Targeting the ARNTL2 Gene as a Potential Strategy for Lung Adenocarcinoma

Targeting the ARNTL2 Gene as a Potential Strategy for Lung Adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Targeting the ARNTL2 Gene as a Potential Strategy for Lung Adenocarcinoma Huan Zhang, Ming Li, Xiangyang Yu, Guangyao Shan, Xing Jin, Mingxiang Feng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1082517/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The relationship between biorhythm and cancer has been increasingly reported in recent years. As one of the core transcription factors of biorhythm, ARNTL2 is thought to be implicated in the occurrence and development of many malignant tumors, such as breast cancer. However, the role of ARNTL2 in lung adenocarcinoma remains elusive. In the current study, we found that expression of ARNTL2 was markedly upregulated in lung adenocarcinoma, and high ARNTL2 expression was correlated with advanced N stage and poor survival in patients. Moreover, ARNTL2 expression levels are closely associated with many important features of lung adenocarcinoma, such as tumor immune microenvironment, ferroptosis, microsatellite instability, tumor mutational load, and drug sensitivity. ARNTL2 could also promote proliferation, invasion, and metastasis of lung adenocarcinoma cells. In addition, we found that high ARNTL2 expression might promote epithelial-mesenchymal transition, metastasis and promotion of angiogenesis of tumor cells. In conclusion, our study reveals that ARNTL2 is a prognostic and promising therapeutic biomarker for people with lung adenocarcinoma. Translational Medicine ARNTL2 lung adenocarcinoma multi-omics integrative analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung cancer is the foremost cause of cancer death, and it has the second-highest incidence worldwide in 2020 1 . Among the primary lung cancers, lung adenocarcinoma is the most common subtype 2 . Although efforts and improvements have been made in therapy for lung adenocarcinoma (LUAD), the prognosis of LUAD patients is still not optimistic, the 5-year survival rate of lung cancer patients was less than 20% 3 . Thus, it is of great importance to determine the etiology and mechanisms of LUAD malignant progression and explore more effective treatment strategies. Circadian rhythms affect many physiological processes, it is widely disrupted in cancers, and its role in the tumor has also been revealed by increasing studies in recent years. Aryl Hydrocarbon Receptor Nuclear Translocator Like 2 (ARNTL2), as a critical circadian transcription factor 4, 5 , has been shown to play an important role in a variety of tumors, such as colorectal cancer, kidney cancer, and pancreatic ductal adenocarcinoma. However, studies on the role of ARNTL2 in lung adenocarcinoma are relatively rare and not sufficiently thorough. The current study comprehensively explored the relationship between the ARNTL2 and lung adenocarcinoma, in order to explore the roles of ARNTL2 in the development, progression and treatment of lung adenocarcinoma. The effects of ARNTL2 on human lung adenocarcinoma cells in terms of tumor mutational burden (TMB), microsatellite instability (MSI), immune microenvironment, ferroptosis, and drug sensitivity were further evaluated, aiming to provide potential targets and ideas for the treatment of lung adenocarcinoma. In addition, we experimentally demonstrated that ARNTL2 promotes tumor cell proliferation, migration, and invasion in lung adenocarcinoma cell lines A549 and H1299. Result Gene expression with clinical features A previous study found that ARNTL2 was frequently highly expressed in many cancers 6 . The differential expression between tumor and adjacent normal tissues for ARNTL2 across TCGA is shown in Figure 1 A. To further validate whether ARNTL2 was differentially expressed in lung adenocarcinoma tissues, we conducted an analysis of 513 cases of LUAD patient data from the TCGA database, which showed significantly higher ARNTL2 expression in LUAD tissues than in normal adjacent tissues, (P < 0.001). The same conclusion was obtained from the validation after combining the GTEx data (Figure 1 B). In addition, we divided the lung adenocarcinoma patients in the TCGA database equally into two groups according to the high and low expression of ARNTL2, and found there were differences in the baseline characteristics of the two groups (Table 1 and Supplementary Figure 1). Specifically, patients in the high-ARNTL2 group were more male and had more advanced N stage and AJCC stage. Table 1 LUAD patient characteristics according to ARNTL2 expression high- group low- group p value Total evaluated 257 256 Age(years) 0.931 Mean ± SD 65.3±10.3 65.3±9.8 Sex 0.046 male 130 107 female 127 149 Smoking history 0.217 No 34 40 Yes 213 212 Unknown 10 4 Pathologic T stage 0.089 T1 72 96 T2 142 134 T3 30 17 T4 11 8 Unknown 2 1 Pathologic N stage 0.001 N0 142 188 N1 60 35 N2 47 27 N3 1 1 Unknown 7 5 Pathologic M stage 0.099 M0 178 166 M1 16 9 Unknown 63 81 Ajcc 8th stage 0.001 1 115 159 2 69 52 3 53 31 4 17 9 Unknown 3 5 Radiation 0.781 Yes 7 6 No/Unknown 250 250 Chemotherapy 0.350 Yes 82 72 No/Unknown 175 184 Survival analysis We analyzed the prognosis of 504 patients with lung adenocarcinoma in the TCGA database in relation to ARNTL2 expression and found that high ARNTL2 expression predicted poor survival. Results are shown in Figure 1 C, ARNTL2-high group vs ARNTL2-low group: OS: HR= 1.53 (95%CI, 1.14 - 2.05) p = 0.005; PFS: HR= 1.35 (95%CI, 1.03 - 1.78) p = 0.031. For lung adenocarcinoma patients in the TCGA database, we also validated the correlation between survival and clinical factors. A total of 513 lung adenocarcinoma participants were involved in univariate and multivariate analyses to validate survival factors. Univariate analyses revealed that ARNTL2 (p ＜ 0.001), T stage (p ＜ 0.001), and N stage (p ＜ 0.001) were statistically significant predictors of tumor-specific survival. According to multivariate analysis, ARNTL2 (p ＜ 0.001), T stage (p = 0.005), and N stage (p = 0.001) remained independent prognostic predictors for LUAD patients. The details of the correlations between survival outcomes and parameters are shown in the following forest plot (Figure 1D). TMB and mRNAsi analyses As one of the immunotherapy biomarkers, tumor mutation burden (TMB) has attracted more and more attention in recent years, we analyzed the relationship between ARNTL2 gene expression and TMB in lung adenocarcinoma patients according to the TCGA database, and the results showed that there was a degree of positive correlation between ARNTL2 gene expression and TMB (Figure 2 A), which may indicate that in lung adenocarcinoma patients, as ARNTL2 gene expression increases, TMB also increases. Thus ARNTL2 may have a reference value for immunotherapy. It has been previously shown that mRNAsi is a valid method to evaluate the level of tumor differentiation. Histopathology confirmed that higher values of mRNAsi tend to represent a greater degree of tumor dedifferentiation. AS shown in Figure 2 B, in lung adenocarcinoma, the mRNAsi was significantly higher in the lung tumor specimens than in normal lung tissue. Furthermore, compared with the low ARNTL2 expression group, the high ARNTL2 group showed significantly higher mRNAsi, which may be related to the worse prognosis of patients in the high ARNTL2 group. MSI Microsatellites (MS), also known as short tandem repeats (STR) or simple sequence repeats (SSR), are structures consisting of repetitive sequences of 1-6 nucleotides 7 . MSI is an important factor in tumorigenesis and progression and has been extensively studied in tumors such as colorectal cancer. Early diagnosis of MSI is of great significance to the prognosis and treatment of MSI 8 . We found that the expression level of ARNTL2 and MSI were negatively correlated in lung adenocarcinoma patients (Figure 2 C). Somatic mutations Based on previous reports, the number of somatic mutations is thought to correlate with prognosis. The changes in the copy numbers' variation and distribution of somatic mutations in high-ARNTL2 and low-ARNTL2 groups were analyzed. The results showed that TP53 had a high mutation rate in the high-ARNTL2 group (55% vs. 41%, P-value < 0.01). In contrast, some genes, such as RELN (11% for the low subgroup and 18% for the high subgroup), had a lower mutation rate in the high ARNTL2 group (Figure 2 D). DEG, GO, KEGG analyses and single-cell analysis The prognosis of lung adenocarcinoma patients with different ARNTL2 expression levels may be related to DEGs. So, we have analyzed the expression profiles from the TCGA data, to derive the landscape of biological differences. The prognosis of lung adenocarcinoma patients with different ARNTL2 expression levels may be related to DEGs. So, we have analyzed the expression of DEGs to derive landscape of biological differences (Figure 3 A), and 185 significant different genes were found, including 114 upregulated genes in the high-ARNTL2 group such as SLC2A1, CD109, and ADGRF4 (adjusted P < 0.01), and 71 downregulated genes including C16orf89, IRX5, and IRX3 (Supplementary Table 2). Functional enrichment of GO and KEGG in the 185 DEGs were analyzed. These pathways were associated with high-ARNTL2 expression. Cell cycle, apoptosis, IL-17 signaling pathway, and p53 signaling pathway ranked top in the high-ARNTL2 group (Figure 3 B). In addition, we performed a single-cell analysis of lung adenocarcinoma cells through the CancerSEA database. The results showed that ARNTL2 gene expression was highly correlated with Angiogenesis, EMT, and Metastasis (Figure 3 C, Supplementary Table 3). Immune infiltration and immune checkpoint analyses The composition and proportion of immune cells in the tumor microenvironment (TME) have a significant impact on tumor development and treatment 9 . In lung adenocarcinoma, CD8+ and CD4+ T cells showed a significant positive correlation with ARNTL2 expression. In contrast, NK cell and Macrophage M2 showed a negative correlation (Figure 4 A). In addition, some common genes closely related to immune checkpoints also differed with the high expression of ARNTL2 (Figure 4 B). Compared to the low-ARNTL2 expression group, the high expression group had significantly higher expression of CD274, CTLA4, LAG3, and PDCD1, etc. These differences in the immune microenvironment may provide ideas for immunotherapy. Ferroptosis and drug-sensitive analyses Comparison of important genes associated with ferroptosis between the high and low expression ARNTL2 groups showed that ACSL4, CDKN1A, and MT1G, etc. were