Discovering novel lung cancer associated antigens and the utilization of their autoantibodies in detection of lung cancer

Elucidating the prognostic and therapeutic significance of TOP2A in various malignancies

Topoisomerase IIα (TOP2A) is a crucial enzyme that plays a vital role in DNA replication and transcription mechanisms. Dysregulated expression of TOP2A has been associated with various malignancies, including hepatocellular carcinoma, prostate cancer, colon cancer, lung cancer and breast cancer. In this review, we summarized the prognostic relevances of TOP2A in various types of cancer. The increased expression of TOP2A has been linked to resistance to therapy and reduced survival rates. Therefore, evaluating TOP2A levels could assist in identifying patients who may derive advantages from molecular targeted therapy. The amplification of TOP2A has been linked to a positive response to chemotherapy regimens that contain anthracycline. Nevertheless, the overexpression of TOP2A also indicates a heightened likelihood of disease recurrence and unfavorable prognosis. The prognostic significance of TOP2A has been extensively studied in various types of cancer. The increased expression of TOP2A is associated with poor clinical outcomes, indicating its potential as a valuable biomarker for assessing risk and stratifying treatment in these malignancies. However, further investigation is needed to elucidate the underlying mechanisms by which TOP2A influences cancer progression and to explore its potential as a therapeutic target.

Prognostic impact of metformin in solid cancer patients receiving immune checkpoint inhibitors: novel evidences from a multicenter retrospective study

Objective: Metformin as a common antidiabetic drug, has recently found to exert its anti-cancer and immunomodulatory effect in numerous preclinical studies. This study aims to clarify the prognostic impact of metformin use in solid cancer patients receiving immune checkpoint inhibitors (ICIs). Methods: A retrospective cohort enrolling 516 solid cancer patients who received ICI-based therapy between 2018 and 2023 at three hospitals was analyzed. The primary endpoints included overall survival (OS) and progression-free survival (PFS). In addition, a bioinformatics analysis based on TCGA and GSE cohort was performed to investigate the prognostic significance of metformin target genes (MTGs) and their correlation with immune infiltration in non-small cell lung cancer (NSCLC) patients. Results: In the entire cohort, a total of 76 patients received metformin before and/or during ICI therapy. The global analysis demonstrated that metformin use was unrelated with the OS (p = 0.064) and PFS (p = 0.059) of ICI-treated cancer patients, which was confirmed in the subgroups of esophagus, hepatobiliary or pancreatic cancer (all p > 0.05). However, metformin use was significantly correlated with better OS (p = 0.012) and PFS (p = 0.005) in ICI-treated lung cancer patients. Metformin use was also identified as an independent favorable prognostic factor for these patients. The bioinformatics analysis identified five favorable prognostic MTGs (RPS6KA5, RORA, SH3BP5, NUPR1, and CD40LG) for NSCLC patients, all of which was downregulated in lung cancer tissues as compared with normal tissues. The expressions of five MTGs not only could effectively stratify the OS of NSCLC patients, but also was correlated with infiltration of immune cells such as CD4+ and CD8+ T cells. Conclusion: Metformin use was significantly correlated with better OS and PFS in ICI-treated lung cancer patients. MTGs has the potential to serve as novel clinical biomarkers or druggable targets for cancer immunotherapy. Considering study limitations, the actual impact of metformin use on ICI therapy needs to be clarified by more clinical trials.

Humoral immune response to tumor-associated antigen Ubiquilin 1 (UBQLN1) and its tumor-promoting potential in lung cancer

BACKGROUND

This study aims to investigate the expression of UBQLN1 in lung cancer (LC) tissue and the diagnostic capability of autoantibody to UBQLN1 (anti-UBQLN1) in the detection of LC and the discrimination of pulmonary nodules (PNs).

METHODS

Sera from 798 participants were used to discover and validate the level of autoantibodies via HuProt microarray and Enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was applied to establish model. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic potential. Immunohistochemistry was performed to detect UBQLN1 expression in 88 LC tissues and 88 para-tumor tissues. qRT-PCR and western blotting were performed to detect the expression of UBQLN1 at the mRNA and protein levels, respectively. Trans-well assay and cell counting kit-8 (CCK-8) was used to investigate the function of UBQLN1.

