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. 2026 Feb 20;105(8):e47661. doi: 10.1097/MD.0000000000047661

Identification of shared gene signature between lung cancer and postoperative delirium by transcriptome data analysis

Hua Qian a, Chenghuan Zhang a, Jianlin Wang b,*
PMCID: PMC12928882  PMID: 41731751

Abstract

Postoperative delirium (POD) is clinically associated with a variety of adverse outcomes. The incidence of POD in patients undergoing radical resection of lung cancer is about 5% to 16%, higher than that in patients undergoing other surgeries. However, the molecular mechanisms that specifically lead to POD after radical resection of lung cancer have not been fully elucidated. Gene expression profile datasets of lung cancer (GSE31210) and POD (GSE163943) were downloaded from the Gene Expression Omnibus database. The weighted gene co-expression network analysis (WGCNA) was used to screen key model genes. Differentially expressed genes between disease and control groups were selected. Function enrichment analysis was applied to reveal various biological processes of genes. The hub factors were further selected by protein–protein interaction network, and potential therapeutic drugs were screened using molecular docking. A total of 28 differentially expressed co-expression key module genes shared by lung cancer and POD were obtained. These genes were involved in Fanconi anemia pathway, glycosaminoglycan degradation, and pentose phosphate pathway. A predicted protein–protein interaction network was constructed, which contained 7 proteins. CDC20 (cell division cycle protein 20) had a higher degree. Molecular docking revealed that the drug ergotamine had the lowest docking binding energy with the target protein. Fanconi anemia pathway, glycosaminoglycan degradation and pentose phosphate pathway may play a key role in the pathogenesis of POD after lung cancer surgery via. Ergotamine may serve as a candidate drug for lung cancer-related POD.

Keywords: lung cancer, molecular docking, pathway, postoperative delirium, weighted gene co-expression network analysis

1. Introduction

Delirium is an acute, transient state of confusion, which is a disorder of attention and consciousness that develops rapidly.[1] Postoperative delirium (POD) is clinically associated with a variety of adverse outcomes, including prolonged mechanical ventilation, longer intensive care unit stay, longer length of hospital stay, increased medical costs, and increased mortality.[2,3] Despite our growing understanding of the epidemiology of POD, no laboratory test has contributed to the diagnosis or remission of delirium. Rather, it is entirely a clinical diagnosis. In addition, the etiology and pathogenesis are not well understood, which poses a challenge for biomarker discovery and clinical trials.[4,5]

Now lung cancer remains the leading cause of cancer-related deaths worldwide.[6] Presently, surgery is still the mainstay treatment for lung cancer.[7] It has been reported that the incidence of POD in patients undergoing radical resection of lung cancer is about 5% to 16%,[2,8] which is slightly higher than that in patients undergoing other surgeries. Moreover, most individuals (about 65%) diagnosed with lung cancer are aged ≥60 years.[9,10] In recent years, the cumulative risk of POD in elderly surgical patients has been as high as 15% to 53%.[11-13] Therefore, for patients with lung cancer, prevention and timely treatment of POD are very important. However, the molecular mechanisms that specifically lead to POD after radical resection of lung cancer have not been fully elucidated and require further investigation and exploration.

The weighted gene co-expression network analysis (WGCNA) is an effective bioinformatics analysis method to explore the relationship between genes and phenotypes, which can cluster genes into co-expression modules and establish a connection between sample characteristics and gene expression.[14] In this study, WGCNA was used to construct gene co-expression network, and screen key genes. Function enrichment analysis was applied to reveal various biological processes (BPs) of genes. The hub factors were further selected, and potential therapeutic drugs were screened using molecular docking.

2. Materials and methods

2.1. Data collection

Gene expression profile datasets for lung cancer and normal samples (GSE31210 (Ncase = 91; Ncontrol = 65)) as well as POD and normal samples (GSE163943 (Ncase = 4; Ncontrol = 4)) were downloaded from the Gene Expression Omnibus database. The unloaded probes were removed. When a probe corresponded to multiple genes, this probe was removed. If multiple probes corresponded to the same gene, the average expression value of these probes was used as the expression value of gene.