overexpressed in the high expression group (Figure 4 C), which may predict a correlation between ARNTL2 expression and ferroptosis in lung adenocarcinoma. ANRTL2 expression also appears to have effects on drug sensitivity in lung adenocarcinoma, with IC50s significantly lower in the high ANRTL2 expression group than in the low ANRTL2 expression group for Gefitinib, Gemcitabine, Cisplatin, and Paclitaxel, although there was no significant difference in sensitivity to erlotinib between the two groups (Figure 4 D). IHC and immunofluorescence: ARNTL2 is highly expressed in lung adenocarcinoma tissues The results of immunohistochemistry further confirmed the increased expression of ARNTL2 in lung adenocarcinoma. As shown in Figure 5 A, the overexpression of ARNTL2 was seen in resected tumor tissues compared with adjacent normal tissues. Similar results were obtained by immunofluorescence analysis (Figure 5 B). ARNTL2 promotes proliferation, invasion, and migration of LUAD cells To identify the effect of ARNTL2 on proliferation and migration of lung adenocarcinoma cells, we established cell model of ARNTL2 downregulation by stably transducing with ARNTL2 shRNA-expressing lentiviruses in A549 and H1299 cells. Next, we detected the expression level of ARNTL2 by Western blot after transfection, the stable knockdown efficiency of shRNA at protein expression level was verified (Figure 5 C). ARNTL2 knockdown was very efficient in A549 and H1299 cell lines We subsequently performed CCK8, Clone formation, wound healing, and transwell assays to explore the effect of ARNTL2 in tumor proliferation and migration. CCK8 assays showed when ARNTL2 was downregulated, compared with the control cells, the proliferation ability was decreased in both A549 and H1299 cells (Figure 5 D). To further testify the role of ARNTL2 in lung adenocarcinoma formation, Clone formation of ARNTL2-knockdown and control cells was assessed. The results showed that ARNTL2-knockdown cells formed significantly fewer colonies compared to control cells (Figure 5 E), which indicates ARNTL2 is essential for the oncogenicity of lung adenocarcinoma cells. We also investigated whether ARNTL2-knockdown cells affected migratory and invasion. ARNTL2-knockdown significantly reduced the migration of A549 and H1299 cells in a wound-healing assay, shControl cells almost recovered the wound within 48 hours, but shARNTL2 cells still have large areas of unhealed wounds (Figure 6 A). As shown in figure 6 B, ARNTL2-knockdown cells also showed significantly lower infiltration rates in the transwell migration and invasion assays compared to control cells (p < 0.05). These results suggest that ARNTL2 may be a key promoter of lung adenocarcinoma cell growth and invasion. Discussion Circadian rhythm disorders may be associated with a variety of diseases, such as obesity and depression 10 , 11 . In addition, Strong epidemiological evidence links circadian disruption with cancers 12 . Moreover, in recent years, a large number of studies have demonstrated that circadian rhythm disorders may be associated with the development of various cancers in human being 13 , 14 . As an important biorhythm-related gene, ARNTL has been reported to play an oncogenic role in many human cancers. For example, Prior studies found that upregulation of ARNTL2 is associated with poor survival and immune infiltration in clear cell renal cell carcinoma 15 . Mazzoccoli et al. reported that ARNTL2 is upregulated in colorectal cancer and is related to tumor invasiveness and aggressiveness 16 . There are many other studies that demonstrated that high expression of Arntl2 was associated with poor survival in BRCA, LIHC, and pancreatic ductal adenocarcinoma 6 , 17 , 18 . In this study, we performed a comprehensive analysis by integrating available data from TCGA and GTEx database. We found that ARNTL2 expression was correlated with the pathological N-stage of lung adenocarcinoma patients. Similar results have been reported in colorectal cancer. And patients with high ARNTL2 expression had a worse prognosis and could serve as an independent predictor of prognosis in lung adenocarcinoma. Furthermore, a prognostic nomogram including ARNTL2 based upon the results of multivariate Cox analysis was constructed to predict the long-term survival of LUAD patients. Our study also confirmed that ARNTL2 is upregulated in lung adenocarcinoma, which is consistent with some previous reports: Arntl2 was upregulated in BLCA, BRCA, COAD, and READ 18 . ARNTL2 can also promote proliferation and migration of lung adenocarcinoma cells, which suggests that ARNTL2 may be a key promoter of lung adenocarcinoma cell growth and invasion. In addition, a positive correlation between ARNTL2 expression and TMB was found in lung adenocarcinoma. In contrast, MSI showed a negative correlation with ARNTL2 expression. Although much progress in the treatment of lung cancer has been made in recent decades, lung cancer remains the deadliest malignancy worldwide and significantly affects the quality of life of patients 19 . Especially for advanced patients, the 5-year survival rate of stage IV lung cancer patients is only 4.7% 20 . Immunotherapy is currently attracting increasing attention in the treatment of lung cancer. However, there are still many patients who benefit less from immunotherapy due to insensitivity to drugs. Therefore, it is vital to discover new effective prognostic biomarkers and specific immune-related therapeutic targets. Our study shows that in lung adenocarcinoma, ARNTL2 causes a significant impact on the immune microenvironment. For example, ARNTL2 expression levels were strongly positively correlated with CD8+ T cells, conversely, strongly negatively correlated with Natural killer (NK) cells. NK cells play an important role in the induction of both innate and adaptive immune responses. Many studies have demonstrated the critical role of NK cells in the control of lung cancer: NK cells can infiltrate lung cancer and a significant positive correlation between the number of tumor-infiltrating NK cells and the survival rate of patients after surgery was reported 21 – 23 . Therefore, a previous study suggested that immunotherapy targeting NK cells may be a breakthrough point in treatment for lung cancer 24 . There were also significant differences in immune checkpoints between high-ARNTL2, and low-ARNTL2 group patients. Immune-checkpoint inhibitors (ICIs) is currently making a splash in the treatment of non-small cell lung cancer 25 , 26 , further research on the relationship between ARNTL2 and immune checkpoints may be meaningful and necessary. In addition, numerous studies have demonstrated that TMB and MSI can be used to predict the effect of immunotherapy 27 , 28 , for example, tumor cells with high TMB are reported to be more easily recognized by immune cells, and thus respond more significantly to immunotherapy 29 , which may be used in combination with TNM staging to predict the progression and prognosis of patients with advanced tumors. We found that in lung adenocarcinoma, tumor immune infiltration, immune checkpoints, MSI and TMB were all associated with ARNTL2 expression levels. The above findings may deserve more in-depth studies and help with immunotherapy for a subset of lung adenocarcinoma patients in the future. We found that ARNTL2 gene expression was highly correlated with EMT and Metastasis by single-cell analysis, which This could potentially explain why LUAD patients with high-ARNTL2 expression have a worse prognosis. Indeed, the role of ARNTL2 in promoting tumor invasion and metastasis has been reported in a variety of tumors. A previous study of colorectal cancer found that high ARNTL2 expression was significantly associated with vascular invasion and lymph node metastasis 16 . In addition, several studies have shown that ARNTL2 is associated with the development and metastasis of breast cancer 30 , and Ha NH, et al. indicated that affect the expression levels of ARNTL2 has a significant effect on metastatic progression in ER-breast cancers 31 . It has also been demonstrated that in PDAC, ARNTL2 could affect tumor proliferation, migration, and invasion through the TGF/BETA pathway 6 . Brady et al. reported that ARNTL2 could drive metastatic self-sufficiency in lung cancer 32 . Moreover, Epithelial-to-mesenchymal transition (EMT) has been increasingly recognized to promote carcinoma invasion and metastasis. A previous study confirmed that in colon carcinoma, downregulation of ARNTL2 could suppress tumor cell proliferation and migration via SMOC2-EMT through inactivation of PI3K/AKT signaling pathway 33 . These studies suggest that ARNTL2 may contribute to tumor development through multiple mechanisms, such as the promotion of metastasis and EMT. By applying bioinformatics analysis, we found that there was a high correlation between ARNTL2 expression level and promotional effect on angiogenesis in lung adenocarcinoma cells. In addition, ARNTL2 expression levels appear to have an effect on ferroptosis and drug sensitivity in lung adenocarcinoma cells. However, there are no reports on related fields, further experimental validation is required. This will be the next step of our future work which may could help to reveal deeper mechanisms. There were also some limitations to this study. Although this study used a massive cohort of TCGA and GEO databases to develop and validate the role of ARNTL2 in lung adenocarcinoma, selection bias could not be avoided because of the retrospective nature of our study design. In addition, this experiment did not conduct more in-depth experiments to explore the mechanism of ARNTL promoting tumor metastasis and EMT in lung adenocarcinoma, which is also the direction of our future research. ARNTL2 is highly expressed in lung adenocarcinoma, and high ARNTL2 expression was associated with lymph node metastasis and was an independent predictor of worse prognosis for lung adenocarcinoma patients. Notably, the expression of ARNTL2 in lung adenocarcinoma is associated with a variety of features such as tumor immunity, ferroptosis, MSI, and drug sensitivity, which may provide a prediction and reference for immunotherapy. ARNTL2 may also be associated with the promotion of tumor metastasis, EMT, and angiogenesis. Through experiments, we verified that it could promote the proliferation, invasion and metastasis of lung adenocarcinoma. In conclusion, ARNTL2 may be a potential biomarker for lung adenocarcinoma, it may be useful to predict prognosis and guide the treatment in a subgroup of LUAD patients, and may be a new target for therapeutic