Four and a half LIM domain protein 1 as a Novel Prognostic Biomarker and Correlation with Immune Infiltration Levels in Lung Adenocarcinoma

Four and a half LIM domain protein 1 as a Novel Prognostic Biomarker and Correlation with Immune Infiltration Levels in 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 Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Four and a half LIM domain protein 1 as a Novel Prognostic Biomarker and Correlation with Immune Infiltration Levels in Lung Adenocarcinoma Jingtao Zhang, Minghao Guo, Jing Zhang, Guangming Zhang, Ning Sun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1239170/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 Background: We aimed to investigate the prognostic value of Four and a half LIM domain protein 1 (FHL1) and its correlation with FHL1 and tumor-infiltration immune cells (TIICs) in lung adenocarcinoma (LUAD). Methods: FHL1 expression status and its influence on clinical characteristics in LUAD and Lung squamous cell carcinoma (LUSC) were collected based on GEPIA, TCGA, GEO, CPTAC, the HPA database, and the GTEx Portal. The ROC curve and Kaplan-Meier plots were used to assess the value of FHL1 expression levels in the diagnosis and prognosis of LUAD and LUSC. The interaction network revealed the related genes and proteins of FHL1 by GeneMANIA and STRING. The functional enrichment analysis based on FHL1 and FHL1-related differentially expression genes (DEGs) was conducted by the “clusterProfile” package and Metascape, respectively. The correlation analysis between FHL1 expression and tumor immunity was performed using TISIH, TIMER, and TISIDB. cBioPortal was used to investigate the mutation status between FHL1 and representative immune checkpoints. Results: The results showed that FHL1 expression was significantly lower in tumors relative to adjacent standard samples, and downregulated FHL1 predicted a worse prognosis for LUAD than that for LUSC. Additionally, FHL1 participated in the interleukin 15 mediated signaling pathway, response to interleukin-9, and neutrophil-mediated cytotoxicity. It was also positively correlated with TIICs (B cells, CD8 + T, CD4 + T cells, macrophages, neutrophils, and DC), immune checkpoints (CD80, CD48, VTCN, and PVR), and chemokines (CCL5, CCL17, CCL20 and CXCL8). Conclusion: FHL1 is a powerful prognostic biomarker of immune infiltration. FHL1 LUAD tumor-infiltrating immune cells biomarker prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers[1], with an annual increase in incidence and mortality globally. LUAD has surpassed LUSC to become the most common subtype of NSCLC. Unfortunately, it has been reported that the average 5-year survival rate is only 15%[2,3] due to the high rates of invasion and metastasis. The immune system serves as a key player in identifying and removing tumor cells, and immunotherapies have demonstrated a remarkable and durable response in NSCLC[4]. However, only a small subset of patients benefits from it in clinical practice due to the lack of valid biomarkers. Therefore, it is critical to identify specific markers and immunotherapy targets to improve the survival rate of LUAD. FHL1 is a member of the FHL protein family, characterized by four complete LIM domains and an N-terminal half LIM domain[5]. The FHL1 protein is mainly expressed in skeletal muscle and regulates skeletal growth[6]. Recent studies reported that FHL1 plays a crucial role in inhibiting cancer cell growth, migration, and invasion, and it is significantly downregulated in lung cancer, breast cancer, liver cancer, gastric cancer, kidney cancer, and prostate cancer[7-10]. However, the role of FHL1 in tumor progression is complex, and previous studies have validated that FHL1 regulates angiogenesis and the TGF-β-like signaling pathway and induces G1 and G2/M cell cycle arrest[8,9,11]. Therefore, FHL1 is a potential prognostic biomarker for LUAD. However, the mechanism underlying the association between FHL1 and immune infiltrates is unclear. Therefore, our study aimed to investigate the prognostic value of FHL1 and the association between FHL1 and TME based on bioinformatics techniques and multiple public databases. Furthermore, our results may present novel diagnostic and therapeutic targets and immunotherapeutic strategies for LUAD. Method Identification of NSCLC-related and LUAD-related differentially expressed genes The GSE118370, GSE63459, and GSE27262 datasets were acquired from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database, which includes NSCLC tissues and adjacent normal tissues. The raw data were processed and standardized with GEO2R, | log 2 (fold-change) | >1, and adjusted P-value < 0.05, which were considered as DEGs. The common intersection of three dataset DEGs was selected using the Venn diagram. To obtain LUAD-related DEGs, RNA sequencing data were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). The data were analyzed with threshold | log (fold change) | > 1.5 and adjusted P-value < 0.05, and visualization by conducted R packages of “ggplot2.” Validation of FHL1 