Identification of candidate genes and prognostic value analysis in patients with PDL1-positive and PDL1-negative lung adenocarcinoma

Introduction

Non–small-cell lung cancer (NSCLC) is a leading malignancy threatening human life and health worldwide (Herbst, Morgensztern & Boshoff, 2018; Zhang et al., 2019). Lung adenocarcinoma is the most common type of NSCLC, and it is a more heterogeneous subtype of NSCLC compared with squamous cell carcinoma. Therefore, its individualized treatment and prognosis have attracted considerable attention; understanding its biological characteristics is necessary for achieving such individualized treatment and prognosis. In the past decade, studies have identified tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and ROS proto-oncogene 1 (ROS1) as potential therapies for lung adenocarcinoma on the basis of genotyping (Sgambato et al., 2018; Singhi et al., 2019). Molecular targeted therapy based on these sensitive targets has considerably enhanced overall survival (OS) in lung adenocarcinoma. Despite the progress in the treatment of lung adenocarcinoma, the mortality rate among patients with advanced lung adenocarcinoma remains high, with the 5-year survival rate being approximately 15% in patients with advanced NSCLC (Blandin Knight et al., 2017). Nevertheless, a breakthrough was achieved in terms of immunotherapy for cancer according to recent reports, with immune checkpoint inhibitors prolonging survival in some patients with various cancer types (Yu & Wang, 2018; Lorigan & Eggermont, 2019; Morse, Hochster & Benson, 2020). In particular, targeting programmed cell death protein 1 (PD-1) monoclonal antibodies is rapidly becoming a promising therapeutic approach for NSCLC treatment (El-Osta & Jafri, 2019). Patients with >50% PDL1 expression treated using anti-PD-1 therapy showed a longer progression-free survival (PFS) period than did those treated using standard chemotherapy as the first-line treatment for NSCLC. Furthermore, in the past few years, some of these antibodies have been successfully commercialized and approved as first- and second-line treatments for advanced NSCLC (Gridelli et al., 2018; Dafni et al., 2019). In particular, immunotherapy is different from traditional chemotherapy or targeted therapy in that it has durable clinical benefits and fewer side effects in general (Suresh et al., 2018). Hence, immunotherapy is a promising therapy for lung adenocarcinoma; however, its application is limited to a subtype of patients with lung adenocarcinoma. Individualized immunotherapy guided by predictive biomarkers is crucial for improving the prognosis of lung adenocarcinoma.

Recent studies have proposed PDL1 expression, tumor mutational burden, and DNA mismatch repair deficiency as biomarkers for anti-PD-1 therapy (Teng et al., 2018; Darvin et al., 2018; El-Osta & Jafri, 2019). In particular, PDL1 expression—recommended by the National Comprehensive Cancer Network guidelines—is widely recognized as the most powerful predictive marker for immunotherapy in lung adenocarcinoma. Moreover, patients with PDL1-positive lung adenocarcinoma are more likely to benefit from immunotherapy than those with PDL1-negative lung adenocarcinoma (Gridelli et al., 2018; Dafni et al., 2019), suggesting that PDL1 expression plays a major role in the pathogenesis of lung adenocarcinoma. However, the mechanism underlying this clinical problem remains unclear. Understanding the regulatory role of PDL1 expression in lung adenocarcinoma is crucial for precision immunotherapy. Currently, PDL1 expression is primarily determined using immunohistochemistry (IHC) assays; nevertheless, IHC assays yield inconsistent results due to variable cutoffs and different antibodies with differing affinities (Hunter, Socinski & Villaruz, 2018). Hence, such assays cannot comprehensively elucidate the molecular mechanism and enriched pathways underlying the efficacy of immunotherapy. To solve this problem, novel strategies are imperative to explore the intrinsic mechanism of PDL1 expression in the biology of lung adenocarcinoma.