approaches. Methods Data processing Gene expression data of LUAD patients (FPKM normalized) and corresponding clinical and survival information of The Cancer Genome Atlas (TCGA) were downloaded from the UCSC Xena browser (GDC hub: https://gdc.xenahubs.net ) 34 . The data with missing prognosis information, including outcome status and survival time was removed. The edition of lung cancer staging was classified according to the American Joint Committee on Cancer (AJCC) TNM Classification for Lung and Pleural Tumors (eighth edition). Genome statistical analysis TCGA cases were divided into high or low groups based on the median values of ARNTL2 expression level. Genome statistical analyses performed in R version 4.0.3 were as follows: (a) differentially expressed genes (DEGs): The limma package was used to identify DEGs and miRNAs. The moderated t-test was adopted to calculate DEGs and miRNA expression changes, and the P value was adjusted as FDR by Benjamini and Hochberg method. The log fold change > 0.5, and the adjusted P value < 0.05 35 . (b) copy number variations (CNV) and microRNAs (miRNAs): the maftools package were used to compare the distribution of somatic mutations and the types of copy number variations 36 . The adjusted P value < 0.01 was used to assess the significance of the mutational frequency. The somatic mutations and the types of copy number variations between high and low-ARNTL2 groups were compared by Kruskal–Wallis test, and the adjusted p-value <0.05 were considered statistically significant. Results were shown with the oncoplot function. (c) GO and KEGG: The involved pathways and biological functions of the DEGs were performed with GO and KEGG pathway enrichment analysis by the “clusterProfiler” R package 35 . The cut-off value was set as adjusted P < 0.05 and false discovery rate (FDR) < 0.05. (d) Ferroptosis analysis: Ferroptosis related genes are derived from Ze-Xian Liu et al.'s systematic analysis of the aberrances and functional implications of Ferroptosis in Cancer 37 . (e) mRNAi analysis: We use the OCLR algorithm constructed by Malta et al. to calculate mRNAsi 38 , 39 . Based on the characteristics of mRNA expression, the gene expression profile contains 11,774 genes. We use the same Spearman correlation (RNA expression data), and then subtract the minimum value and divide by the linear transformation of the maximum value maps the dryness index to the range [0,1]. (f) drug sensitivity analysis: We predicted the chemotherapeutic response for each sample based on the largest publicly available pharmacogenomics database [the Genomics of Drug Sensitivity in Cancer (GDSC), https://www.cancerrxgene.org/].The prediction process was implemented by R package “pRRophetic” where the samples' half-maximal inhibitory concentration (IC50) was estimated by ridge regression and the prediction accuracy. All parameters were set by the default values with removal of the batch effect of combat and tissue type of allSoldTumours, and duplicate gene expression was summarized as mean value 40 . (g) immune infiltration and immune checkpoints: to make reliable immune infiltration estimations, we utilized the QUANTISEQ method in the R package Immunodeconv 41 , 42 . SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 were selected to be immune-checkpoint–relevant transcripts and the expression values of these eight genes were extracted. The QUANTISEQ method ( https://quantuseq.stanford.edu ) 43 was selected to analyze the relative levels of the 10 tumor-infiltrating immune cell phenotypes based on the RNA-seq expression profiles. The distribution of 10 tumor-infiltrating immune cell types between worse and better prognostic signature was investigated. (h) Correlation analysis of ARNTL2 expression and TMB/MSI 44, 45 : we used Spearman’s correlation analysis to describe the correlation between quantitative variables without a normal distribution. The value range of Pearson correlation coefficient is [-1,1] with a higher absolute value indicating a stronger association and the sign indicating a positive or negative association between the two variables. The density curve on the right represents the distribution trend of the TMB/MSI score; the upper density curve represents the distribution trend of the gene. All the above analysis methods and R package were implemented by R foundation for statistical computing (2020) version 4.0.3. Single-cell analysis Single-cell analysis was performed through the CANCERSEA website. CancerSEA is the first dedicated database that aims to comprehensively decode distinct functional states of cancer cells at single-cell resolution 46 . Prognosis analysis Prognostic information was analyzed in the PrognoScan database and Kaplan–Meier plotter database 47 . Statistical analysis Statistical analyses in the current study were completed by R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria). The R package included survival, rms and ggplot2. Statistical significance was set at a two-sided p value < 0.05. Categorical variables were compared using Fisher’s exact test and Pearson’s c2 test and continuous variables were compared using Student’s t-test and the Wilcoxon test. Multivariable Cox regression analyses were used to test independent prognostic value using the R package survival and the coxph function. Cell culture and lentivirus infection Lung adenocarcinoma cell lines (A549 and H1299) were purchased from the Chinese Academy of Sciences Cell Bank. Cells were cultured in DMEM (Hyclone, Logan, UT, USA) supplemented with 10% fetal bovine serum (Hyclone), 100 U/mL penicillin, and 100 U/mL streptomycin in a humidified 5% CO2 atmosphere at 37°C. Two different short hairpin RNAs (shRNAs) encoding shARNTL2 and scramble shRNA control (catalogue) were designed by Shanghai Genechem Co.,Ltd. and cloned into a lentivirus vector with puromycin resistance, which was then transfected into cells and screened with puromycin to ensure transfection efficacy. Viral transduction was performed as the manufacturer’s protocol. After 3 days of virus transfection, knockdown was verified by western blot analysis using ARNTL2-specific antibody (ab221557, abcam). Sequences of the shRNAs and the control are provided in Supplementary table 1. Western blot RIPA buffer (Beyotime, Shanghai, China) containing protease and phosphatase inhibitors cocktail (Beyotime) were used to extract proteins, enhanced BCA Protein Assay Kit (Beyotime) was used for protein quantification. As previously reported, Protein samples were separated by electrophoresis on SDS-PAGE and transferred to a polyvinylidene difluoride membrane (Merck-Millipore, Burlington, MA, USA). After the transfer, the blots were blocked with 5% milk for 1 hour and incubated with primary antibody for 12h at 4°C. Then, tris-buffered saline Tween-20 (TBST) was used to wash the membranes three times. After washing, the membranes were incubated with secondary antibodies at room temperature for 1 h. Finally, the protein bands were visualized and analyzed using a Moon Chemiluminescence Reagent kit (Beyotime). The following antibodies were used in this study: Anti-ANRTL2 antibody (1:1,000, abcam, ab221557) Cell proliferation assay We seeded 2000 cells that were in the logarithmic growth phase, per well in a final volume of 100 µL growth medium into black 96-well plates. After incubation at 37°C for 0, 24, 48, 72, 96, and 120 h, the cell proliferation was measured by Enhanced Cell Counting Kit-8 Viability Assay Kit (Beyotime). Wound healing assay Control shRNA and shARNTL2 cells were inoculated on the 6-well plate on average. Cells were grown into monolayer and manual scratching with a 200-µl pipette tip, then cells were rinsed with PBS and incubated at 37°C in serum-free media. Photographs of the wounded areas were taken every 24h by phase-contrast microscopy. Transwell migration and invasion assay Cell migration and invasion abilities were measured by Transwell assays using the 24-well transwell chambers with 8 µm polycarbonate membranes (Corning, NY). the uncoated were used to determine migration and pre-coated with Matrigel Basement Membrane Matrix were used to determine invasion (BD Biosciences). The chambers were rehydrated in a serum-free medium for 2 hours as described by the manufacturer. Then the upper chambers were added with serum-free medium, while the lower chambers were added with serum medium. Cells were seeded onto the upper chambers with a density of 5×104 cells per well, and incubated for 24 h at 37°C, 5% CO2. Cells migrated toward the lower chambers were fixed with methanol and stained with 0.5% crystal violet. Each assay was photographed under the inverted microscope (Olympus), and the number of cells within each chamber was counted by ImageJ software. Clone formation assay Control or ARNTL2 shRNA-transduced A549 and H1299 cells (3 × 103 cells/well) were cultured in 6-well plates were cultured at 37°C in 5% CO2 environment. ARNTL2 shRNA-transduced or control cells were seeded in 6-well plates at a density of 5 × 106 cells per well and cultured at 37°C in 5% CO2 environment. After 9 days, cells were stained with 4% formaldehyde/0.005% gentian violet solution and captured under the inverted microscope. Immunohistochemistry and immunofluorescence The tissue specimens were collected from both tumor and adjacent normal tissues of 100 patients with LUAD who received surgery from September to November 2020 in the Zhongshan Hospital. As previously reported 48 , the tissues were stained by a GTVision + Detection System/Mo&Rb Immunohistochemistry kit (GK500710, GeneTech, Shanghai, China) following the manufacturer's protocol. Specifically, the 5-µm paraffin-embedded tissues were dewaxed, rehydrated, and incubated with antibodies against ARNTL2 (1:500, Abcam, Cambridge, UK) at 4°C overnight, and then were incubated with biotinylated secondary antibodies. For immunofluorescence, sections were incubated with primary antibodies against ARNTL2 (rabbit polyclonal, 1:500), followed by incubation with the respective secondary antibodies (Cy3-labeled goat anti-rabbit IgG). DAPI nuclear counterstaining was then performed. Finally, fluorescence microscope was used to take micrographs. Abbreviations LUAD: lung adenocarcinoma ARNTL2: Aryl Hydrocarbon Receptor Nuclear Translocator Like 2 TMB: tumor mutation burden MSI: Microsatellite instability TCGA: The Cancer Genome Atlas AJCC: American Joint Committee on Cancer DEGs: differentially expressed genes CNV: copy number variations miRNAs: microRNAs GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes FDR: false discovery rate GDSC: the Genomics of Drug Sensitivity in Cancer IC50: half-maximal inhibitory concentration shRNAs: short hairpin RNAs qRT-PCR: Quantitative real-time polymerase chain reaction TBST: tris-buffered saline Tween-20 MS: Microsatellites STR: short tandem repeats SSR: simple sequence repeats TME: tumor microenvironment NK cells: Natural killer cells. ICIs: immune-checkpoint inhibitors EMT: Epithelial-to-mesenchymal transition Declarations Acknowledgments The authors appreciate the academic support from the Home for Researchers. We also have asked the International Science Editing Corporation to edit the language. Formatting of funding sources This work was supported by the Shanghai Medical Innovation Research Project (Grant No. 20Y11908200). Author Contributions Conceptualization was contributed by HW, ML, and QW; Data collection and curation were contributed by HZ, GS, and XJ; Data analysis and interpretation were contributed by ML, HZ, GS, XJ, MF, HW, and ZC; Draft of the manuscript was contributed by HZ, ML and XY; Critical revision of the manuscript was contributed by MF, and CZ; Final approval of manuscript and submission were contributed by all authors. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests There are no conflicts of interest to declare. 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Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med . May 24 2019;11(1):34. doi:10.1186/s13073-019-0638-6 Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol . Oct 20 2016;17(1):218. doi:10.1186/s13059-016-1070-5 Thorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. Immunity . Apr 17 2018;48(4):812-830.e14. doi:10.1016/j.immuni.2018.03.023 Zheng Y, Huang Y, Bi G, et al. Multi-omics characterization and validation of MSI-related molecular features across multiple malignancies. Life Sci . Apr 1 2021;270:119081. doi:10.1016/j.lfs.2021.119081 Yuan H, Yan M, Zhang G, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res . Jan 8 2019;47(D1):D900-d908. doi:10.1093/nar/gky939 Hou GX, Liu P, Yang J, Wen S. Mining expression and prognosis of topoisomerase isoforms in non-small-cell lung cancer by using Oncomine and Kaplan-Meier plotter. PLoS One . 2017;12(3):e0174515. doi:10.1371/journal.pone.0174515 Bi G, Zhu D, Bian Y, et al. Knockdown of GTF2E2 inhibits the growth and progression of lung adenocarcinoma via RPS4X in vitro and in vivo. Cancer Cell Int . Mar 23 2021;21(1):181. doi:10.1186/s12935-021-01878-z Supplementary Files FigS1.tif Supplementary Figure 1 LUAD patient characteristics divided into high and low groups according to ARNTL2 expression FigS2.tiff Supplementary Figure 2 The waterfall plots show the somatic mutations and copy numbers' variations in the high and low-ARNTL2 groups. TableS1shRNA.xlsx Supplementary Table 1 The sequences of all the shRNAs used in this study. TableS2DEGs.csv Supplementary Table 2 The detailed results of DEGs TableS3PlotDatacorrheatmap.csv Supplementary Table 3 Result of the role of ARNTL2 in lung adenocarcinoma cells by CancerSEA Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1082517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":65654564,"identity":"4213b8a4-b0a3-4eb7-a3a3-f314094d9313","order_by":0,"name":"Huan Zhang","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Zhang","suffix":""},{"id":65654565,"identity":"90712b0c-c06d-474f-9502-8e5157c9a889","order_by":1,"name":"Ming Li","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""},{"id":65654566,"identity":"6d0d4ddd-d00e-495a-8d1e-10a5093dc5b1","order_by":2,"name":"Xiangyang Yu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences Cancer Institute and Hospital: Cancer Hospital Chinese Academy of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangyang","middleName":"","lastName":"Yu","suffix":""},{"id":65654567,"identity":"613b67de-f633-495d-836b-af11177aa0bf","order_by":3,"name":"Guangyao Shan","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangyao","middleName":"","lastName":"Shan","suffix":""},{"id":65654568,"identity":"0278c6da-4bfa-4c5c-8bff-0f41be587a21","order_by":4,"name":"Xing Jin","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Jin","suffix":""},{"id":65654569,"identity":"aeb8e890-fb17-4d2f-bf0b-5bb0d42aa79d","order_by":5,"name":"Mingxiang Feng","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingxiang","middleName":"","lastName":"Feng","suffix":""},{"id":65654570,"identity":"c2739b98-4a18-4a85-8d14-5382a5d925fd","order_by":6,"name":"Cheng Zhan","email":"","orcid":"https://orcid.org/0000-0001-8745-9276","institution":"Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhan","suffix":""},{"id":65654571,"identity":"6b517419-4056-4038-a1cb-d1bc0ca9c41e","order_by":7,"name":"Hao Wang","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":65654572,"identity":"4da940bb-0bc2-451e-9aef-d16363dba4d3","order_by":8,"name":"Miao Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2QsWrDMBCGzxWcF9lancV5BQVDxjyLTVcTDF0KhcRg8FTIGshLJBQ6q2jwYsiaIYMh0Dl7PfRi4iHFdtZA9Q2nG+6Tfh2AwfCAuOxSJYCgo+kRwFIAqlfBVhmlrYLN/IDSNlJdFbir2I56OiQzP9gXFUteF3N3nFUK6iOITdoTzA1ZLJ+DqSolW5f6BRGlsvJv8I7dLyHjkhQWfX692z9OrqIcKaSVapBeOKQso4/MrpiTL0ixzxTsrqKjLV1OCiOF0ypwWNGxLAKvJJc3f4kTiqe5d+hWhCgnp7h+88WKNsZpY+Os2FXnWvti3a1cuFlM2FbeO/+X/psNBoPh3/ILunNSo50wDvAAAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Lin","suffix":""},{"id":65654573,"identity":"5dbb4b91-8a23-44eb-8a00-766b51c2740d","order_by":9,"name":"Qun Wang","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2021-11-15 15:11:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1082517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1082517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16024437,"identity":"c131f00f-fb2a-4acc-8436-8fdbf9eba88c","added_by":"auto","created_at":"2021-11-30 15:59:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3269352,"visible":true,"origin":"","legend":"A ARNTL2 expression levels in different tumor types from TCGA database were determined by TIMER. B differences of ARNTL2 expression in lung adenocarcinoma tissues and normal tissues. C Survival curves comparing the high and low expression of ARNTL2 in lung adenocarcinoma. D Univariate and multivariate analysis of overall survival in LUAD patients from TCGA database.","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/294acc6aa6cb8aa826e71652.jpg"},{"id":16024427,"identity":"b36e5a76-0345-4cfb-aa70-cfda3e61a363","added_by":"auto","created_at":"2021-11-30 15:59:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2474427,"visible":true,"origin":"","legend":"A Scatterplots of correlations between ARNTL2 expression and TMB. B The comparison of mRNAsi in high, low ARNTL2 expression groups of lung adenocarcinoma tissues versus normol tissues. C Scatterplots of correlations between ARNTL2 expression and MSI. D Differential mutations and their distributions in the high and low-ARNTL2 expression groups.","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/cb8477dc02fad9ed439e1ae1.jpg"},{"id":16024457,"identity":"1a18be77-c5cc-4e51-be90-76ab76d3d8b5","added_by":"auto","created_at":"2021-11-30 15:59:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2422073,"visible":true,"origin":"","legend":"A Differential expressed genes between high and low-ARNTL2 expression groups were shown in a volcano plot. B Dot plot of GO and KEGG pathway analysis to the DEGs. C Scatterplots of correlations between ARNTL2 expression and angiogenesis, EMT and Metastasis by single-cell analysis of lung adenocarcinoma cells through the CancerSEA database.","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/d43d8b171f60472264fb2177.jpg"},{"id":16024443,"identity":"e11ba215-b22c-42b5-9407-6521649d7bb0","added_by":"auto","created_at":"2021-11-30 15:59:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4631785,"visible":true,"origin":"","legend":"A Correlation of ARNTL2 expression with immune infiltration level in LUAD through QUANTISEQ method. B Differences in the expression of some important immune gene closely related to immune checkpoints between high and low-ARNTL2 groups. C Differences in the expression of important genes associated with ferroptosis between high and low-ARNTL2 groups. D Relationship between drug sensitivity in lung adenocarcinoma and ANRTL2 expression were shown in box plot: Cisplatin, Paclitaxel, Gefitiinib, Gemcitabine and Erlotinib","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/f4843898dcdd4f2356ccfded.jpg"},{"id":16024441,"identity":"7ea06911-b118-4ad2-9094-a4e8e753eb9a","added_by":"auto","created_at":"2021-11-30 15:59:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3803352,"visible":true,"origin":"","legend":"A-B The IHC (A) and immunofluorescence (B) staining of ARNTL2 in LUAD tissue versus adjacent normal tissues: ARNTL2 expression in tumor tissues was significantly higher than that in adjacent normal tissues. C Western blotting analyses verifying the ARNTL2 knockdown efficiency, quantification was performed by ImageJ: ARNTL2 knockdown was very efficient. D The effects of ARNTL2 knockdown on cell proliferation in A549 and H1299 cells through CCK8 assays: knockdown of ARNTL2 dramatically inhibited PDAC cells proliferation. E Comparison of colony formation efficiency between ARNTL2-knockdown group and control group: knockdown of ARNTL2 dramatically inhibited colony formation of LUAD cells.","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/675c1cb02efbd6418f5595e6.jpg"},{"id":16024452,"identity":"4b992fc9-4214-44a3-a2ea-010069afc6c2","added_by":"auto","created_at":"2021-11-30 15:59:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3294610,"visible":true,"origin":"","legend":"A Comparison of Wound-healing assays between ARNTL2-knockdown group and control group: ARNTL2 knockdown decreases cell migration in LUAD cells compared to control cells. B The invasion and migration capability of LUAD cells transfected with NC or sh-ARNTL2 was analyzed by transwell assay: ARNTL2 knockdown diminished migration and invasion ability of LUAD cells.","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/46a45772d6b0281bba3971ce.jpg"},{"id":16152381,"identity":"af1ecb00-23e5-4faf-8a97-aefae06e2c8e","added_by":"auto","created_at":"2021-12-03 17:01:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1423739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/20e3189e-7b60-4503-a714-5053fadafc3a.pdf"},{"id":16024429,"identity":"2d386f8a-01a7-4256-8369-54a13ca77947","added_by":"auto","created_at":"2021-11-30 15:59:14","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":894354,"visible":true,"origin":"","legend":"Supplementary Figure 1 LUAD patient characteristics divided into high and low