expression level in LUAD and LUSC The gene expression datasets of FHL1 in LUAD, LUSC, and normal tissues were derived from the TCGA and GEO databases. Furthermore, the FHL1 protein expression level and distribution and localization between tumor and normal tissues were shown by immunohistochemistry pictures based on CPTAC databases and the HPA (https://www.proteinatlas.org/), respectively. In addition, the corresponding clinical characteristics of FHL1 expression, such as age, sex, smoking status, TNM stage, and tumor location, were also collected from TCGA. Furthermore, receiver operating characteristic (ROC) and Kaplan–Meier (K-M) analyses were used to evaluate FHL1 expression levels in the diagnosis and prognosis of LUAD and LUSC patients. FHL1-related Interaction Networks and Functional Enrichment Analysis Gene-gene interaction (GGI) and protein-protein interaction (PPI) networks were used to identify potential interaction genes and proteins of FHL1 based on GeneMANIA (http://genemania.org/) and STRING (https://string-db.org/). Correlation analysis further validated the relationship between these proteins and FHL1. Functional enrichment analysis was conducted with the “ClusterProfiler” package and further visualized using the “ggplot2” package. The Metascape database was used to investigate the functional enrichment analysis of FHL1-related DEGs in the LUAD dataset and the GO and KEGG analysis visualization by applying the “ggplot2” package in R. Relationship between FHL1 expression and immunity TISCH (http://tisch.comp-genomics.org) was used to assess the abundance of FHL1 expression in different components of the TME, including immune cells, malignant cells, others, and stromal cells. In addition, correlation analysis was performed to investigate the abundance of TIICs with FHL1 expression in LUAD based on the TIMER (https://cistrome.shinyapps.io/timer/) and TISIDB (http://cis.hku.hk/TISIDB/) databases. Furthermore, TISIDB was utilized to investigate the relationship between FHL1 expression and chemokines and immune checkpoints and the prognostic value of the representative immune checkpoints in LUAD. cBioPortal (http://cbioportal.org) was used to evaluate the frequency of genetic alterations in FHL1 and immune checkpoints in LUAD samples, as well as the tendency of co-occurrence and mutual exclusivity. Statistical Analyses Statistical analysis was performed using R software v4.0.5 and corresponding packages. Paired t -tests and Mann-Whitney U tests were performed to examine the differences between tumor samples and normal samples. Kaplan–Meier (K-M) analyses and log-rank tests were used to evaluate the prognostic value of FHL1. ROC curves were constructed to investigate the FHL1 and its role in sensitivity and specificity of diagnosis. Spearman’s correlation analysis was used to assess the correlation between FHL1 and immune infiltration cells, immune checkpoints, and chemokines. Differences were considered statistically significant at P < 0.05, except for the genetic alteration analysis. Q-value < 0.05 was considered statistically significant. Results Identification of NSCLC-related DEGs in LUAD The intersection analysis showed that 76 DEGs overlapped among the GSE118370, GSE63459, and GSE27262 datasets (Figure 1A). The top ten targets were VIPR1, ADARB1, PECAM1, CLDN18, NOTCH4, FHL1, TIMP3, TCF21, MACF1, and CD36 , considered key genes in NSCLC. Notably, FHL1 has been considered as a tumor suppressor gene that exerts an inhibitory effect through various mechanisms underlying cancer growth, invasion, and metastasis[9,12-14]. A recent study found that FHL1 can also play a promoting role in tumors[15]. Therefore, our study aimed to identify the function of FHL1 in NSCLC progression using bioinformatics. Downregulated expression of FHL1 in LUAD and LUSC Transcriptomic data were analyzed to systematically investigate the mRNA expression levels of FHL1 across diverse cancers based on TCGA and GTEx databases (Figure 1B). The results showed that the FHL1 expression level was significantly lower in 17 types of tumors than that in adjacent normal tissues, especially in LUAD and LUSC. GEPIA and GEO databases were used further to validate the findings of TCGA and GTEx web and displayed a low expression of FHL1 in LUAD and LUSC tissues compared with that of normal samples (Figure 1C-D). In addition, UALCAN was performed to examine the protein expression level of FHL1 based on the CPTAC database (Figure 1E), and the results showed that the FHL1 protein was remarkably decreased in tumor tissues compared to that of normal samples. Immunohistochemistry images from the HPA database showed results similar to those of CPTAC (Figure 1F). These findings suggest that both mRNA and protein expression levels of FHL1 are downregulated in LUAD and LUSC compared to those of normal tissues. Correlation Between FHL1 and Clinical Characteristics in NSCLC To clarify the association between the transcription of FHL1 and clinicopathological parameters in LUAD and LUSC patients, the information was analyzed using Wilcoxon and logistic