Because of advances in next-generation sequencing technology, high-throughput sequencing results on cancers have been increasingly released on the Gene Expression Omnibus (GEO), a public database; this can thus enable understanding the molecular mechanism of PDL1 expression and pathogenesis of lung adenocarcinoma. Using next-generation sequencing technology, previous studies have successfully identified key biomarkers in lung adenocarcinoma, which has proved to be essential in understanding the molecular mechanism of tumors (Tang et al., 2018; Li et al., 2019). Because PDL1 expression might affect certain key pathways in immunotherapy for lung adenocarcinoma, exploring the differences between patients with PDL1-positive and those with PDL1-negative lung adenocarcinoma would provide insights into the regulatory mechanisms of immunotherapy. Hence, it is reasonable to explore the differences in gene expression profiles and enriched pathways between the two groups of patients. Using the GEO database, we studied a gene expression dataset (GEO accession number: GSE99995) and comparatively analyzed sequencing data of gene expression between patients with PDL1-positive and PDL1-negative lung adenocarcinoma using the Agilent oligonucleotide microarray system. The aim of the study was to explore the differences in gene expression profiles and enriched pathways between the two groups of patients and identify the potential biomarkers predicting patient prognosis in order to elucidate the regulatory mechanism of PDL1 in immunotherapy, which could be crucial in guiding precision immunotherapy and improve prognosis in lung adenocarcinoma.

Material and Methods

Results

Identification and mapping of DEGs

Our results revealed that the PDL1-positive and PDL1-negative groups comprised 34,729 genes. According to criteria of a log FoldChange of >2, and an adjusted P value of <0.05, a total of 869 DEGs were identified, with 387 and 482 DEGs in the PDL1-positive and PDL1-negative groups, respectively. The corresponding heat map and volcano map are shown in Fig. 1.

GO terms and KEGG pathway enrichment analysis of DEGs

The analysis of DEGs by using GO terms was divided into three parts, namely biological processes, cellular components, and molecular functions. The GO analysis of the PDL1-positive group mainly revealed the enrichment of pathways involved in cell adhesion (BP, GO:0007155) plasma membrane (CC, GO:0005886), and cadmium ion binding (MF, GO:0046870). The GO analysis of the PDL1-negative group mainly revealed the enrichment of pathways involved in cell division (BP, GO:0051301), nucleoplasm (CC, GO:0005654), and DNA binding (MF, GO:0003677). The detailed results are shown in Table 1. The KEGG pathway enrichment results in the PDL1-positive group revealed that the enriched pathways were mainly those involved in cell adhesion molecules, PPAR signaling, and ECM-receptor interaction. However, the KEGG pathway enrichment results of the PDL1-negative group revealed that the enriched pathways were mainly those involved in DNA replication, cell cycle, and mismatch repair (Table 2). The detailed results are shown in Table 2. The top 10 GO terms and KEGG enriched pathways were mapped using the Hmisc and ggplot2 packages. The results are shown in Fig. 2.

Table 1:

Top 10 GO terms.

Category	Term	Count	%	P-Value	FDR
PDL1 positive group
GOTERM_BP_FAT	Cell adhesion	21	8.713692946	2.88E−04	0.469256078
GOTERM_BP_FAT	Biological adhesion	21	8.713692946	2.94E−04	0.478025436
GOTERM_BP_FAT	Cell–cell adhesion	12	4.979253112	5.17E−04	0.841085346
GOTERM_BP_FAT	Regulation of cell motion	10	4.149377593	5.50E−04	0.894179746
GOTERM_BP_FAT	MAPKKK cascade	9	3.734439834	0.00173479	2.79437728
GOTERM_CC_FAT	Plasma membrane	81	33.60995851	1.28E−08	1.64E−05
GOTERM_CC_FAT	Intrinsic to plasma membrane	33	13.69294606	2.53E−05	0.032468756
GOTERM_CC_FAT	Intrinsic to membrane	93	38.58921162	5.00E−05	0.064061563
GOTERM_CC_FAT	Integral to plasma membrane	31	12.86307054	1.03E−04	0.132137329
GOTERM_CC_FAT	Membrane raft	8	3.319502075	0.001913071	2.423429637
GOTERM_MF_FAT	Cadmium ion binding	3	1.244813278	0.006756465	8.971494974
GOTERM_MF_FAT	Sugar binding	8	3.319502075	0.012297355	15.76520126
GOTERM_MF_FAT	Carbohydrate binding	11	4.564315353	0.01503554	18.94591653
GOTERM_MF_FAT	Calcium ion binding	20	8.298755187	0.024264811	28.86459093
GOTERM_MF_FAT	Lipid binding	12	4.979253112	0.02867722	33.1973471
PDL1 negative group
GOTERM_BP_DIRECT	Cell division	34	8.629441624	4.29E−15	7.22E−12
GOTERM_BP_DIRECT	DNA replication	22	5.583756345	4.68E−13	7.79E−10
GOTERM_BP_DIRECT	Mitotic nuclear division	26	6.598984772	2.39E−12	3.98E−09
GOTERM_BP_DIRECT	Chromosome segregation	14	3.553299492	1.75E−10	0.000000291
GOTERM_BP_DIRECT	G1/S transition of mitotic cell cycle	15	3.807106599	3.32E−09	0.00000554
GOTERM_CC_DIRECT	Nucleoplasm	89	22.58883249	1.26E−09	0.00000169
GOTERM_CC_DIRECT	Condensed chromosome kinetochore	14	3.553299492	2.29E−09	0.00000306
GOTERM_CC_DIRECT	Nucleus	136	34.5177665	6.77E−08	0.0000906
GOTERM_CC_DIRECT	Chromosome, centromeric region	10	2.538071066	0.00000043	0.000576
GOTERM_CC_DIRECT	Kinetochore	11	2.791878173	0.00000104	0.001390211
GOTERM_MF_DIRECT	DNA binding	52	13.19796954	0.000106	0.150583673
GOTERM_MF_DIRECT	Protein binding	190	48.22335025	0.000124	0.175586276
GOTERM_MF_DIRECT	Chromatin binding	19	4.822335025	0.000252	0.356042613
GOTERM_MF_DIRECT	DNA helicase activity	5	1.269035533	0.000804	1.132060211
GOTERM_MF_DIRECT	ATP binding	43	10.91370558	0.002267608	3.161395589