RESULTS

Anti-UBQLN1 was identified with the highest fold change by protein microarray. The level of anti-UBQLN1 in LC patients was obviously higher than that in NC or patients with benign lung disease of validation cohort 1 (P<0.05). The area under the curve (AUC) of anti-UBQLN1 was 0.610 (95%CI: 0.508-0.713) while reached at 0.822 (95%CI: 0.784-0.897) when combining anti-UBQLN1 with CEA, CYFRA21-1, CA125 and three CT indicators (vascular notch sign, lobulation sign and mediastinal lymph node enlargement) in the discrimination of PNs. UBQLN1 protein was overexpressed in lung adenocarcinoma (LUAD) tissues compared to para-tumor tissues. UBQLN1 knockdown remarkably inhibited the migration, invasion and proliferation of LUAD cell lines.

CONCLUSIONS

Anti-UBQLN1 might be a potential biomarker for the diagnosis of LC and the discrimination of PNs.

The role of MAPK, notch and Wnt signaling pathways in papillary thyroid cancer: Evidence from a systematic review and meta-analyzing microarray datasets employing bioinformatics knowledge and literature

Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the role of PTC in pathogenesis requires studying the various gene expressions to find out which particular molecular biomarkers will be helpful. The authors conducted a comprehensive search on the PubMed microarray database and a meta-analysis approach on the remaining ones to determine the differentially expressed genes between PTC and normal tissues, along with the analyses of overall survival (OS) and recurrence-free survival (RFS) rates in patients with PTC. We considered the associated genes with MAPK, Wnt, and Notch signaling pathways. Two GEO datasets have been included in this research, considering inclusion and exclusion criteria. Nineteen genes were found to have higher differences through the meta-analysis procedure. Among them, ten genes were upregulated, and nine genes were downregulated. The expression of 19 genes was examined using the GEPIA2 database, and the Kaplan-Meier plot statistics were used to analyze RFS and the OS rates. We discovered seven significant genes with the validation: PRICKLE1, KIT, RPS6KA5, GADD45B, FGFR2, FGF7, and DTX4. To further explain these findings, it was discovered that the mRNA expression levels of these seven genes and the remaining 12 genes were shown to be substantially linked with the results of the experimental literature investigations on the PTC. Our research found nineteen panels of genes that could be involved in the PTC progression and metastasis and the immune system infiltration of these cancers.

Cooperation of MLL1 and Jun in controlling H3K4me3 on enhancers in colorectal cancer

BACKGROUND

Enhancer dysregulation is one of the important features for cancer cells. Enhancers enriched with H3K4me3 have been implicated to play important roles in cancer. However, their detailed features and regulatory mechanisms have not been well characterized.

RESULTS

Here, we profile the landscape of H3K4me3-enriched enhancers (m3Es) in 43 pairs of colorectal cancer (CRC) samples. M3Es are widely distributed in CRC and averagely possess around 10% of total active enhancers. We identify 1322 gain variant m3Es and 367 lost variant m3Es in CRC. The target genes of the gain m3Es are enriched in immune response pathways. We experimentally prove that repression of CBX8 and RPS6KA5 m3Es inhibits target gene expression in CRC. Furthermore, we find histone methyltransferase MLL1 is responsible for depositing H3K4me3 on the identified Vm3Es. We demonstrate that the transcription factor AP1/JUN interacts with MLL1 and regulates m3E activity. Application of a small chemical inhibitor for MLL1 activity, OICR-9429, represses target gene expression of the identified Vm3Es, enhances anti-tumor immunity and inhibits CRC growth in an animal model.

CONCLUSIONS

Taken together, our study illustrates the genome-wide landscape and the regulatory mechanisms of m3Es in CRC, and reveals potential novel strategies for cancer treatment.