2.2. Construction of WGCNA co-expression network

WGCNA is a systems biology method for identifying co-expressed gene modules, exploring the relationship between gene networks and phenotypes of interest, and studying core genes within the network. In WGCNA analysis, the correlation coefficients between any 2 genes were first calculated, and the weighted values of the correlation coefficients were used to make the connections between genes in the network obey a scale-free network. Then, a hierarchical clustering tree was constructed based on the correlation coefficients between genes. Different branches of the clustering tree represented different gene modules, and different colors represented different modules. Next, module significance was calculated and used to calculate the correlation between disease and modules, and the genes in each module were recorded, with the genes in each module being considered as characteristic genes of the module.

2.3. Identification of signature genes in lung cancer and POD

The modules highly related to lung cancer and POD were selected. The module genes were performed Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using the R package clusterProfiler. The shared signature genes in lung cancer and POD were visualized using the R package VennDiagram.

2.4. Identification and function analysis of differentially expressed genes (DEGs)

Based on the gene expression profile data, the differentially expressed genes (DEGs) between of the disease and control groups were selected using the wilcox.test in R. In the differential analysis, genes with |log Fold Change (FC)| > 0.5 and P value < .05 were considered to be differentially expressed. To further explore the functional characteristics and biological relevance of the DEGs, function enrichment analysis was performed on the genes using the R package clusterProfiler.

Subsequently, the obtained signature module genes were intersected with DEGs to obtain the differentially expressed module genes. Then these differentially expressed module genes for lung cancer and POD were also intersected, obtaining the common differentially expressed signature genes.

2.5. Transcription factor (TF) regulatory network analysis

The TRRUST v2 database contains human TF-mRNA regulatory relationships, and we selected the TFs that regulated the signature genes to construct a TF-mRNA regulatory network.

2.6. Construction of PPI network to screen hub factors

The STRING database is an online resource that integrates a large amount of known and predicted protein–protein interaction (PPI) information. Based on the identified differentially expressed common signature genes, a PPI network was constructed using the STRING database. A confidence score of 0.4 was used as the minimum interaction score, with all other parameters set to default.

2.7. Molecular docking

According to the results of the PPI network, the hub factors were obtained. The protein structure information was searched in the PDB database, and the protein structure with the highest resolution was downloaded (species: human). The structural information of compounds was downloaded from the ZINC database, and molecular docking was performed using autodock-vina. The conformation with the smallest binding energy was selected, and binding diagram of the protein and the compound was made by using pymol.

3. Results

3.1. Construction of WGCNA co-expression network for lung cancer

WGCNA algorithm was used to construct co-expression modules and identify the modules related to lung cancer. The soft threshold was set to 3 to ensure the overall connectivity of the co-expression modules (Fig. 1A, B). Sixteen co-expression modules were constructed and displayed in different colors (Table 1). The clustering dendrogram clusters genes that shared gene expression patterns in the same color module (Fig. C). The analysis results showed that the cyan and red modules showed the most significantly positive correlation with lung cancer, and the turquoise module showed the most significantly negative correlation with lung cancer (Fig. 1D). These modules were considered as key modules, which contained 142, 274, and 6460 genes, respectively (Figs. 1D–G). Finally, genes in the key models were screened for subsequent analysis based on GS > 0.6 and MM > 0.65.

Figure 1.

Figure 1.

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Co-expression network construction for lung cancer. (A) Scale-free fit index of soft threshold; (B) average connectivity of soft threshold power; (C) gene clustering dendrogram; (D) correlation between different modules and diseases; and (E–G) scatter plot of the featured genes in the module.

Table 1.

The module information in GSE19188.

Module Mumber Cor
Cyan 142 0.585916382
Red 274 0.497564738
Green 294 0.482826641
Tan 165 0.480133221
Yellow 316 0.443863795
Pink 243 0.436399608
Greenyellow 220 0.434664935
Magenta 227 0.434072531
Blue 824 0.389335847
Gray 4119 −0.040772167
Purple 225 −0.239892449
Salmon 160 −0.308613752
Midnightblue 95 −0.325084263
Black 255 −0.372844338
Brown 807 −0.384779517
Turquoise 6460 −0.863120708
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Cor: correlation coefficient between the module eigengene and the clinical trait (lung cancer status).