regression analysis (Figure 2). The results of multiple subgroup analysis showed that FHL1 expression was significantly associated with age, sex, and smoking status and was downregulated in younger (age < 65 years), male, and smoking patients (p < 0.05, respectively). However, FHL1 expression was not correlated with other clinicopathological parameters, such as pathologic stage, TNM stage, and anatomic neoplasm subdivision (Figure 2A). Moreover, no statistical correlation was found between FHL1 expression and clinicopathological characteristics in LUSC, including age, sex, smoking status, TNM stage, pathologic stage, and anatomic neoplasm subdivision (Figure 2B) . Diagnosis and Prognosis value of FHL1 in LUAD and LUSC ROC curve analysis was performed to identify the role of FHL1 in distinguishing LUAD and LUSC samples from normal samples. As shown in Figure 3A and B, the area under the curve (AUC) of FHL1 was 0.993 (95% CI, 0.989–0.998) in LUAD and 0.998 (95% CI: 0.995–1.000) in LUSC, indicating that FHL1 may be a strong identification biomarker for LUAD and LUSC. The curves illustrate the association between FHL1 expression and overall survival (OS) and disease-specific survival (DSS), which helps to investigate the prognostic value of FHL1 in LUAD and LUSC based on K-M analysis. Figure 3A shows that LUAD patients with low expression of FHL1 were associated with shorter OS (P = 0.025) and poor DSS (P = 0.054). However, the levels of FHL1 were not significantly correlated with OS and DSS in LUSC patients (P = 0.475, P = 0.533, respectively) (Figure 3B). These findings suggest that low expression of FHL1 could be a promising biomarker to diagnose LUAD and LUSC, as well as the poor prognosis of LUAD patients. Because FHL1 expression is not associated with the prognosis and clinical characteristics of LUSC, the following study focused on the role of FHL1 expression in LUAD. PPI Networks and Functional Annotations To explore FHL1-correlated genes and FHL1-binding proteins, GGI and PPI were generated using GeneMANIA and STRING databases (Figure 4A and B). The correlation analysis suggested that the proteins (AKT1, IGFBP5, INPP5A, KCNA5, RBPJ, STAT3, STAT5A, STAT5B, and TTN) in the PPI network had a significant relationship with FHL1 expression, except for RING1 (Figure 4D). Figure 4C shows that FHL1 is associated with the biological functions of DNA-binding transcriptional activator activity, RNA polymerase l-specific, and RNA polymerase II repressing transcription factor binding and participates in the JAK-STAT signaling pathway, interleukin 15 mediated signaling pathway, and response to interleukin-9. Additionally, volcano plots and heatmaps were generated to identify differentially expressed genes (DEGs, | log (fold change) | > 1.5, and adjusted P-value < 0.05), based on the TCGA database (Figure 5A and B). Metascape was used to explore the role of FHL1-related DEGs in LUAD patients (Figure 5C - F). The results showed that the FHL1-related DEGs were involved in ERKl and ERK2 cascades, cell differentiation, and blood circulation. Biological process analysis further revealed that FHL1-related DEGs of LUAD participated in humoral immune response, vascular processes in the circulatory system, and neutrophil-mediated cytotoxicity. Moreover, KEGG analysis showed that these genes were closely related to the metabolism of xenobiotics by cytochrome P450. A recent study confirmed that cytochrome P450 is a crucial mediator of ferroptosis[16], considered a novel therapeutic strategy to treat NSCLC[17]. Altogether, FHL1 is strongly linked to the immune response. Therefore, the correlation between FHL1 and the anti-cancer immune response was investigated. Correlation of FHL1 expression with infiltrating immune cells To better understand the expression status of FHL1 in different cell types, the violin plots showed that FHL1 expression was the most frequent in immune cells, while only parts of stromal cells, malignant cells, and other cells were expressed (Figure 6A). Furthermore, establishing scatterplots to evaluate the correlation of FHL1 expression with the TIICs (Figure 6B) illustrates that FHL1 expression was positively associated with the level of B cells (r = 0.157, P = 5.21e-04), CD8 + T cells (r = 0.207, P = 4.05e-06), CD4 + T cells (r = 0.279, P = 4.41e-10), macrophages (r = 0.433, P = 1.45e-23), neutrophils (r = 0.275, P = 7.44e-10), and dendritic cells (r = 0.31, P = 2.40e-12), while being negatively related to tumor purity (r = −0.313, P = 1.03e-12). The enrichment score boxplot further validated that all six types of immune cells had a higher degree of immune infiltration in the high FHL1 expression group than that in the low FHL1 expression group (Figure 6C). Further, the correlation analysis based on the TISIDB database was used to confirm that the level of FHL1 was clearly correlated with TILs in diverse cancers (Figure 7A). As shown in Figure 7B, 26 TILs were closely related to FHL1 expression in LUAD. Specifically, FHL1 was significantly positively associated with 24 types of TILs but negatively associated