Autoantibodies of inflammatory cytokines as serum biomarkers in OSA patients

As many as 90% of patients with obstructive sleep apnea (OSA) may be undiagnosed. It is necessary to explore the potential value of autoantibodies against CRP, IL-6, IL-8 and TNF-α in the diagnosis of OSA. ELISA was performed to detect the level of autoantibodies against CRP, IL-6, IL-8 and TNF-α in sera from 264 OSA patients and 231 normal controls (NCs). The expression level of autoantibodies against CRP, IL-6 and IL-8 in OSA were significantly higher than that in NC while the level of anti-TNF-α was lower in OSA than that in NC. The per SD increment of anti-CRP, anti-IL-6 and anti-IL-8 autoantibodies were significantly associated with a 430%, 100% and 31% higher risk for OSA, respectively. The AUC of anti-CRP was 0.808 (95% CI: 0.771-0.845) when comparing OSA with NC, while the AUC increased to 0.876 (95% CI: 0.846-0.906) combining four autoantibodies. For discrimination of severe OSA versus NC and non-severe OSA versus NC, the AUC for four autoantibodies combination was 0.885 (95% CI: 0.851-0.918) and 0.876 (95% CI: 0.842-0.913). This study revealed the association between autoantibodies against inflammatory factors and OSA, and the combination of autoantibodies against CRP, IL-6, IL-8 and TNF-α may function as novel biomarker for monitoring the presence of OSA.

Sequencing-based network analysis provides a core set of genes for understanding hemolymph immune response mechanisms against Poly I:C stimulation in Amphioctopus fangsiao

Aquatic viruses can spread rapidly and widely in seawater for their high infective ability. Polyinosinic-polycytidylic acid (Poly I:C), a viral dsRNA analog, is an immunostimulant that has been proved to activate various immune responses of immune cells in invertebrate. Hemolymph is a critical site that host immune response in invertebrates, and its transcriptome information obtained from Amphioctopus fangsiao stimulated by Poly I:C is crucial for understanding the antiviral molecular mechanisms of this species. In this study, we analyzed gene expression data in A. fangsiao hemolymph tissue within 24 h under Poly I:C stimulation and found 1082 and 299 differentially expressed genes (DEGs) at 6 and 24 h, respectively. Union set (1,369) DEGs were selected for subsequent analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were carried out for identifying DEGs related to immunity. Several significant immune-related terms and pathways, such as toll-like receptor signaling pathways term, inflammatory response term, TNF signaling pathway, and chemokine signaling pathway were identified. A protein-protein interaction (PPI) network was constructed for examining the relationships among immune-related genes. Finally, 12 hub genes, including EGFR, ACTG1, MAP2K1, and other nine hub genes, were identified based on the KEGG enrichment analysis and PPI network. The quantitative RT-PCR (qRT-PCR) was used to verify the expression profile of 12 hub genes. This research provides a reference for solving the problem of high mortality of A. fangsiao and other mollusks and provides a reference for the future production of some disease-resistant A. fangsiao.

Autoantibody signatures discovered by HuProt protein microarray to enhance the diagnosis of lung cancer

This study aims to discover novel autoantibodies against tumor-associated antigens (TAAs) and establish diagnostic models for assisting in the diagnosis of lung cancer and discrimination of pulmonary nodules (PNs). Ten autoantibodies to TAAbs (TAAbs) were discovered by means of protein microarray and their serum level was also higher in 212 LC patients than that in 212 NC of validation cohort 1 (P < 0.05). The model 1 comprising 4 TAAbs and CEA reached an AUC of 0.813 (95%CI: 0.762-0.864) for diagnosing LC from normal individuals. Five TAAbs existed a significant difference between 105 malignant pulmonary nodules (MPNs) and 105 benign pulmonary nodules (BPNs) patients in validation cohort 2 (P < 0.05). Model 2 could distinguish MPNs from BPNs with an AUC of 0.845. High-throughput protein microarray is an efficient approach in discovering novel TAAbs which could be used as biomarkers in lung cancer diagnosis.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

Transcriptome profiling based on protein-protein networks provides a core set of genes for understanding blood immune response mechanisms against LPS stress in Amphioctopus fangsiao