DOI: 10.7717/peerj.9362/table-1

Table 2:

Top 10 KEGG pathway enrichment results.

Category	Term	Count	%	P-Value	Genes	FDR
PDL1 positive group
KEGG_PATHWAY	Arrhythmogenic right ventricular cardiomyopathy (ARVC)	7	2.904564315	0.001300189	LAMA2, ITGA9, CACNA2D1, RYR2, ITGA10, CACNA2D3, CTNNA3	1.414211029
KEGG_PATHWAY	Cell adhesion molecules (CAMs)	7	2.904564315	0.019094239	NCAM2, ITGA9, SELP, CDH15, CD22, CLDN22, HLA-DQA1	19.02707303
KEGG_PATHWAY	PPAR signaling pathway	5	2.074688797	0.024587412	LPL, SLC27A1, OLR1, FABP3, ANGPTL4	23.85529626
KEGG_PATHWAY	Complement and coagulation cascades	5	2.074688797	0.024587412	KNG1, CD55, CR2, F3, CFD	23.85529626
KEGG_PATHWAY	ECM-receptor interaction	5	2.074688797	0.045850772	LAMA2, ITGA9, ITGA10, CHAD, THBS4	40.17953232
PDL1 negative group
KEGG_PATHWAY	DNA replication	10	2.538071066	4.44E−09	RFC3, RFC4, POLD2, PCNA, POLA1, MCM2, MCM3, MCM5, CM6, RPA3	0.00000536
KEGG_PATHWAY	Cell cycle	15	3.807106599	1.25E−08	E2F2, SKP2, TTK, SMAD2, MCM2, MCM3, MCM5, MCM6, CCNE2, CDC45, CDKN2A, PCNA, BUB1, BUB1B, ORC1	0.0000151
KEGG_PATHWAY	Mismatch repair	5	1.269035533	0.000497	RFC3, RFC4, POLD2, PCNA, RPA3	0.598123244
KEGG_PATHWAY	Nucleotide excision repair	6	1.52284264	0.001008028	RFC3, RFC4, POLD2, PCNA, GTF2H4, RPA3	1.210410161
KEGG_PATHWAY	p53 signaling pathway	6	1.52284264	0.004883612	CCNE2, CDKN2A, SERPINB5, RPRM, PERP, GTSE1	5.740021178

DOI: 10.7717/peerj.9362/table-2

GSEA enrichment analysis and validation

The results were validated through GSEA analysis after all the DEGs were considered. The GSEA results also provided the KEGG results. However, a new pathway enrichment was observed in the PDL1-positive group, namely the T-cell receptor pathway, which was not observed in KEGG results of DEGs (Fig. 3).

Construction of PPI network and analysis of hub genes

The STRING database consists of known and predicted PPIs. The interactions include direct (physical) and indirect (functional) associations. To explore the expression relationships between the DEGs, a PPI network was constructed in our study. Our results showed that the PPI network consisted of 570 nodes and 2937 edges (Fig. 4A). The average node degree was 10.3 and the average local clustering coefficient was 0.404 (PPI enrichment p value: <1.0e−16). In addition, the MNC algorithm was used to screen and identify hub genes that might be key genes with crucial regulatory functions. The top 10 hub genes, BUB1B, CDC45, BUB1, TTK, BRCA1, TOP2A, NDC80, RFC4, MCM2, and DTL, were identified in our study (Fig. 4B). The results were ranked using the MNC method, as shown in Table 3.