with active CD4+T cells and CD56 dim cells in LUAD. Correlation of FHL1 expression with immune molecules TILs are important constituents of the tumor immune microenvironment and play a crucial role in antitumor efficacy and prognostic ability[18,19]. Immune checkpoints inhibit the anti-tumor immune response of TILs, contributing to tumor cell immune escape. To identify whether FHL1 impacts TIL infiltration via immune checkpoints, a correlation analysis between FHL1 expression and 47 immune checkpoint genes was performed (Figure 8A). The results showed that FHL1 was associated with most immune checkpoint genes, including CD274, CD48, CD80, VTCN, and PVR. Then, correlation analysis was performed to explore the relationship between FHL1 expression and chemokines (Figure 8B). The results showed that FHL1 expression levels were markedly associated with CCL5 (r = 0.097, P = 0.0267), CCL17 (r = -0.138, P = 0.00166), CCL20 (r = -0.172, P = 8.31e−05), and CXCL8 (r = -0.104, P = 0.018). These results revealed that FHL1 participated widely in regulating immune molecules, thereby affecting immune cell infiltration. Correlation of the genomic alteration between FHL1 and immune checkpoint Further, investigation of the prognosis of immune checkpoints based on TISIDB showed that PD-L1, PD-L2, CD80, CD86, VSIR, PVR, LGALS9, and CD48 were downregulated, while VTCIN, CD112, TNFSF4, CD70, and TNFSF18 were upregulated in LUAD (Figure 9). Notably, not all the different expressions of immune checkpoints have a prognostic role in LUAD. Briefly, low expression of CD80 and CD48 and high expression of VTCN1 indicated high OS and/or DFS, but downregulated PVR and was significantly related to poor OS and DFS (P = 0.012, 0.034 respectively). Moreover, mutation analysis revealed that FHL1 was altered in 2.3% of all study subjects, including missense mutations, splice mutations, truncating mutations, structural variants, amplifications, and deep deletions (Figure 10A). Figure 10B shows the correlation between FHL1 and immune checkpoints. In addition, alterations in PVR, NECTIN2, and HHLA2 have a co-occurrence tendency with FHL1 alterations. These results indicate that FHL1 may participate in regulating immune checkpoints in LUAD. Discussion Although surgical and targeted therapies have significantly improved, the mortality of LUAD continues to be high in the past decades, which seriously threatens human health. Therefore, as a novel cancer treatment, immunotherapy has attracted wide attention. Recently, an increasing number of studies have confirmed that immunotherapy is a successful option for LUAD patients[20,21]. Previous studies have validated that FHL1 is downregulated in LUAD, and it is closely related to tumor invasion and metastasis[7,9]. However, the correlation between FHL1 expression and immune infiltration has not been thoroughly investigated in LUAD. In this study, we analyzed the prognostic value of FHL1 and the relationship between FHL1 expression and immune cell infiltration based on bioinformatic analysis. This study revealed that FHL1 expression was significantly lower in LUAD tissues than that in normal tissues, and the expression level of FHL1 was affected by age, sex, and smoking status. Currently, the role of FHL1 in LUAD has not been thoroughly investigated. Previous trials suggested that FHL1 participates in cancer cell growth, migration, and invasion, which caused a worse prognosis[9,22]. K-M and ROC analyses were conducted to confirm the diagnostic and prognostic value of FHL1 in clinical settings. The results of the K-M analysis showed that low expression of FHL1 indicated poor OS and short DSS, consistent with the results of previous studies[9,22]. ROC analysis showed that FHL1 had a substantial AUC value, which played an important role in the diagnosis of LUAD. Therefore, this study suggests that FHL1 is a powerful diagnostic and prognostic biomarker. To further investigate the functions of FHL1 in LUAD, GO and KEGG analyses were performed to reveal that FHL1 regulates cell growth via DNA-binding transcription activator activity, RNA polymerase l-specific, and RNA polymerase II repressing transcription factor binding, as well as regulates human immunity through interleukin 15 mediated signaling pathway, and response to interleukin-9. Moreover, the FHL1-related DEGs of LUAD were co-regulated in humoral immune response and neutrophil-mediated cytotoxicity. These results show that FHL1 expression may play a crucial role in tumor growth and immune cell infiltration. However, the underlying mechanism by which FHL1 expression affects immune cells in the TME has not yet been reported. TIICs play a critical role in tumor progression through complex intercellular interaction networks and are associated with clinical prognosis. TISCH analysis results showed that FHL1 expression was more abundant in immune cells than that in stromal cells, malignant cells, and other cells in the TME. Regarding TIMER databases, FHL1 downregulation might be closely correlated with the low degree of TIL infiltration, especially for