HRD. Methylation‐associated DMEGs may function as biomarkers and therapeutic targets for LUAD. Further studies are needed to elucidate their roles in LUAD carcinogenesis.

Bioinformatics Analysis and Experimental Study of Exonuclease 1 Gene in Lung Adenocarcinoma

The objective of this study is to examine the role of Human Exonuclease 1(EXO1) gene in the diagnosis and prognosis of lung adenocarcinoma (LUAD), and predict the signal pathways EXO1 involved in. The clinical parameters and EXO1 expression datasets of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), Oncomine and Gene Expression Omnibus (GEO) database. Wilcoxon rank-sum test was performed to determine whether EXO1 expression was upregulated in LUAD. The correlation between EXO1 expression and clinicopathological parameters was analyzed by Chi-square test, and Kaplan-Meier survival analysis and COX regression models were adopted to analyze and verify the correlation of EXO1 expression with OS of LUAD patients for the exploration of prognostic value of EXO1 in LUAD patients. The signaling pathway related to EXO1 was predicted by gene set enrichment analysis (GSEA). In addition, sera from LUAD patients and healthy subjects were collected, and real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was conducted to detect EXO1 expression. EXO1 expression was upregulated in LUAD patients with respect to normal individuals. EXO1 expression was negatively correlated with the prognosis and thus could independently predict the prognosis of LUAD patients. EXO1 gene was involved in 128 signal pathways, of which 9 pathways may be closely related. EXO1 was highly expressed in the blood of LUAD patients. High EXO1 expression can serve as an independent risk factor for poor prognosis, and the expression of serum EXO1 has certain diagnostic value for LUAD.

Novel Genetic Prognostic Signature for Lung Adenocarcinoma Identified by Differences in Gene Expression Profiles of Low- and High-Grade Histological Subtypes

The 2021 WHO classification proposed a pattern-based grading system for early-stage invasive non-mucinous lung adenocarcinoma. Lung adenocarcinomas with high-grade patterns have poorer outcomes than those with lepidic-predominant patterns. This study aimed to establish genetic prognostic signatures by comparing differences in gene expression profiles between low- and high-grade adenocarcinomas. Twenty-six (9 low- and 17 high-grade adenocarcinomas) patients with histologically “near-pure” patterns (predominant pattern comprising >70% of tumor areas) were selected retrospectively. Using RNA sequencing, gene expression profiles between the low- and high-grade groups were analyzed, and genes with significantly different expression levels between these two groups were selected for genetic prognostic signatures. In total, 196 significant candidate genes (164 upregulated and 32 upregulated in the high- and low-grade groups, respectively) were identified. After intersection with The Cancer Genome Atlas–Lung Adenocarcinoma prognostic genes, three genes, exonuclease 1 (EXO1), family with sequence similarity 83, member A (FAM83A), and disks large-associated protein 5 (DLGAP5), were identified as prognostic gene signatures. Two independent cohorts were used for validation, and the areas under the time-dependent receiver operating characteristic were 0.784 and 0.703 in the GSE31210 and GSE30219 cohorts, respectively. Our result showed the feasibility and accuracy of this novel three-gene prognostic signature for predicting the clinical outcomes of lung adenocarcinoma.

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Background

Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).

Methods

“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.

Results

A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.

Conclusion

In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.