Analysis of the mechanism of hub genes in the TCGA database

To prevent the limitations caused by using a single GEO database, our results were verified using the TCGA and GTEx databases. First, comparative analysis of hub gene expression in lung adenocarcinoma and adjacent normal tissue was performed in our study, and our results showed that the expression levels of BUB1B, CDC45, BUB1, TTK, TOP2A, NDC80, MCM2, and DTL were significantly higher in lung adenocarcinoma tissues than in adjacent normal lung tissues (P < 0.01). However, although the expression levels of BRCA1 and RFC4 were higher in lung adenocarcinoma tissues than in adjacent normal tissues, the differences were not significant (Fig. 5).

In addition, the relationship between hub genes and pathological staging were analyzed. The results showed that BUB1B (F value = 5.22, p = 0.00148), CDC45 (F value = 2.86, P = 0.0364), BUB1 (F value = 5.22, P = 0.00149), TTK (F value = 5.06, P = 0.00185), BRCA1 (F value = 4.9, P = 0.0023), TOP2A (F value = 2.88, P = 0.0354), NDC80 (F value = 3.58, P = 0.014), MCM2 (F value = 2.78, P = 0.0407), and DTL (F value = 5.97, P = 0.000535) were positively correlated with pathological staging, and the differences were statistically significant. However, regarding the correlation between RFC4 and pathological staging, although RFC4 was positively correlated with pathological staging, the expression of RFC4 did not differ significantly between the two groups (F value = 1.82, P = 0.142; Fig. 6).

Table 3:

Top 10 hub genes ranked using the MNC method.

Rank	Name	Score
1	BUB1B	81
2	CDC45	79
3	BUB1	78
3	TTK	78
5	BRCA1	75
6	TOP2A	73
7	NDC80	72
8	RFC4	71
9	MCM2	69
9	DTL	69

DOI: 10.7717/peerj.9362/table-3

Finally, the relationship between the hub genes and prognosis was analyzed. The results showed that patients with lung adenocarcinoma with low expression levels of BUB1B (Log rank P = 3.8e −05, P (HR) = 5.1e −05 ), CDC45 (log rank P = 0.0032, p (HR) = 0.0035), BUB1 (log rank P = 0.0024, p (HR) = 0.0026 ), TTK [log rank P = 0.00029, P (HR) = 0.00034], BRCA1 [log rank P = 0.0035, P (HR) = 0.0038], TOP2A [log rank P = 0.011, P (HR) = 0.012], NDC80 [log rank P = 0.0027, P (HR) = 0.003], MCM2 [log rank P = 0.02, P (HR) = 0.021], and DTL [log rank P = 0.0016, P (HR) = 0.0018] had significantly higher overall survival than those with high gene expression levels (P < 0.05). However, no significant correlation was observed between RFC4 gene expression level and overall survival (log rank P = 0.059; Fig. 7).

Analysis of the mechanism of hub genes in the TCGA database

Furthermore, analysis of the lung adenocarcinoma was performed using the database TCGA. The results showed that the hub genes had different mutation frequencies, ranging from 1% to 5%. For example, BUB1B (4%), CDC45 (1.9%), and DTL (5%) (Fig. 8A). Moreover, the potential hub genes exhibited different mutation forms in lung adenocarcinoma, which may facilitate further exploration of the function of these hub genes in lung adenocarcinoma. For example, BUB1B mainly exhibited deep deletion, whereas BRCA1 mainly exhibited amplification (Fig. 8B). However, these different mutant forms of hub genes were involved in different signaling pathways influencing the development of lung adenocarcinoma; for example, BRCA1 was mainly involved in the cell cycle and DNA damage response pathways (Figs. 9A and 9B).