B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and DCs. Co-expression analyses based on TISIDB further validated the results of the TIMER database, revealing that FHL1 expression was positively correlated with CD8 + T cells, NK cells, and DCs. High CD8 + T cell infiltration levels were considered to be associated with excellent prognosis and more prolonged survival because CD8 + T cells play an important role in killing tumor cells[23]. Previous research has confirmed that NK cells release perforin and granzymes and excrete various cytokines to exert an antitumor effect[24]. DCs, as professional antigen-presenting cells, participate in T cell polarization and Th1 differentiation[25]. Altogether, FHL1 expression affects the level of TIL infiltration, which promotes the construction of an immunosuppressive environment. Moreover, to investigate the influence of FHL1 in the TME, including immune molecules, the TISIDB database was used to analyze the correlation between FHL1 expression and representative chemokines (such as CCL5, CCL17, CCL20, and CXCL8) and immune inhibitors (such as CD80, CD48, VTCN, and PVR). Notably, CCL20 has been confirmed to enhance the migration and proliferation of A549 cells and recruit TAM cells[26]. Liu, et al [27] found that CXCL8 was overexpressed in LUAD and acted as a poor prognostic factor promoting tumor progression, consistent with our results. Intriguingly, beyond the classic immune checkpoints PD-L1 and CTLA-4, FHL1 expression was significantly associated with CD80, CD48, and VTCN1, closely related to patient outcomes. CD80 acts as a surface ligand on immune cells, and CTLA-4 negatively regulates T-cell activation[28]. CD48 is a member of the signaling lymphocyte activation molecule family and participates in the adhesion and activation of immune cells[29]. VTCN1 belongs to the B7 family protein, is upregulated in LUAD, and negatively regulates T‐cell immunity by restraining T‐cell proliferation, cytokine secretion, and the development of cytotoxicity[30]. Therefore, FHL1 expression affects the function of tumor-antagonizing immune cells, weakening the effects of immune surveillance and contributing to immune escape and tumor progression. Interestingly, mutation analysis further revealed that PVR and NECTIN2 and HHLA2 alterations co-occurred with the FHL1 mutation. PVR and NECTIN2 inhibit the activation of T and NK cells by interacting with T cell immunoglobulin and ITIM domain (TIGIT)[31]. Furthermore, HHLA2 expression is closely correlated with CD8 T-cell infiltration status[32]. A recent study reported that HHLA2 was widely expressed in patients with PD-1-negative NSCLC, which indicated that HHLA2 might be a potential target for patients who do not respond to PD-1 pathway blockade[33]. These findings partly demonstrate the mechanism by which FHL1 regulates the expression of immune checkpoints in LUAD. Therefore, FHL1 can function as a novel target to investigate the immunosuppressive status of LUAD. Altogether, this study reported that FHL1 regulates anti-tumor immune responses through various mechanisms, including recruitment of different immune cells in the TME, thereby affecting the expression of chemokines and immune inhibitors, and the co-occurrence mutation of some immune checkpoints. These results suggest that FHL1 may impact patient outcomes by modulating the immunosuppressive microenvironment and could be a promising immunotherapeutic target for LUAD. Despite comprehensive and systematic evaluation of the role of FHL1 in LUAD, based on different public databases, there are several limitations. First, the row data were collected from public repositories, and the quality standards were nonuniform, which might have influenced the study outcomes. Second, the perspective of transcriptome and genome levels could not reflect overall aspects of immune status, and there is a lack of direct evidence to demonstrate the role of FHL1 expression in regulating the immune response impacting patient prognosis. Third, more in vivo and in vitro experiments are needed to further validate the mechanism of FHL1 expression in LUAD. In the follow-up research, we will further detect the underlying function of FHL1 in LUAD. Conclusion In summary, this study demonstrated that FHL1 was expressed at low levels in LUAD and revealed a correlation between FHL1 expression and tumor progression. Furthermore, FHL1 expression was strongly diagnosed as a prognostic factor for LUAD. In addition, FHL1 expression was related to immune cell infiltration, immunoinhibition, and chemokines, indicating that FHL1 is a better immunotherapy target. Therefore, FHL1 is considered a potential prognostic biomarker and immunotherapy target for LUAD. Abbreviations FHL1, four and a half LIM domain protein 1; TCGA: The Cancer Genome Atlas; TIICs: tumor-infiltration immune cells; LUAD, lung adenocarcinoma; LUSC: lung squamous cell carcinoma; DEGs: differentially expression genes; NSCLC: non-small cell lung cancer; BP: Biological processes; MF: Molecular function; CC: Cellular component; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; OS: Overall survival; ROC: Receiver operating characteristic. Declarations Acknowledgements Not applicable. Author contributions Jingtao Zhang wrote the original draft, Fei Xu and Minghao Guo prepared the figures and tables, Jing Zhang analyzed the data, Guangming Zhang downloaded the raw data from TCGA and GEO databases, Ning Sun reviewed the relevant literature, Wenqiang Cui proofread the manuscript, and Fei Xu edited the draft and made revisions. Funding The present study was supported by the National Natural Science Foundation of China (grant No. 82004281 and 82001190), the China Postdoctoral Science Foundation (grant No. 2021T140427 and 2021M691986), and the Development Plan of Shandong Medical and Health Technology (grant No. 2019WS581). Availability of data and materials All data generated or analyzed during this study are included in this published article. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Jemal, A., Siegel, R., Xu, J. & Ward, E. Cancer statistics, 2010. CA: a cancer journal for clinicians. 2010; 60 (5): 277-300. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2018. CA: a cancer journal for clinicians. 2018; 68 (1): 7-30. 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Roles of CD48 in regulating immunity and tolerance. Clinical immunology (Orlando, Fla.). 2016; 164: 10-20. Tsai, S. M., Wu, S. H., Hou, M. F., Yang, H. H. & Tsai, L. Y. The Immune Regulator VTCN1 Gene Polymorphisms and Its Impact on Susceptibility to Breast Cancer. Journal of clinical laboratory analysis. 2015; 29 (5): 412-418. Samanta, D., Guo, H., Rubinstein, R., Ramagopal, U. A. & Almo, S. C. Structural, mutational and biophysical studies reveal a canonical mode of molecular recognition between immune receptor TIGIT and nectin-2. Molecular immunology. 2017; 81: 151-159. Zhu, Z. & Dong, W. Overexpression of HHLA2, a member of the B7 family, is associated with worse survival in human colorectal carcinoma. Onco Targets Ther. 2018; 11: 1563-1570. Cheng, H., Borczuk, A., Janakiram, M., Ren, X., Lin, J., Assal, A. et al. Wide Expression and Significance of Alternative Immune Checkpoint Molecules, B7x and HHLA2, in PD-L1-Negative Human Lung Cancers. Clinical cancer research : an official journal of the American Association for Cancer Research. 2018; 24 (8): 1954-1964. Additional Declarations No competing interests reported. Supplementary Files RawdataFigure1D.xlsx RawdataFigure1A.xlsx RawdataLUSCclinicalraw.xlsx RawdataLUADclinicalraw.xlsx RawdataFigure8A.xlsx 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 Our Team In Review Editorial Policies Advisory Board 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-1239170","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":75122685,"identity":"cede749e-1625-4a44-aa75-c0cbda42378d","order_by":0,"name":"Jingtao Zhang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingtao","middleName":"","lastName":"Zhang","suffix":""},{"id":75122686,"identity":"05d168fc-c01b-46a9-9a51-9e1290a8ebde","order_by":1,"name":"Minghao Guo","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minghao","middleName":"","lastName":"Guo","suffix":""},{"id":75122687,"identity":"7961979c-acc9-42b7-9149-e1cdcfc6c7c7","order_by":2,"name":"Jing Zhang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":75122688,"identity":"0f1f98e6-41ec-4f83-b59c-2dc0d0738796","order_by":3,"name":"Guangming Zhang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Zhang","suffix":""},{"id":75122689,"identity":"9bea82c0-c544-43b8-aa8b-ae71c7bbaabe","order_by":4,"name":"Ning Sun","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Sun","suffix":""},{"id":75122690,"identity":"fae5aaaa-fe5a-4632-a4b4-46934f4413f5","order_by":5,"name":"Wenqiang Cui","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenqiang","middleName":"","lastName":"Cui","suffix":""},{"id":75122691,"identity":"b446dc9e-fbe9-4c51-8ac8-72b78549acbd","order_by":6,"name":"Fei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3RMUvEMBTA8YRCbnnYNcdJ+xUihROpnqNfoyFgNxEE6dhSeLf4EfwQWd0SAroczge3XHF18BbBRc3dqj063pD/+MgP3iOEhEIHGBs1tSmqc2Bp16wrEu2mfB85AteY9eI6iYlqxWIISfhVazt02bgukQ8ijFO/GIukNhYri3l6SiK7AjK76SWT7S3ApLYNLi2WJ081UzkQdddLjq0nHKR2FFcbdFQbmE6AGFn3LiY9EVzqZ4q3Ft2lNvHnAFKIbPxAkXjijwK2n8B2MVMkMactN6+l0o5lZ49C9ZJ0Pn/bfH3/AOOj7sPc5xf6pe2W79Wsl/zT7mvE8PehUCgU+tsvrWNi8elfX78AAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2022-01-07 16:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1239170/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1239170/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17635654,"identity":"88d8a5dc-0a1b-43a6-9966-f3519e8093d9","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1156684,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA and protein expression of FHL1 in LUAD and LUSC compared with normal tissues. \u003cstrong\u003e(A)\u003c/strong\u003e The Venn diagram shows the intersection of differentially expressed genes (DEGs) based on GSE63459 and GSE118370 and GSE27262 datasets. \u003cstrong\u003e(B)\u003c/strong\u003e The expression of FHL1 from a Pan-cancer perspective.