Gram-negative bacteria are significant pathogens in the ocean, posing serious threats to marine organisms. Lipopolysaccharide (LPS) is a characteristic chemical constituent in Gram-negative bacteria that can be recognized by the pattern recognition receptor (PRR) of immune cells. This system is often used to simulate the invasion of bacteria. Blood is a transport channel for immune cells, and its transcriptome information obtained from Amphioctopus fangsiao stimulated by LPS is essential for understanding the antibacterial biological mechanisms of this species. In this study, we analyzed the gene expression profiles of A. fangsiao blood within 24h under LPS stress and found 778 and 561 differentially expressed genes (DEGs) at 6 and 24h, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed to search for immune-related DEGs. The relationships among immune genes were examined by constructing a protein-protein interaction (PPI) network. Finally, 16 hub genes were identified based on the PPI network and KEGG enrichment analysis. The expression profiles of these genes were verified using quantitative RT-PCR (qRT-PCR). This research provides valuable resources for the healthy culture of A. fangsiao and helps us understand the molecular mechanisms of innate immunity.

Recent Advances in DNA Vaccines against Lung Cancer: A Mini Review

Lung cancer is regarded as the major causes of patient death around the world. Although the novel tumor immunotherapy has made great progress in the past decades, such as utilizing immune checkpoint inhibitors or oncolytic viruses, the overall 5-year survival of patients with lung cancers is still low. Thus, development of effective vaccines to treat lung cancer is urgently required. In this regard, DNA vaccines are now considered as a promising immunotherapy strategy to activate the host immune system against lung cancer. DNA vaccines are able to induce both effective humoral and cellular immune responses, and they possess several potential advantages such as greater stability, higher safety, and being easier to manufacture compared to conventional vaccination. In the present review, we provide a global overview of the mechanism of cancer DNA vaccines and summarize the innovative neoantigens, delivery platforms, and adjuvants in lung cancer that have been investigated or approved. Importantly, we highlight the recent advance of clinical studies in the field of lung cancer DNA vaccine, focusing on their safety and efficacy, which might accelerate the personalized design of DNA vaccine against lung cancer.

A Diagnostic Model With IgM Autoantibodies and Carcinoembryonic Antigen for Early Detection of Lung Adenocarcinoma

Immunoglobulin M (IgM) autoantibodies, as the early appearing antibodies in humoral immunity when stimulated by antigens, might be excellent biomarkers for the early detection of lung cancer (LC). We aimed to develop a multi-analyte integrative model combining IgM autoantibodies and a traditional tumor biomarker that could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of lung adenocarcinoma (LUAD). A customized protein array based on cancer driver genes was constructed and applied in the discovery cohort consisting of 68 LUAD patients and 68 normal controls (NCs); 31 differentially expressed IgM autoantibodies were identified. The top 5 candidate IgM autoantibodies [based on the area under the receiver operating characteristic curve (AUC) ranking], namely, TSHR, ERBB2, survivin, PIK3CA, and JAK2, were validated in the validation cohort using enzyme-linked immunosorbent assay (ELISA), which included 147 LUAD samples, 72 lung squamous cell carcinoma (LUSC) samples, 44 small cell lung carcinoma (SCLC) samples, and 147 NCs. These indicators presented diagnostic capacity for LUAD, with AUCs of 0.599, 0.613, 0.579, 0.601, and 0.633, respectively (p < 0.05). However, none of them showed a significant difference between the SCLC and NC groups, and only the IgM autoantibody against JAK2 showed a higher expression in LUSC than in NC (p = 0.046). Through logistic regression analysis, with the five IgM autoantibodies and carcinoembryonic antigen (CEA), one diagnostic model was constructed for LUAD. The model yielded an AUC of 0.827 (sensitivity = 56.63%, specificity = 93.98%). The diagnostic efficiency was superior to that of either CEA (AUC = 0.692) or IgM autoantibodies alone (AUC = 0.698). Notably, the accuracy of this model in early-stage LUAD reached 83.02%. In conclusion, we discovered and identified five novel IgM indicators and developed a multi-analyte model combining IgM autoantibodies and CEA, which could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of LUAD.