3.2. Construction of WGCNA co-expression network for POD

The same method was used to construct co-expression modules for POD. The soft threshold was set to 20. Total 57 co-expression modules was obtained (Table 2). The darkgrey and salmon modules showed the most significantly positive correlation with POD, and the blue module showed the most significantly negative correlation with POD. The 3 modules above contained 225, 389 and 2225 genes, respectively (Fig. 2). Finally, genes in the key models with GS > 0.6 and MM > 0.65 were screened for subsequent analysis.

Table 2.

The module information in GSE163943.

Module Number Cor
Farkgrey 225 0.924129044
Salmon 389 0.823771271
Pink 809 0.74978764
Magenta 691 0.740580436
Darkgreen 255 0.718768843
Ivory 84 0.686311159
Orange 224 0.639036709
Brown 1674 0.550726757
Thistle2 52 0.524453133
Saddlebrown 180 0.517353388
Maroon 40 0.477938032
Lightgreen 324 0.469053152
Purple 600 0.46434722
Darkslateblue 62 0.457693412
Darkorange2 68 0.370744676
Darkmagenta 132 0.333287725
Lightpink4 34 0.295317164
Bisque4 63 0.291285449
Darkolivegreen 134 0.271814154
Red 1095 0.271682124
Paleturquoise 166 0.248530491
Skyblue 180 0.247372141
Midnightblue 360 0.200667417
Navajowhite2 41 0.19860121
Lightcyan 354 0.189391735
Gray 403 0.187143051
Royalblue 313 0.153678714
Cyan 381 0.130699158
Plum2 52 0.112119722
Floralwhite 78 0.099625212
Black 1020 0.075085223
Skyblue3 104 0.071345899
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Cor: correlation coefficient between the module eigengene and the clinical trait (lung cancer status).

Figure 2.

Figure 2.

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Co-expression network construction for postoperative delirium. (A) Scale-free fit index of soft threshold; (B) average connectivity of soft threshold power; (C) gene clustering dendrogram; (D) correlation between different modules and diseases; and (E–G) scatter plot of the featured genes in the module.

3.3. Signature genes shared between lung cancer and POD

In GSE19188 dataset, the signature genes in the cyan module were mainly involved in protein digestion and absorption pathway, the genes in the red module were mainly involved in renal cell carcinoma pathway, and the genes in the turquoise module were mainly involved in cell cycle pathways (Fig. 3A).

Figure 3.

Figure 3.

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KEGG enrichment analysis of module signature genes for (A) lung cancer and (B) postoperative delirium. (C) Shared signature genes of lung cancer and postoperative delirium. KEGG =Kyoto Encyclopedia of Genes and Genomes.

In GSE163943 dataset, the signature genes of darkgrey module mainly participated in fatty acid metabolism pathway, while of blue module mainly participated in the pathways of viral proteins, and cytokine-receptor interaction. The genes of salmon module did not show significant enrichment analysis results (Fig. 3B).

Subsequently, venn intersection analysis was used to find the shared signature genes of cyan and turquoise modules for lung cancer and darkgrey and blue modules for POD. It was found that 64 genes overlated in the negative correlation module of lung cancer and POD. These genes may be associated with the pathogenesis of lung cancer and POD (Fig. 3C).

3.4. Identification of DEGs for lung cancer and POD

In GSE19188 (lung cancer dataset), 4099 DEGs were obtained, including 1804 up-regulated genes and 2295 down-regulated genes (Fig. 4A). In order to highlight the DEGs in different samples, the top 30 genes with the largest FC were selected and their expression was visualized through heatmaps (Fig. 4B).

Figure 4.

Figure 4.

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(A, B, D, E) Volcano map and heat map of differentially expressed genes in (A, B) lung cancer and (D, E) postoperative delirium. (C, F) Enrichment analysis of differentially expressed genes in (C) lung cancer and (F) postoperative delirium.

Gene Ontology enrichment analysis showed that, in terms of BP, DEGs were mainly concentrated in the leukocyte migration, cell chemotaxis, and positive regulation of response to external stimuli. In terms of KEGG pathway, DEGs were mainly involved in DNA replication, cell cycle and complement, and coagulation cascades (Fig. 4C).

In GSE163943 (POD dataset), 1239 DEGs were identified, including 557 up-regulated genes and682 down-regulated genes (Fig. 4D). The top 30 genes with largest FC are shown in Figure 4E. Function analysis revealed that these DEGs were mainly associated with molecular functions of scavenger receptor activity, oxidoreductase activity, and disulfide bond oxidoreductase activity. In terms of cellular components, these genes were mainly concentrated in protein phosphatase type 1 complex, sarcomas, and early phagosomes (Fig. 4F). In terms of BPs and KEGG pathway, no significant enrichment results were obtained.