groups according to ARNTL2 expression ","description":"","filename":"FigS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/62fb0aa9016942839d10a72e.tif"},{"id":16024981,"identity":"e4616618-48f3-4b34-9339-360e2d20b201","added_by":"auto","created_at":"2021-11-30 16:02:14","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":580898,"visible":true,"origin":"","legend":"Supplementary Figure 2 The waterfall plots show the somatic mutations and copy numbers' variations in the high and low-ARNTL2 groups.","description":"","filename":"FigS2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/38f8dcf270801b36e9b490fc.tiff"},{"id":16024456,"identity":"e5681843-992c-4a3c-9828-f2e518b70cdf","added_by":"auto","created_at":"2021-11-30 15:59:15","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10160,"visible":true,"origin":"","legend":"Supplementary Table 1 The sequences of all the shRNAs used in this study.","description":"","filename":"TableS1shRNA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/a258d139c7887b173b9a457b.xlsx"},{"id":16024980,"identity":"43472c67-4a3f-42e0-b2d7-63249b726d39","added_by":"auto","created_at":"2021-11-30 16:02:14","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1999312,"visible":true,"origin":"","legend":"Supplementary Table 2 The detailed results of DEGs","description":"","filename":"TableS2DEGs.csv","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/17d249895b1ff37394fe3f2a.csv"},{"id":16024446,"identity":"b67efaf6-bf17-4eb6-8a44-8b9890643d71","added_by":"auto","created_at":"2021-11-30 15:59:15","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":297,"visible":true,"origin":"","legend":"Supplementary Table 3 Result of the role of ARNTL2 in lung adenocarcinoma cells by CancerSEA","description":"","filename":"TableS3PlotDatacorrheatmap.csv","url":"https://assets-eu.researchsquare.com/files/rs-1082517/v1/eb62d35b1a18c292beb4e417.csv"}],"financialInterests":"","formattedTitle":"\u003cp\u003eTargeting the ARNTL2 Gene as a Potential Strategy for Lung Adenocarcinoma\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the foremost cause of cancer death, and it has the second-highest incidence worldwide in 2020\u003csup\u003e1\u003c/sup\u003e. Among the primary lung cancers, lung adenocarcinoma is the most common subtype\u003csup\u003e2\u003c/sup\u003e. Although efforts and improvements have been made in therapy for lung adenocarcinoma (LUAD), the prognosis of LUAD patients is still not optimistic, the 5-year survival rate of lung cancer patients was less than 20%\u003csup\u003e3\u003c/sup\u003e. Thus, it is of great importance to determine the etiology and mechanisms of LUAD malignant progression and explore more effective treatment strategies.\u003c/p\u003e\n\u003cp\u003eCircadian rhythms affect many physiological processes, it is widely disrupted in cancers, and its role in the tumor has also been revealed by increasing studies in recent years. Aryl Hydrocarbon Receptor Nuclear Translocator Like 2 (ARNTL2), as a critical circadian transcription factor\u003csup\u003e4, 5\u003c/sup\u003e, has been shown to play an important role in a variety of tumors, such as colorectal cancer, kidney cancer, and pancreatic ductal adenocarcinoma. However, studies on the role of ARNTL2 in lung adenocarcinoma are relatively rare and not sufficiently thorough.\u003c/p\u003e\n\u003cp\u003eThe current study comprehensively explored the relationship between the ARNTL2 and lung adenocarcinoma, in order to explore the roles of ARNTL2 in the development, progression and treatment of lung adenocarcinoma. The effects of ARNTL2 on human lung adenocarcinoma cells in terms of tumor mutational burden (TMB), microsatellite instability (MSI), immune microenvironment, ferroptosis, and drug sensitivity were further evaluated, aiming to provide potential targets and ideas for the treatment of lung adenocarcinoma. In addition, we experimentally demonstrated that ARNTL2 promotes tumor cell proliferation, migration, and invasion in lung adenocarcinoma cell lines A549 and H1299.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003ch2\u003eGene expression with clinical features\u003c/h2\u003e\n\u003cp\u003eA previous study found that ARNTL2 was frequently highly expressed in many cancers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The differential expression between tumor and adjacent normal tissues for ARNTL2 across TCGA is shown in Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA. To further validate whether ARNTL2 was differentially expressed in lung adenocarcinoma tissues, we conducted an analysis of 513 cases of LUAD patient data from the TCGA database, which showed significantly higher ARNTL2 expression in LUAD tissues than in normal adjacent tissues, (P \u0026lt; 0.001). The same conclusion was obtained from the validation after combining the GTEx data (Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). In addition, we divided the lung adenocarcinoma patients in the TCGA database equally into two groups according to the high and low expression of ARNTL2, and found there were differences in the baseline characteristics of the two groups (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Figure 1). Specifically, patients in the high-ARNTL2 group were more male and had more advanced N stage and AJCC stage.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLUAD patient characteristics according to ARNTL2 expression\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ehigh- group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elow- group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal evaluated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge(years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.931\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.3\u0026plusmn;10.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.3\u0026plusmn;9.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoking history\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.217\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic T stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic N stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic M stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAjcc 8th stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadiation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.781\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo/Unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemotherapy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.350\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo/Unknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eSurvival analysis\u003c/h2\u003e\n\u003cp\u003eWe analyzed the prognosis of 504 patients with lung adenocarcinoma in the TCGA database in relation to ARNTL2 expression and found that high ARNTL2 expression predicted poor survival. Results are shown in Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, ARNTL2-high group \u003cem\u003evs\u003c/em\u003e ARNTL2-low group: OS: HR= 1.53 (95%CI, 1.14 - 2.05) p = 0.005; PFS: HR= 1.35 (95%CI, 1.03 - 1.78) p = 0.031.\u003c/p\u003e\n\u003cp\u003eFor lung adenocarcinoma patients in the TCGA database, we also validated the correlation between survival and clinical factors. A total of 513 lung adenocarcinoma participants were involved in univariate and multivariate analyses to validate survival factors. Univariate analyses revealed that ARNTL2 (p ＜ 0.001), T stage (p ＜ 0.001), and N stage (p ＜ 0.001) were statistically significant predictors of tumor-specific survival. According to multivariate analysis, ARNTL2 (p ＜ 0.001), T stage (p = 0.005), and N stage (p = 0.001) remained independent prognostic predictors for LUAD patients. The details of the correlations between survival outcomes and parameters are shown in the following forest plot (Figure 1D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eTMB and mRNAsi analyses\u003c/h2\u003e\n\u003cp\u003eAs one of the immunotherapy biomarkers, tumor mutation burden (TMB) has attracted more and more attention in recent years, we analyzed the relationship between ARNTL2 gene expression and TMB in lung adenocarcinoma patients according to the TCGA database, and the results showed that there was a degree of positive correlation between ARNTL2 gene expression and TMB (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), which may indicate that in lung adenocarcinoma patients, as ARNTL2 gene expression increases, TMB also increases. Thus ARNTL2 may have a reference value for immunotherapy.\u003c/p\u003e\n\u003cp\u003eIt has been previously shown that mRNAsi is a valid method to evaluate the level of tumor differentiation. Histopathology confirmed that higher values of mRNAsi tend to represent a greater degree of tumor dedifferentiation. AS shown in Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, in lung adenocarcinoma, the mRNAsi was significantly higher in the lung tumor specimens than in normal lung tissue. Furthermore, compared with the low ARNTL2 expression group, the high ARNTL2 group showed significantly higher mRNAsi, which may be related to the worse prognosis of patients in the high ARNTL2 group.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003eMSI\u003c/h2\u003e\n\u003cp\u003eMicrosatellites (MS), also known as short tandem repeats (STR) or simple sequence repeats (SSR), are structures consisting of repetitive sequences of 1-6 nucleotides\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. MSI is an important factor in tumorigenesis and progression and has been extensively studied in tumors such as colorectal cancer. Early diagnosis of MSI is of great significance to the prognosis and treatment of MSI\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. We found that the expression level of ARNTL2 and MSI were negatively correlated in lung adenocarcinoma patients (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eSomatic mutations\u003c/h2\u003e\n\u003cp\u003eBased on previous reports, the number of somatic mutations is thought to correlate with prognosis. The changes in the copy numbers' variation and distribution of somatic mutations in high-ARNTL2 and low-ARNTL2 groups were analyzed. The results showed that TP53 had a high mutation rate in the high-ARNTL2 group (55% \u003cem\u003evs.