\u003cstrong\u003e (C-D) \u003c/strong\u003eThe mRNA expression levels of FHL1 are based on TCGA and GEO databases. \u003cstrong\u003e(E-F)\u003c/strong\u003e The protein expression status of FHL1 is based on CPTAC and HPA databases, respectively. (ns, no significance, \u003csup\u003e*\u003c/sup\u003eP \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003eP \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003eP \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/9f337581204c2358c4d77d7b.png"},{"id":17635652,"identity":"17f3bf82-7254-49c7-ae24-df688f88e5ea","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184652,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between FHL1 mRNA levels and clinicopathological characteristics. \u003cstrong\u003e(A)\u003c/strong\u003e FHL1 expression was significantly related to age, gender, and smoker status, while there was no significant relationship with TNM stage, pathologic stage, and anatomic neoplasm subdivision. \u003cstrong\u003e(B)\u003c/strong\u003e No statistical correlation was found between the FHL1 expression levels and the nine clinical-pathological characteristics.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/a7432bfcbb4bd0099077543d.png"},{"id":17636215,"identity":"20391e50-afd2-4870-9b5d-6838c78755d0","added_by":"auto","created_at":"2022-01-25 15:30:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169019,"visible":true,"origin":"","legend":"\u003cp\u003eThe predictive and diagnostic value of FHL1. \u003cstrong\u003e(A)\u003c/strong\u003e FHL1 showed high sensitivity and specificity with respect to diagnostic ability in LUAD. Patients with high FHL1 expression had poor OS and DSS in LUAD. \u003cstrong\u003e(B)\u003c/strong\u003e FHL1 showed high sensitivity and specificity with respect to diagnostic ability in LUSC. Patients with high or low FHL1 expression had no significant values in OS and DSS for LUSC.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/11687068b2febec72e29345a.png"},{"id":17635659,"identity":"48cf08cf-d775-4af5-bd5b-96b213131635","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1595779,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network, functional enrichment, and co-expression analysis of FHL1. \u003cstrong\u003e(A-B)\u003c/strong\u003e GGI and PPI of FHL1 using GeneMANIA and STRING. \u003cstrong\u003e(C) \u003c/strong\u003eThe functional enrichment of FHL1. \u003cstrong\u003e(D)\u003c/strong\u003e The expression correlation between FHL1 and related protein based on STRING.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/32143e766cbd0be861a19d89.png"},{"id":17635660,"identity":"f3afeaee-a926-43c1-950c-3b49c2426a1e","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":832269,"visible":true,"origin":"","legend":"\u003cp\u003eIdentifying DEGs of FHL1 and the functional enrichment of these genes. \u003cstrong\u003e(A)\u003c/strong\u003e Volcano plot of differential gene profiles between FHL1 high expression and low expression. \u003cstrong\u003e(B)\u003c/strong\u003e Heatmap of the top 20 DEGs between FHL1 high expression and FHL1 low expression.\u003cstrong\u003e (C) \u003c/strong\u003eMetascape analysis of GO functional categories enriched in the FHL1-related genes. \u003cstrong\u003e(D)\u003c/strong\u003e The KEGG pathway analysis is displayed as a network analyzed by Metascape.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/e4b90ce5f0dc76931223c529.png"},{"id":17635663,"identity":"5b01b8de-d25d-4464-a750-a850ea841246","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":435765,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation analysis of FHL1 and TIILs. \u003cstrong\u003e(A)\u003c/strong\u003e The violin plots show the FHL1 expression status in the immune cells, stromal cells, other cells, and malignant melanoma cells. \u003cstrong\u003e(B)\u003c/strong\u003e Association of FHL1 expression level with six types of immune infiltrations and tumor purity. \u003cstrong\u003e(C)\u003c/strong\u003e The box plot shows the differential immune infiltration between FHL1 low- and high-groups.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/c56657e794cddfbafeb0b70c.png"},{"id":17635664,"identity":"5b47f2d4-d4f3-4734-b57c-364f6f9cb417","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3311494,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of FHL1 levels and lymphocytes infiltration levels from TISIDB database. \u003cstrong\u003e(A)\u003c/strong\u003e Relations between expression of FHL1 and 28 types of TILs across human heterogeneous cancers. \u003cstrong\u003e(B)\u003c/strong\u003e The infiltration degree of 26 types of TILs with FHL1 expression.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/901dc2a06bd1f38dbd3892e4.png"},{"id":17636220,"identity":"3d50ca6b-de8a-4f61-ae15-a100fd4838f6","added_by":"auto","created_at":"2022-01-25 15:30:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2503622,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between FHL1 expression and immunomodulators and chemokines based on the TISIDB database. \u003cstrong\u003e(A)\u003c/strong\u003e Correlations between the abundance of immune checkpoints and FHL1 and FHL1 expression levels. \u003cstrong\u003e(B)\u003c/strong\u003e Correlations between chemokines and FHL1 expression levels.