3.5. Differentially expressed signature genes shared between lung cancer and POD

The signature genes obtained from co-expression module and DEGs obtained from lung cancer and POD were investigated jointly. Total 1347 differentially expressed co-expression key module genes specific to lung cancer were obtained, and 683 differentially expressed co-expression key module genes specific to POD were obtained. Finally, 28 differentially expressed co-expression key module genes shared by lung cancer and POD were obtained (Fig. 5A).

Figure 5.

Figure 5.

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(A) Shared differentially expressed signature genes in lung cancer and POD. (B) Enrichment analysis of differentially expressed shared signature genes in lung cancer and POD. POD = postoperative delirium.

In order to further explore the functional characteristics and biological correlation of the differentially expressed shared genes in lung cancer and POD, Rpackage clusterProfiler was used for enrichment analysis. The results showed that genes were mainly enriched in meiosis recombination intermediates, meiosis chromosome separation, and chromosome separation associated BP. In terms of KEGG pathway, genes were mainly involved in Fanconi anemia pathway, glycosaminoglycan degradation, and pentose phosphate pathway (Fig. 5B).

3.6. Network analysis

In order to study the role and regulatory relationship of shared signature genes in lung cancer and POD, TFs interacting with genes were screened from TRRUST v2 database. Thirteen TFs, such as PHF8 (PHD finger protein 8), YBX1 (Y-box binding protein 1) and EGR1 (early growth response 1) were identified and the TF-mRNA regulatory network was constructed, as shown in Figure 6A.

Figure 6.

Figure 6.

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(A) Transcription factor-mRNA regulatory network (mRNA are shown in yellow; transcription factors are shown in orange). (B) PPI network. (C) Schematic diagram of molecular docking between target protein and drug (green represents target protein; cyan represents small drug molecules; amino acid residues linked to drug small molecules are represented in red; yellow represents bonded hydrogen bonds). PPI = predicted protein-protein interaction.

Additionally, we also constructed a PPI network, which contained 7 proteins (Fig. 6B). CDC20 (cell division cycle protein 20), EME1 (essential meiotic structure-specific endonuclease 1), and PGM5 (phosphoglucomutase 5) had higher degrees.

3.7. Molecular docking

The gene CDC20 with the highest connectivity with other genes was screened, and its expressed protein was subjected to molecular docking. The result showed that the drug ergotamine had the lowest docking binding energy with the target protein, and the binding diagram of protein and compound was drawn by pymol (Fig. 6C).

4. Discussion

Postoperative delirium (POD) is a frequent and serious complication following major surgery, particularly in elderly patients and those with underlying malignancies. Lung cancer patients are especially vulnerable due to systemic inflammation, malnutrition, anemia, and hypoxia caused by both the tumor and surgical intervention. Although no direct causal link has been firmly established between lung cancer and POD, clinical observations have noted an increased incidence of POD in this population.[15]These observations suggest the need to explore possible molecular connections between lung cancer pathophysiology and susceptibility to POD.

In this study, we employed a comprehensive bioinformatics approach – combining WGCNA, PPI network construction, pathway enrichment analysis, and molecular docking – to identify genes and pathways potentially linking lung cancer and POD. We identified 28 DEGs that were common to both lung cancer and POD-associated profiles. Functional enrichment analysis revealed that these genes were involved in several biologically relevant pathways, including the Fanconi anemia pathway, glycosaminoglycan degradation, and the pentose phosphate pathway.