\u003c/em\u003e 41%, P-value \u0026lt; 0.01). In contrast, some genes, such as RELN (11% for the low subgroup and 18% for the high subgroup), had a lower mutation rate in the high ARNTL2 group (Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eDEG, GO, KEGG analyses and single-cell analysis\u003c/h2\u003e\n\u003cp\u003eThe prognosis of lung adenocarcinoma patients with different ARNTL2 expression levels may be related to DEGs. So, we have analyzed the expression profiles from the TCGA data, to derive the landscape of biological differences. The prognosis of lung adenocarcinoma patients with different ARNTL2 expression levels may be related to DEGs. So, we have analyzed the expression of DEGs to derive landscape of biological differences (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), and 185 significant different genes were found, including 114 upregulated genes in the high-ARNTL2 group such as SLC2A1, CD109, and ADGRF4 (adjusted P \u0026lt; 0.01), and 71 downregulated genes including C16orf89, IRX5, and IRX3 (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003eFunctional enrichment of GO and KEGG in the 185 DEGs were analyzed. These pathways were associated with high-ARNTL2 expression. Cell cycle, apoptosis, IL-17 signaling pathway, and p53 signaling pathway ranked top in the high-ARNTL2 group (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eIn addition, we performed a single-cell analysis of lung adenocarcinoma cells through the CancerSEA database. The results showed that ARNTL2 gene expression was highly correlated with Angiogenesis, EMT, and Metastasis (Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, Supplementary Table 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eImmune infiltration and immune checkpoint analyses\u003c/h2\u003e\n\u003cp\u003eThe composition and proportion of immune cells in the tumor microenvironment (TME) have a significant impact on tumor development and treatment\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In lung adenocarcinoma, CD8+ and CD4+ T cells showed a significant positive correlation with ARNTL2 expression. In contrast, NK cell and Macrophage M2 showed a negative correlation (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). In addition, some common genes closely related to immune checkpoints also differed with the high expression of ARNTL2 (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Compared to the low-ARNTL2 expression group, the high expression group had significantly higher expression of CD274, CTLA4, LAG3, and PDCD1, etc. These differences in the immune microenvironment may provide ideas for immunotherapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eFerroptosis and drug-sensitive analyses\u003c/h2\u003e\n\u003cp\u003eComparison of important genes associated with ferroptosis between the high and low expression ARNTL2 groups showed that ACSL4, CDKN1A, and MT1G, etc. were overexpressed in the high expression group (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), which may predict a correlation between ARNTL2 expression and ferroptosis in lung adenocarcinoma.\u003c/p\u003e\n\u003cp\u003eANRTL2 expression also appears to have effects on drug sensitivity in lung adenocarcinoma, with IC50s significantly lower in the high ANRTL2 expression group than in the low ANRTL2 expression group for Gefitinib, Gemcitabine, Cisplatin, and Paclitaxel, although there was no significant difference in sensitivity to erlotinib between the two groups (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eIHC and immunofluorescence: ARNTL2 is highly expressed in lung adenocarcinoma tissues\u003c/h2\u003e\n\u003cp\u003eThe results of immunohistochemistry further confirmed the increased expression of ARNTL2 in lung adenocarcinoma. As shown in Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, the overexpression of ARNTL2 was seen in resected tumor tissues compared with adjacent normal tissues. Similar results were obtained by immunofluorescence analysis (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eARNTL2 promotes proliferation, invasion, and migration of LUAD cells\u003c/h2\u003e\n\u003cp\u003eTo identify the effect of ARNTL2 on proliferation and migration of lung adenocarcinoma cells, we established cell model of ARNTL2 downregulation by stably transducing with ARNTL2 shRNA-expressing lentiviruses in A549 and H1299 cells. Next, we detected the expression level of ARNTL2 by Western blot after transfection, the stable knockdown efficiency of shRNA at protein expression level was verified (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). ARNTL2 knockdown was very efficient in A549 and H1299 cell lines\u003c/p\u003e\n\u003cp\u003eWe subsequently performed CCK8, Clone formation, wound healing, and transwell assays to explore the effect of ARNTL2 in tumor proliferation and migration. CCK8 assays showed when ARNTL2 was downregulated, compared with the control cells, the proliferation ability was decreased in both A549 and H1299 cells (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). To further testify the role of ARNTL2 in lung adenocarcinoma formation, Clone formation of ARNTL2-knockdown and control cells was assessed. The results showed that ARNTL2-knockdown cells formed significantly fewer colonies compared to control cells (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE), which indicates ARNTL2 is essential for the oncogenicity of lung adenocarcinoma cells.\u003c/p\u003e\n\u003cp\u003eWe also investigated whether ARNTL2-knockdown cells affected migratory and invasion. ARNTL2-knockdown significantly reduced the migration of A549 and H1299 cells in a wound-healing assay, shControl cells almost recovered the wound within 48 hours, but shARNTL2 cells still have large areas of unhealed wounds (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). As shown in figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB, ARNTL2-knockdown cells also showed significantly lower infiltration rates in the transwell migration and invasion assays compared to control cells (p \u0026lt; 0.05). These results suggest that ARNTL2 may be a key promoter of lung adenocarcinoma cell growth and invasion.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCircadian rhythm disorders may be associated with a variety of diseases, such as obesity and depression\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In addition, Strong epidemiological evidence links circadian disruption with cancers\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, in recent years, a large number of studies have demonstrated that circadian rhythm disorders may be associated with the development of various cancers in human being\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. As an important biorhythm-related gene, ARNTL has been reported to play an oncogenic role in many human cancers. For example, Prior studies found that upregulation of ARNTL2 is associated with poor survival and immune infiltration in clear cell renal cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Mazzoccoli et al. reported that ARNTL2 is upregulated in colorectal cancer and is related to tumor invasiveness and aggressiveness\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. There are many other studies that demonstrated that high expression of Arntl2 was associated with poor survival in BRCA, LIHC, and pancreatic ductal adenocarcinoma \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we performed a comprehensive analysis by integrating available data from TCGA and GTEx database. We found that ARNTL2 expression was correlated with the pathological N-stage of lung adenocarcinoma patients. Similar results have been reported in colorectal cancer. And patients with high ARNTL2 expression had a worse prognosis and could serve as an independent predictor of prognosis in lung adenocarcinoma. Furthermore, a prognostic nomogram including ARNTL2 based upon the results of multivariate Cox analysis was constructed to predict the long-term survival of LUAD patients. Our study also confirmed that ARNTL2 is upregulated in lung adenocarcinoma, which is consistent with some previous reports: Arntl2 was upregulated in BLCA, BRCA, COAD, and READ\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. ARNTL2 can also promote proliferation and migration of lung adenocarcinoma cells, which suggests that ARNTL2 may be a key promoter of lung adenocarcinoma cell growth and invasion. In addition, a positive correlation between ARNTL2 expression and TMB was found in lung adenocarcinoma. In contrast, MSI showed a negative correlation with ARNTL2 expression.\u003c/p\u003e \u003cp\u003eAlthough much progress in the treatment of lung cancer has been made in recent decades, lung cancer remains the deadliest malignancy worldwide and significantly affects the quality of life of patients\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Especially for advanced patients, the 5-year survival rate of stage IV lung cancer patients is only 4.7%\u003csup\u003e20\u003c/sup\u003e. Immunotherapy is currently attracting increasing attention in the treatment of lung cancer. However, there are still many patients who benefit less from immunotherapy due to insensitivity to drugs. Therefore, it is vital to discover new effective prognostic biomarkers and specific immune-related therapeutic targets. Our study shows that in lung adenocarcinoma, ARNTL2 causes a significant impact on the immune microenvironment. For example, ARNTL2 expression levels were strongly positively correlated with CD8+ T cells, conversely, strongly negatively correlated with Natural killer (NK) cells. NK cells play an important role in the induction of both innate and adaptive immune responses. Many studies have demonstrated the critical role of NK cells in the control of lung cancer: NK cells can infiltrate lung cancer and a significant positive correlation between the number of tumor-infiltrating NK cells and the survival rate of patients after surgery was reported\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Therefore, a previous study suggested that immunotherapy targeting NK cells may be a breakthrough point in treatment for lung cancer\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. There were also significant differences in immune checkpoints between high-ARNTL2, and low-ARNTL2 group patients. Immune-checkpoint inhibitors (ICIs) is currently making a splash in the treatment of non-small cell lung cancer\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, further research on the relationship between ARNTL2 and immune checkpoints may be meaningful and necessary. In addition, numerous studies have demonstrated that TMB and MSI can be used to predict the effect of immunotherapy\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, for example, tumor cells with high TMB are reported to be more easily recognized by immune cells, and thus respond more significantly to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which may be used in combination with TNM staging to predict the progression and prognosis of patients with advanced tumors. We found that in lung adenocarcinoma, tumor immune infiltration, immune checkpoints, MSI and TMB were all associated with ARNTL2 expression levels. The above findings may deserve more in-depth studies and help with immunotherapy for a subset of lung adenocarcinoma patients in the future.