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Substantial studies indicate that autoantibodies to tumor-associated antigens (TAAbs) arise in early stage of lung cancer (LC). However, since single TAAbs as non-invasive biomarkers reveal low diagnostic performances, a panel approach is needed to provide more clues for early detection of LC. In the present research, potential TAAbs were screened in 150 serum samples by focused protein array based on 154 proteins encoded by cancer driver genes. Indirect enzyme-linked immunosorbent assay (ELISA) was used to verify and validate TAAbs in two independent datasets with 1,054 participants (310 in verification cohort, 744 in validation cohort). In both verification and validation cohorts, eight TAAbs were higher in serum of LC patients compared with normal controls. Moreover, diagnostic models were built and evaluated in the training set and the test set of validation cohort by six data mining methods. In contrast to the other five models, the decision tree (DT) model containing seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1), built in the training set, yielded the highest diagnostic value with the area under the receiver operating characteristic curve (AUC) of 0.897, the sensitivity of 94.4% and the specificity of 84.9%. The model was further assessed in the test set and exhibited an AUC of 0.838 with the sensitivity of 89.4% and the specificity of 78.2%. Interestingly, the accuracies of this model in both early and advanced stage were close to 90%, much more effective than that of single TAAbs. Protein array based on cancer driver genes is effective in screening and discovering potential TAAbs of LC. The TAAbs panel with TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1 is excellent in early detection of LC, and they might be new target in LC immunotherapy.

Anti-Cancer Auto-Antibodies: Roles, Applications and Open Issues

Auto-antibodies are classically associated with autoimmune diseases, where they are an integral part of diagnostic panels. However, recent evidence is accumulating on the presence of auto-antibodies against single or selected panels of auto-antigens in many types of cancer. Auto-antibodies might initially represent an epiphenomenon derived from the inflammatory environment induced by the tumor. However, their effect on tumor evolution can be crucial, as is discussed in this paper. It has been demonstrated that some of these auto-antibodies can be used for early detection and cancer staging, as well as for monitoring of cancer regression during treatment and follow up. Interestingly, certain auto-antibodies were found to promote cancer progression and metastasis, while others contribute to the body's defense against it. Moreover, auto-antibodies are of a polyclonal nature, which means that often several antibodies are involved in the response to a single tumor antigen. Dissection of these antibody specificities is now possible, allowing their identification at the genetic, structural, and epitope levels. In this review, we report the evidence available on the presence of auto-antibodies in the main cancer types and discuss some of the open issues that still need to be addressed by the research community.

Identification of tumor-associated antigens of lung cancer: SEREX combined with bioinformatics analysis

The aim of this study is to identify novel tumor-associated antigens (TAAs) of lung cancer by using serological analysis of recombinant cDNA expression library (SEREX) and bioinformatics analysis as well as to explore their humoral immune response. SEREX and pathway enrichment analysis were used to immunoscreen TAAs of lung cancer and elaborate their function in biological pathways, respectively. Subsequently, the sera level of autoantibodies against the selected TAAs (TOP2A, TRIM37, HSP90AB1, EEF1G and TPP1) was detected by immunoserological analysis to explore the immune response of these antigens. The Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) database were applied to explore the mRNA and protein expression level of TOP2A, TRIM37 and HSP90AB1 in tissues, respectively. Seventy positive clones were identified by SEREX which contain 63 different genes, and 35 genes of them have been reported. These 35 genes were mainly related to regulation of different transcription factor and performed enrichment in legionellosis, RNA transport, IL-17 signaling pathway via enrichment analysis. Additionally, the positive rate of autoantibodies against TOP2A, TRIM37 and HSP90AB1 in lung cancer patients were typically higher than normal control (NC; P < 0.05). Moreover, the combination of the autoantibodies against TOP2A, TRIM37 and HSP90AB1 possessed an excellent diagnostic performance with sensitivity of 84% and specificity of 60%. The mRNA expression level of TOP2A was obviously unregulated in squamous cell carcinoma (SCC) tissues and adenocarcinoma (ADC) tissues compared to normal tissues (P < 0.05). In addition, TRIM37 and HSP90AB1 also showed a significant difference between SCC and NC at the mRNA expression level (P < 0.05). This study combining comprehensive autoantibody and gene expression assays has added to the growing list of lung cancer antigens, which may aid the development of diagnostic and immunotherapeutic targets for lung cancer patients.