Discussion

Lung adenocarcinoma is a highly heterogeneous type of cancer, and its individualized treatment has attracted considerable attention. An increasing number of studies have recently indicated that immunotherapy, especially anti-PD-1 therapy, is a promising strategy for treating lung adenocarcinoma. Some anti-PD-1 antibodies have been successfully commercialized and approved as first- and second-line immunotherapy options for advanced NSCLC in the past few years. Nevertheless, the overall response rate for such immunotherapy is only approximately 20% (Greillier, Tomasini & Barlesi, 2018), and the efficacy of such immunotherapy is affected by PDL1 expression. In particular, PDL1 expression has been proposed as a predictive biomarker, suggesting that it plays a major role in immune regulation in lung adenocarcinoma. Therefore, understanding the effects of PDL1 expression on the biological behavior and the efficacy of immunotherapy in lung adenocarcinoma is crucial. However, the use of IHC alone to assess PDL1 expression cannot provide a complete explanation for the molecular mechanism and enriched pathways underlying the efficacy of immunotherapy. With the development of next-generation sequencing technology and bioinformatics, a novel strategic solution to this problem may be identified (Morganti et al., 2019). Furthermore, previous studies have used bioinformatic analysis to explore the core genes of lung adenocarcinoma and their malignant transformation mechanisms (Yuan et al., 2017; Yeh et al., 2019). However, no research has been conducted on PDL1-positive and PDL1-negative patients. Understanding DEGs and differences in the biological processes and enrichment pathways between PDL1-positive and PDL1-negative patients is crucial. Hence, by using gene bioinformatics analysis, we explored the differences in gene expression profiles and enrichment pathways between the two groups of patients and identified the potential key biomarkers that could be used for predicting disease prognosis in patients. The elucidation of the regulatory mechanism of PDL1 is crucial to improve precision immunotherapy for lung adenocarcinoma.

Our study demonstrated that even in patients with lung adenocarcinoma, differences in gene expression were observed between those in the PDL1-positive and PDL1-negative group, thus suggesting that these two types of patients should be treated differently.

The important findings in our study in the GO terms and KEGG pathway analysis were the differences between the PDL1-positive and PDL1-negative groups of patients with lung adenocarcinoma. Our results reveal that the PDL1-positive group mainly demonstrated enrichment of adhesion-related biological processes and the PPAR signaling pathway. Adhesion-related biological processes have been previously reported to be associated with invasion and metastasis in patients with lung adenocarcinoma (Stevens et al., 2017; Alonso-Nocelo et al., 2018), resulting in relatively high levels of organ and tissue metastases and poor prognosis. Real-world studies have revealed similar results (Wang et al., 2015; Okita et al., 2017). Previous studies have reported that PPAR, an anti-inflammatory molecule with a potent, could promote tumor proliferation, angiogenesis, inflammation, and metastasis in lung adenocarcinoma (Reka et al., 2011; Ammu et al., 2019), suggesting that it is a vital and potential therapeutic target for lung adenocarcinoma. Moreover, Lv & Wang (2015) discovered that the PPAR signaling pathway played a major role in malignant transformation of cells among nonsmoking patients with lung adenocarcinoma. These results are consistent with our findings. Although several studies have suggested the relationship between PPAR and lung adenocarcinoma, the role of PPAR in immunotherapy for lung adenocarcinoma has yet to be reported. Moreover, recent studies have demonstrated that PPAR could enhance indoleamine 2,3-dioxgenase-1 (IDO) activity and promote the generation of regulatory T cells in melanoma; hence, the use of PPAR inhibitors could enhance cancer immunotherapy (Poupot et al., 2014). On the basis of our results along with the results of these studies, we hypothesized that the PPAR pathway might play a crucial role in immunotherapy for lung adenocarcinoma. A comprehensive analysis of our results indicated that although PDL1 expression was associated with the immune response pathway, it might have potential to contribute to invasion and metastasis in patients with PDL1-positive lung adenocarcinoma.

Conversely, the results of the PDL1-negative group mainly revealed enrichment of division-related biological processes and repair systems pathways, which indicated that these pathways might be able to repair defects in apoptosis or recombination to reduce the production of neoantigens. Some clinical studies have shown that PDL1-negative patients tend to have lower tumor mutation burdens or fewer neoantigens than PDL1-positive patients (Xia et al., 2017; Chan et al., 2019), which might be related to its involvement in various repair systems. Further research must be conducted to confirm our conclusions.

In addition, the GSEA results were similar to those for the KEGG pathway analysis. More importantly, a notable and novel finding was reported in the PDL1-positive group, namely that related to the T-cell receptor pathway, which has been proved to be crucial for achieving a favorable immunotherapeutic response in NSCLC (Van De Ven & Borst, 2015). This finding suggests that PDL1 might be involved in the regulation of the T-cell pathway; therefore, understanding the effects of PDL1 expression on tumor microenvironmental immune cells and mediation of T-cell pathways is necessary.