\u003c/p\u003e \u003cp\u003eWe found that ARNTL2 gene expression was highly correlated with EMT and Metastasis by single-cell analysis, which This could potentially explain why LUAD patients with high-ARNTL2 expression have a worse prognosis. Indeed, the role of ARNTL2 in promoting tumor invasion and metastasis has been reported in a variety of tumors. A previous study of colorectal cancer found that high ARNTL2 expression was significantly associated with vascular invasion and lymph node metastasis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In addition, several studies have shown that ARNTL2 is associated with the development and metastasis of breast cancer\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and Ha NH, et al. indicated that affect the expression levels of ARNTL2 has a significant effect on metastatic progression in ER-breast cancers\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. It has also been demonstrated that in PDAC, ARNTL2 could affect tumor proliferation, migration, and invasion through the TGF/BETA pathway\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Brady et al. reported that ARNTL2 could drive metastatic self-sufficiency in lung cancer\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Moreover, Epithelial-to-mesenchymal transition (EMT) has been increasingly recognized to promote carcinoma invasion and metastasis. A previous study confirmed that in colon carcinoma, downregulation of ARNTL2 could suppress tumor cell proliferation and migration via SMOC2-EMT through inactivation of PI3K/AKT signaling pathway\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These studies suggest that ARNTL2 may contribute to tumor development through multiple mechanisms, such as the promotion of metastasis and EMT.\u003c/p\u003e \u003cp\u003eBy applying bioinformatics analysis, we found that there was a high correlation between ARNTL2 expression level and promotional effect on angiogenesis in lung adenocarcinoma cells. In addition, ARNTL2 expression levels appear to have an effect on ferroptosis and drug sensitivity in lung adenocarcinoma cells. However, there are no reports on related fields, further experimental validation is required. This will be the next step of our future work which may could help to reveal deeper mechanisms.\u003c/p\u003e \u003cp\u003eThere were also some limitations to this study. Although this study used a massive cohort of TCGA and GEO databases to develop and validate the role of ARNTL2 in lung adenocarcinoma, selection bias could not be avoided because of the retrospective nature of our study design. In addition, this experiment did not conduct more in-depth experiments to explore the mechanism of ARNTL promoting tumor metastasis and EMT in lung adenocarcinoma, which is also the direction of our future research.\u003c/p\u003e \u003cp\u003eARNTL2 is highly expressed in lung adenocarcinoma, and high ARNTL2 expression was associated with lymph node metastasis and was an independent predictor of worse prognosis for lung adenocarcinoma patients. Notably, the expression of ARNTL2 in lung adenocarcinoma is associated with a variety of features such as tumor immunity, ferroptosis, MSI, and drug sensitivity, which may provide a prediction and reference for immunotherapy. ARNTL2 may also be associated with the promotion of tumor metastasis, EMT, and angiogenesis. Through experiments, we verified that it could promote the proliferation, invasion and metastasis of lung adenocarcinoma. In conclusion, ARNTL2 may be a potential biomarker for lung adenocarcinoma, it may be useful to predict prognosis and guide the treatment in a subgroup of LUAD patients, and may be a new target for therapeutic approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eData processing\u003c/h2\u003e\n\u003cp\u003eGene expression data of LUAD patients (FPKM normalized) and corresponding clinical and survival information of The Cancer Genome Atlas (TCGA) were downloaded from the UCSC Xena browser (GDC hub: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdc.xenahubs.net\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e34\u003c/sup\u003e. The data with missing prognosis information, including outcome status and survival time was removed. The edition of lung cancer staging was classified according to the American Joint Committee on Cancer (AJCC) TNM Classification for Lung and Pleural Tumors (eighth edition).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eGenome statistical analysis\u003c/h2\u003e\n\u003cp\u003eTCGA cases were divided into high or low groups based on the median values of ARNTL2 expression level. Genome statistical analyses performed in R version 4.0.3 were as follows:\u003c/p\u003e\n\u003cp\u003e(a) differentially expressed genes (DEGs): The limma package was used to identify DEGs and miRNAs. The moderated t-test was adopted to calculate DEGs and miRNA expression changes, and the \u003cem\u003eP\u003c/em\u003e value was adjusted as FDR by Benjamini and Hochberg method. The log fold change \u0026gt; 0.5, and the adjusted \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05\u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(b) copy number variations (CNV) and microRNAs (miRNAs): the maftools package were used to compare the distribution of somatic mutations and the types of copy number variations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The adjusted \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.01 was used to assess the significance of the mutational frequency. The somatic mutations and the types of copy number variations between high and low-ARNTL2 groups were compared by Kruskal\u0026ndash;Wallis test, and the adjusted p-value \u0026lt;0.05 were considered statistically significant. Results were shown with the oncoplot function.\u003c/p\u003e\n\u003cp\u003e(c) GO and KEGG: The involved pathways and biological functions of the DEGs were performed with GO and KEGG pathway enrichment analysis by the \u0026ldquo;clusterProfiler\u0026rdquo; R package\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The cut-off value was set as adjusted P \u0026lt; 0.05 and false discovery rate (FDR) \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e(d) Ferroptosis analysis: Ferroptosis related genes are derived from Ze-Xian Liu et al.'s systematic analysis of the aberrances and functional implications of Ferroptosis in Cancer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(e) mRNAi analysis: We use the OCLR algorithm constructed by Malta et al. to calculate mRNAsi\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Based on the characteristics of mRNA expression, the gene expression profile contains 11,774 genes. We use the same Spearman correlation (RNA expression data), and then subtract the minimum value and divide by the linear transformation of the maximum value maps the dryness index to the range [0,1].\u003c/p\u003e\n\u003cp\u003e(f) drug sensitivity analysis: We predicted the chemotherapeutic response for each sample based on the largest publicly available pharmacogenomics database [the Genomics of Drug Sensitivity in Cancer (GDSC), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/].The\u003c/span\u003e\u003c/span\u003e prediction process was implemented by R package \u0026ldquo;pRRophetic\u0026rdquo; where the samples' half-maximal inhibitory concentration (IC50) was estimated by ridge regression and the prediction accuracy. All parameters were set by the default values with removal of the batch effect of combat and tissue type of allSoldTumours, and duplicate gene expression was summarized as mean value\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(g) immune infiltration and immune checkpoints: to make reliable immune infiltration estimations, we utilized the QUANTISEQ method in the R package Immunodeconv\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 were selected to be immune-checkpoint\u0026ndash;relevant transcripts and the expression values of these eight genes were extracted. The QUANTISEQ method (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://quantuseq.stanford.edu\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e43\u003c/sup\u003e was selected to analyze the relative levels of the 10 tumor-infiltrating immune cell phenotypes based on the RNA-seq expression profiles. The distribution of 10 tumor-infiltrating immune cell types between worse and better prognostic signature was investigated.\u003c/p\u003e\n\u003cp\u003e(h) Correlation analysis of ARNTL2 expression and TMB/MSI\u003csup\u003e44, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e: we used Spearman\u0026rsquo;s correlation analysis to describe the correlation between quantitative variables without a normal distribution. The value range of Pearson correlation coefficient is [-1,1] with a higher absolute value indicating a stronger association and the sign indicating a positive or negative association between the two variables. The density curve on the right represents the distribution trend of the TMB/MSI score; the upper density curve represents the distribution trend of the gene.\u003c/p\u003e\n\u003cp\u003eAll the above analysis methods and R package were implemented by R foundation for statistical computing (2020) version 4.0.3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eSingle-cell analysis\u003c/h2\u003e\n\u003cp\u003eSingle-cell analysis was performed through the CANCERSEA website. CancerSEA is the first dedicated database that aims to comprehensively decode distinct functional states of cancer cells at single-cell resolution\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003ePrognosis analysis\u003c/h2\u003e\n\u003cp\u003ePrognostic information was analyzed in the PrognoScan database and Kaplan\u0026ndash;Meier plotter database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analyses in the current study were completed by R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria). The R package included survival, rms and ggplot2. Statistical significance was set at a two-sided p value \u0026lt; 0.05. Categorical variables were compared using Fisher\u0026rsquo;s exact test and Pearson\u0026rsquo;s c2 test and continuous variables were compared using Student\u0026rsquo;s t-test and the Wilcoxon test. Multivariable Cox regression analyses were used to test independent prognostic value using the R package survival and the coxph function.