Humoral immune response to epidermal growth factor receptor in lung cancer

The aim of this study was to explore the potential value of autoantibody to epidermal growth factor receptor (EGFR) in the diagnosis of lung cancer (LC) and its relation with EGFR mutations. Enzyme-linked immunosorbent assay (ELISA) was performed to detect the level of autoantibody to EGFR in sera from 254 LC patients and 222 normal controls (NCs). Besides, the mRNA and protein levels of EGFR were investigated in Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) database, respectively. The level of autoantibody to EGFR (anti-EGFR) in LC even different types of LC was obviously higher than that in NC (P < 0.05). The area under the curve (AUC) of anti-EGFR was 0.695 (95% CI 0.645-0.742) when comparing LC patients with NC, while the AUC of carcinoembryonic antigen (CEA) was 0.681 (95% CI 0.629-0.730). Moreover, by integrating anti-EGFR with CEA to diagnose LC, the AUC was up to 0.784 (95% CI 0.737-0.826). However, the expression level of autoantibody to EGFR had no difference between LC patients with and without EGFR gene mutation (P > 0.05). EGFR mRNA expression level was obviously upregulated in squamous cell carcinoma (SCC) tissues compared with normal tissues (P < 0.05), but not in adenocarcinoma (ADC) (P > 0.05). The study confirmed that anti-EGFR could be a potential biomarker for LC diagnosis; additionally, it could improve the diagnostic value of CEA in clinical work.

Autoantibodies to tumor-associated antigens in lung cancer diagnosis

Lung cancer (LC) accounts for the majority of cancer-related deaths worldwide. Although screening the high-risk population by low-dose CT (LDCT) has reduced mortality, the cost and high false positivity rate has prevented its general diagnostic use. As such, better and more specific minimally invasive biomarkers are needed in general and for early LC detection, specifically. Autoantibodies produced by humoral immune response to tumor-associated antigens (TAA) are emerging as a promising noninvasive biomarker for LC. Given the low sensitivity of any one single autoantibody, a panel approach could provide a more robust and promising strategy to detect early stage LC. In this review, we summarize the background of TAA autoantibodies (TAAb) and the techniques currently used for identifying TAA, as well as recent findings of LC specific antigens and TAAb. This review provides guidance toward the development of accurate and reliable TAAb as immunodiagnostic biomarkers in the early detection of LC.

Identification of the Biomarkers and Pathological Process of Heterotopic Ossification: Weighted Gene Co-Expression Network Analysis

Heterotopic ossification (HO) is the formation of abnormal mature lamellar bone in extra-skeletal sites, including soft tissues and joints, which result in high rates of disability. The understanding of the mechanism of HO is insufficient. The aim of this study was to explore biomarkers and pathological processes in HO+ samples. The gene expression profile GSE94683 was downloaded from the Gene Expression Omnibus database. Sixteen samples from nine HO- and seven HO+ subjects were analyzed. After data preprocessing, 3,529 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 13 modules. Finally, the cyan and purple modules were selected for further study. Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the cyan module was enriched in a variety of components, including protein binding, membrane, nucleoplasm, cytosol, poly(A) RNA binding, biosynthesis of antibiotics, carbon metabolism, endocytosis, citrate cycle, and metabolic pathways. In addition, the purple module was enriched in cytosol, mitochondrion, protein binding, structural constituent of ribosome, rRNA processing, oxidative phosphorylation, ribosome, and non-alcoholic fatty liver disease. Finally, 10 hub genes in the cyan module [actin related protein 3 (ACTR3), ADP ribosylation factor 4 (ARF4), progesterone receptor membrane component 1 (PGRMC1), ribosomal protein S23 (RPS23), mannose-6-phosphate receptor (M6PR), WD repeat domain 12 (WDR12), synaptosome associated protein 23 (SNAP23), actin related protein 2 (ACTR2), siah E3 ubiquitin protein ligase 1 (SIAH1), and glomulin (GLMN)] and 2 hub genes in the purple module [proteasome 20S subunit alpha 3 (PSMA3) and ribosomal protein S27 like (RPS27L)] were identified. Hub genes were validated through quantitative real-time polymerase chain reaction. In summary, 12 hub genes were identified in two modules that were associated with HO. These hub genes could provide new biomarkers, therapeutic ideas, and targets in HO.