In our study, 10 hub genes screened in the GEO database were verified and further analyzed using the TCGA and GTEx databases. Our study further analyzed the prognostic value and possible mechanism of hub genes in lung adenocarcinoma considering their importance.

Several hub genes, such as BUB1B, CDC45, BUB1, TTK, TOP2A, MCM2, NDC80, and DTL, have been reported to be associated with poor prognosis in patients with lung adenocarcinoma (Hayama et al., 2006; He et al., 2019; Liu et al., 2017; Perez-Pea et al., 2017; Song et al., 2018; Sun et al., 2020). Our findings corroborate these results. Another major finding of our study is that BRCA1 is a predictor in patients with lung adenocarcinoma.

BRCA1 encodes a nuclear phosphoprotein that helps maintain genomic stability. It is involved in biological processes such as cell cycle regulation, replication, and mitotic spindle assembly. Previous studies have suggested that BRCA1 overexpression regulates drug response in chemotherapy, is related to the efficacy of EGFR-TKIs, and is prevalent in patients with NSCLC with early disease onset (Reguart et al., 2008; Sun et al., 2018). Recent studies have analyzed the potential prognostic role of BRCA1 in early-stage NSCLC, suggesting that BRCA1 is a predictor of survival in only stage III NSCLC (Hu et al., 2019). Although several studies have demonstrated the role of BRCA1 in NSCLC, the prognostic value and possible mechanism of BRCA1 in lung adenocarcinoma remain unclear. Our study results suggest that BRCA1 overexpression was associated with poor prognosis in lung adenocarcinoma. In particular, our results indicate that BRCA1 was mainly involved in cell cycle and DNA damage responses in lung adenocarcinoma, which might produce new antigens and enhance immune responses. BRCA1 mutation was also reported to be associated with tumor neoantigen production, immune cell invasion, and PDL1 expression in ovarian cancer (Strickland et al., 2016). Overall, these findings suggest that BRCA1 plays a key role in the immunomodulatory pathway of lung adenocarcinoma.

RFC4, also named activator 1, is a protein complex consisting of five subunits measuring 140, 40, 38, 37, and 36 kD. Telomere C-strand (Lagging Strand) Synthesis and E2F-mediated regulation of DNA replication are some of its related pathways. A previously conduced weighted gene co-expression network analysis revealed that it was a prognosis-related biomarker in lung adenocarcinoma (Yi et al., 2020). However, our GEPIA survival analysis showed that RFC4 overexpression was positively correlated with pathological staging but was not associated with poor prognosis in patients with lung adenocarcinoma. Possible reasons for these inconsistent findings are differences in sample selection strategies and algorithms. Accordingly, the prognostic value of RFC4 for lung adenocarcinoma warrants further study.

The present study has several limitations. First, only a single GSE was searched and analyzed in the GEO database. To prevent bias resulting from the analysis of a single data set, we adopted an integrated bioinformatic analysis approach. Second, the main objective of this study was to explore the influence of PDL1 expression on related gene expression profiles in patients with lung adenocarcinoma. Hence, this study was divided into expression in PDL1-positive and PDL1-negative groups; however, no further distinction was made in the PDL1-positive group between patients with high (50%) and low (<1%) levels of PDL1 expression. Different PDL1 expression levels might have caused differences in the expression profiles of genes, which warrants further confirmation. Third, some key genes have been studied in various tumors, including non–small-cell lung cancer. However, this study identified core genes that might be related to or regulated by the expression of PDL1 in lung adenocarcinoma, which will assist in the further discovery of mechanisms for designing novel immunotherapeutic options.

This study revealed the DEGs and different biological pathways between PDL1-positive and PDL1-negative patients with lung adenocarcinoma, speculating that these two types of patients might have different subtypes of lung adenocarcinoma. The identification and verification of hub genes through integrated bioinformatic analysis revealed that they related to immune response pathways and prognosis in patients with lung adenocarcinoma. In particular, they had different mutations and were involved in different pathways in lung adenocarcinoma.

Conclusions

PDL1-positive lung adenocarcinomaand PDL1-negative lung adenocarcinoma might be different subtypes of lung adenocarcinoma. Potential hub genes might be involved in PDL1 regulatory pathways, and further research is warranted to reveal new mechanisms underlying the regulation of PDL1 expression. This could be of great significance for precision immunotherapy for lung adenocarcinoma.