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eCell culture and lentivirus infection\u003c/h2\u003e\n\u003cp\u003eLung adenocarcinoma cell lines (A549 and H1299) were purchased from the Chinese Academy of Sciences Cell Bank. Cells were cultured in DMEM (Hyclone, Logan, UT, USA) supplemented with 10% fetal bovine serum (Hyclone), 100 U/mL penicillin, and 100 U/mL streptomycin in a humidified 5% CO2 atmosphere at 37\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eTwo different short hairpin RNAs (shRNAs) encoding shARNTL2 and scramble shRNA control (catalogue) were designed by Shanghai Genechem Co.,Ltd. and cloned into a lentivirus vector with puromycin resistance, which was then transfected into cells and screened with puromycin to ensure transfection efficacy. Viral transduction was performed as the manufacturer\u0026rsquo;s protocol. After 3 days of virus transfection, knockdown was verified by western blot analysis using ARNTL2-specific antibody (ab221557, abcam). Sequences of the shRNAs and the control are provided in Supplementary table 1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eWestern blot\u003c/h2\u003e\n\u003cp\u003eRIPA buffer (Beyotime, Shanghai, China) containing protease and phosphatase inhibitors cocktail (Beyotime) were used to extract proteins, enhanced BCA Protein Assay Kit (Beyotime) was used for protein quantification. As previously reported, Protein samples were separated by electrophoresis on SDS-PAGE and transferred to a polyvinylidene difluoride membrane (Merck-Millipore, Burlington, MA, USA). After the transfer, the blots were blocked with 5% milk for 1 hour and incubated with primary antibody for 12h at 4\u0026deg;C. Then, tris-buffered saline Tween-20 (TBST) was used to wash the membranes three times. After washing, the membranes were incubated with secondary antibodies at room temperature for 1 h. Finally, the protein bands were visualized and analyzed using a Moon Chemiluminescence Reagent kit (Beyotime). The following antibodies were used in this study: Anti-ANRTL2 antibody (1:1,000, abcam, ab221557)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eCell proliferation assay\u003c/h2\u003e\n\u003cp\u003eWe seeded 2000 cells that were in the logarithmic growth phase, per well in a final volume of 100 \u0026micro;L growth medium into black 96-well plates. After incubation at 37\u0026deg;C for 0, 24, 48, 72, 96, and 120 h, the cell proliferation was measured by Enhanced Cell Counting Kit-8 Viability Assay Kit (Beyotime).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eWound healing assay\u003c/h2\u003e\n\u003cp\u003eControl shRNA and shARNTL2 cells were inoculated on the 6-well plate on average. Cells were grown into monolayer and manual scratching with a 200-\u0026micro;l pipette tip, then cells were rinsed with PBS and incubated at 37\u0026deg;C in serum-free media. Photographs of the wounded areas were taken every 24h by phase-contrast microscopy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n\u003ch2\u003eTranswell migration and invasion assay\u003c/h2\u003e\n\u003cp\u003eCell migration and invasion abilities were measured by Transwell assays using the 24-well transwell chambers with 8 \u0026micro;m polycarbonate membranes (Corning, NY). the uncoated were used to determine migration and pre-coated with Matrigel Basement Membrane Matrix were used to determine invasion (BD Biosciences). The chambers were rehydrated in a serum-free medium for 2 hours as described by the manufacturer. Then the upper chambers were added with serum-free medium, while the lower chambers were added with serum medium. Cells were seeded onto the upper chambers with a density of 5\u0026times;104 cells per well, and incubated for 24 h at 37\u0026deg;C, 5% CO2. Cells migrated toward the lower chambers were fixed with methanol and stained with 0.5% crystal violet. Each assay was photographed under the inverted microscope (Olympus), and the number of cells within each chamber was counted by ImageJ software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n\u003ch2\u003eClone formation assay\u003c/h2\u003e\n\u003cp\u003eControl or ARNTL2 shRNA-transduced A549 and H1299 cells (3 \u0026times; 103 cells/well) were cultured in 6-well plates were cultured at 37\u0026deg;C in 5% CO2 environment. ARNTL2 shRNA-transduced or control cells were seeded in 6-well plates at a density of 5 \u0026times; 106 cells per well and cultured at 37\u0026deg;C in 5% CO2 environment. After 9 days, cells were stained with 4% formaldehyde/0.005% gentian violet solution and captured under the inverted microscope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n\u003ch2\u003eImmunohistochemistry and immunofluorescence\u003c/h2\u003e\n\u003cp\u003eThe tissue specimens were collected from both tumor and adjacent normal tissues of 100 patients with LUAD who received surgery from September to November 2020 in the Zhongshan Hospital. As previously reported\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, the tissues were stained by a GTVision + Detection System/Mo\u0026amp;Rb Immunohistochemistry kit (GK500710, GeneTech, Shanghai, China) following the manufacturer's protocol. Specifically, the 5-\u0026micro;m paraffin-embedded tissues were dewaxed, rehydrated, and incubated with antibodies against ARNTL2 (1:500, Abcam, Cambridge, UK) at 4\u0026deg;C overnight, and then were incubated with biotinylated secondary antibodies. For immunofluorescence, sections were incubated with primary antibodies against ARNTL2 (rabbit polyclonal, 1:500), followed by incubation with the respective secondary antibodies (Cy3-labeled goat anti-rabbit IgG). DAPI nuclear counterstaining was then performed. Finally, fluorescence microscope was used to take micrographs.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLUAD: lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eARNTL2: Aryl Hydrocarbon Receptor Nuclear Translocator Like 2\u003c/p\u003e\n\u003cp\u003eTMB: tumor mutation burden\u003c/p\u003e\n\u003cp\u003eMSI: Microsatellite instability\u003c/p\u003e\n\u003cp\u003eTCGA: The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eAJCC: American Joint Committee on Cancer\u003c/p\u003e\n\u003cp\u003eDEGs: differentially expressed genes\u003c/p\u003e\n\u003cp\u003eCNV: copy number variations\u003c/p\u003e\n\u003cp\u003emiRNAs: microRNAs\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eFDR: false discovery rate\u003c/p\u003e\n\u003cp\u003eGDSC: the Genomics of Drug Sensitivity in Cancer\u003c/p\u003e\n\u003cp\u003eIC50: half-maximal inhibitory concentration\u003c/p\u003e\n\u003cp\u003eshRNAs: short hairpin RNAs\u003c/p\u003e\n\u003cp\u003eqRT-PCR: Quantitative real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eTBST: tris-buffered saline Tween-20\u003c/p\u003e\n\u003cp\u003eMS: Microsatellites\u003c/p\u003e\n\u003cp\u003eSTR: short tandem repeats\u003c/p\u003e\n\u003cp\u003eSSR: simple sequence repeats\u003c/p\u003e\n\u003cp\u003eTME: tumor microenvironment\u003c/p\u003e\n\u003cp\u003eNK cells: Natural killer cells.\u003c/p\u003e\n\u003cp\u003eICIs: immune-checkpoint inhibitors\u003c/p\u003e\n\u003cp\u003eEMT: Epithelial-to-mesenchymal transition\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the academic support from the Home for Researchers. We also have asked the International Science Editing Corporation to edit the language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormatting of funding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shanghai Medical Innovation Research Project (Grant No. 20Y11908200).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization was contributed by HW, ML, and QW; Data collection and curation were contributed by HZ, GS, and XJ; Data analysis and interpretation were contributed by ML, HZ, GS, XJ, MF, HW, and ZC; Draft of the manuscript was contributed by HZ, ML and XY; Critical revision of the manuscript was contributed by MF, and CZ; Final approval of manuscript and submission were contributed by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, et al. 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Mar 23 2021;21(1):181. doi:10.1186/s12935-021-01878-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ARNTL2, lung adenocarcinoma, multi-omics, integrative analysis","lastPublishedDoi":"10.21203/rs.3.rs-1082517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1082517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between biorhythm and cancer has been increasingly reported in recent years. As one of the core transcription factors of biorhythm, ARNTL2 is thought to be implicated in the occurrence and development of many malignant tumors, such as breast cancer. However, the role of ARNTL2 in lung adenocarcinoma remains elusive. In the current study, we found that expression of ARNTL2 was markedly upregulated in lung adenocarcinoma, and high ARNTL2 expression was correlated with advanced N stage and poor survival in patients. Moreover, ARNTL2 expression levels are closely associated with many important features of lung adenocarcinoma, such as tumor immune microenvironment, ferroptosis, microsatellite instability, tumor mutational load, and drug sensitivity. ARNTL2 could also promote proliferation, invasion, and metastasis of lung adenocarcinoma cells. In addition, we found that high ARNTL2 expression might promote epithelial-mesenchymal transition, metastasis and promotion of angiogenesis of tumor cells. In conclusion, our study reveals that ARNTL2 is a prognostic and promising therapeutic biomarker for people with lung adenocarcinoma.\u003c/p\u003e","manuscriptTitle":"Targeting the ARNTL2 Gene as a Potential Strategy for Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-30 15:59:11","doi":"10.21203/rs.3.rs-1082517/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0d69905-391d-440e-80f3-35a69d1d7aa5","owner":[],"postedDate":"November 30th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8761016,"name":"Translational Medicine"}],"tags":[],"updatedAt":"2021-12-03T17:01:55+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-30 15:59:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1082517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1082517","identity":"rs-1082517","version":["v1"]},"buildId":"re_ckhLnmML6MCF96OHNJ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}