The Fanconi anemia pathway is essential for DNA repair and hematopoietic function. Anemia, a common complication in lung cancer patients, has been associated with impaired cognitive performance,[16,17] particularly due to chronic cerebral hypoxia.[18,19] Given the sensitivity of brain tissue to oxygen deprivation, dysregulation in the Fanconi anemia pathway may contribute to neuronal vulnerability and the development of POD. Similarly, glycosaminoglycans (GAGs) play a vital role in maintaining blood-brain barrier integrity, modulating neuroinflammation, and regulating neuroregeneration. Disruption of GAG metabolism has been associated with increased neuroinflammatory responses, which are thought to be central to the pathogenesis of POD.[20–23] Delirium is a neurobehavioral syndrome caused by dysregulation of neuronal activity secondary to systemic disturbances,[5] thus we speculated that glycosaminoglycan degradation may be involved in the pathogenesis of POD. When functioning normally, the pentose phosphate pathway is neuroprotective. Microglia and immune cells produce high concentrations of glucose-6-phosphate dehydrogenase, the rate-limiting enzyme of the pentose phosphate pathway. When microglia glucose-6-phosphate dehydrogenase is over-activated, it can lead to excess reactive oxygen species.[24] Elevated levels of reactive oxygen species eventually lead to mitochondrial apoptosis. Previous studies have shown that dysregulation of redox homeostasis can lead to POD.[4] Taken together, these key genes may play a key role in the pathogenesis of POD after lung cancer surgery via these pathways.

To further explore the regulatory mechanisms behind the key genes, we constructed a protein–protein interaction (PPI) network and identified CDC20 as a central hub. CDC20 is a well-characterized regulator of mitotic progression and is commonly overexpressed in various malignancies, including lung cancer.[25] Its abnormal expression is closely related to the progress and poor prognosis of malignant tumor, including lung cancer, making it become a kind of possible tumor therapeutic targets.[26,27] Nevertheless, its role in POD has not been reported to our knowledge. Interestingly, its TF PHF8 has been found to be associated with neuronal differentiation and function.[28] PHF8 histone demethylase deficiency causes cognitive impairments.[29] Thus, we speculated that CDC20 may be a key biomarker of POD induced by lung cancer surgery.

Despite POD is a known condition for many years, little is understood about the pathophysiology of the disorder and there is no specific cure. In this study, we predicted the candidate drug through molecular docking. CDC20 was used as target protein for molecular docking and ergotamine that had the lowest docking binding energy with the target protein was predicted to be the potential drugs. In the second half of the 19th century, controlled doses of ergotamine was used effectively to treat severe and acute migraines.[30] In more recent times, it has been modified for pharmaceutical use, used for neurological disorders.[31] Its function in POD needs further experiment validation.

In conclusion, our study identified 28 shared DEGs between lung cancer and POD. These genes are enriched in key pathways related to DNA repair, neuroinflammation, and oxidative stress. CDC20 emerged as a potential biomarker linking tumor proliferation and neurocognitive dysfunction, and ergotamine was identified as a candidate drug through molecular docking. Although the relationship between lung cancer and POD remains complex and multifactorial, our findings offer a novel molecular perspective and a foundation for further research into cancer-related delirium.

This study is based on publicly available gene expression data and in silico analyses. The findings are exploratory and require experimental validation in clinical or preclinical models. Furthermore, while we suggest possible systemic effects of lung cancer on the central nervous system, prospective studies are needed to directly assess the impact of tumor burden, treatment regimens, and perioperative factors on POD risk. Future research should also explore the therapeutic potential of targeting CDC20 and other key regulators in the prevention or management of POD.

Author contributions

Data curation: Hua Qian, Jianlin Wang.

Formal analysis: Hua Qian, Chenghuan Zhang.

Funding acquisition: Jianlin Wang.

Project administration: Chenghuan Zhang.

Software: Hua Qian.

Supervision: Hua Qian.

Validation: Jianlin Wang.

Writing – original draft: Chenghuan Zhang.

Writing – review & editing: Jianlin Wang.

Abbreviations:

BP
biological process
DEG
differentially expressed gene
KEGG
Kyoto Encyclopedia of Genes and Genomes
POD
postoperative delirium
PPI
predicted protein–protein interaction
STRING
search tool for the retrieval of interacting genes

We thanked the support from Young Talent Development Plan of Changzhou Health Commission (CZQM2020074); Top Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022CZBJ070); Science and Technology Project of Changzhou Health Commission (ZD202313).Clinical Program of Changzhou Medical Center, Nanjing Medical University (CMCC202401)

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Qian H, Zhang C, Wang J. Identification of shared gene signature between lung cancer and postoperative delirium by transcriptome data analysis. Medicine 2026;105:8(e47661).

Contributor Information

Hua Qian, Email: 806829735@qq.com.

Chenghuan Zhang, Email: zch2980799@163.com.

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