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/c0a12bd70550a385ee8fd9fb.png"},{"id":17637182,"identity":"f65f1f1c-ea70-443f-93d6-db3f5fd96df7","added_by":"auto","created_at":"2022-01-25 15:33:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":99972,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic summary of the immune checkpoints in LUAD.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/c51fb841cee9575b73bc5715.png"},{"id":17635662,"identity":"1923577b-e2a2-40ad-9aca-d7756edff485","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":356397,"visible":true,"origin":"","legend":"\u003cp\u003eFHL1 couples with immune checkpoints in LUAD. \u003cstrong\u003e(A) \u003c/strong\u003eThe landscape of FHL1 and immune checkpoint alteration in LUAD. \u003cstrong\u003e(B)\u003c/strong\u003e Mutual-exclusivity analysis between FHL1 and multiple-immune checkpoints in LUAD. q-value \u0026lt; 0.05 was considered to be statistically significant.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/d2b94e5ef4d08476703fec94.png"},{"id":17637183,"identity":"67b79071-c387-4c62-8a2b-991c116c9c79","added_by":"auto","created_at":"2022-01-25 15:33:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3405116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/f32180b4-a759-4406-9de0-464f9917bc75.pdf"},{"id":17635653,"identity":"23c26794-082a-46d1-9b04-5c1a0ac93bf7","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11758,"visible":true,"origin":"","legend":"","description":"","filename":"RawdataFigure1D.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/33a8bb72c7abfe9098127113.xlsx"},{"id":17636217,"identity":"05d7266a-4aea-4080-a54b-cccdf9e11984","added_by":"auto","created_at":"2022-01-25 15:30:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44875,"visible":true,"origin":"","legend":"","description":"","filename":"RawdataFigure1A.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/ead1121178677be6c3e60cf7.xlsx"},{"id":17636218,"identity":"114c2d30-0e3b-47d4-8ad5-5f84bcd14555","added_by":"auto","created_at":"2022-01-25 15:30:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":390865,"visible":true,"origin":"","legend":"","description":"","filename":"RawdataLUSCclinicalraw.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/9776789ffb95d1b0c091d59a.xlsx"},{"id":17636219,"identity":"5f9e9687-2033-4463-ab48-2d9a72349c75","added_by":"auto","created_at":"2022-01-25 15:30:27","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":423492,"visible":true,"origin":"","legend":"","description":"","filename":"RawdataLUADclinicalraw.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/42b85cd844912ebbc437146d.xlsx"},{"id":17635666,"identity":"5fd46526-54d8-494e-8173-11d6383366cc","added_by":"auto","created_at":"2022-01-25 15:27:27","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7703444,"visible":true,"origin":"","legend":"","description":"","filename":"RawdataFigure8A.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1239170/v1/5a0a679a6e139b75415879d7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Four and a half LIM domain protein 1 as a Novel Prognostic Biomarker and Correlation with Immune Infiltration Levels in Lung Adenocarcinoma","fulltext":[{"header":"Background ","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers[1], with\u0026nbsp;an\u0026nbsp;annual increase\u0026nbsp;in incidence and mortality globally. LUAD\u0026nbsp;has surpassed LUSC to become the most common subtype of NSCLC. Unfortunately, it has been reported that the average 5-year survival rate is only 15%[2,3]\u0026nbsp;due to the high rates of invasion and metastasis. The immune system serves as a key player in identifying and removing tumor cells, and immunotherapies have demonstrated a remarkable and durable response in NSCLC[4]. However, only a small subset of patients benefits from it in clinical practice due to the lack of valid biomarkers. Therefore, it is critical to identify specific markers and immunotherapy targets to improve the survival rate of LUAD.\u003c/p\u003e\n\u003cp\u003eFHL1 is a member of the FHL protein family, characterized by four complete LIM domains and an N-terminal half LIM domain[5].\u0026nbsp;The FHL1 protein is mainly expressed in skeletal\u0026nbsp;muscle and regulates\u0026nbsp;skeletal growth[6]. Recent studies reported that FHL1 plays a crucial role in inhibiting cancer cell growth, migration, and invasion, and it is significantly downregulated in lung cancer, breast cancer, liver cancer, gastric cancer, kidney cancer, and prostate cancer[7-10]. However, the role of FHL1 in tumor progression is complex,\u0026nbsp;and previous studies have validated that FHL1\u0026nbsp;regulates angiogenesis\u0026nbsp;and\u0026nbsp;the TGF-\u0026beta;-like signaling pathway and induces G1 and G2/M cell cycle arrest[8,9,11]. Therefore, FHL1 is a potential prognostic biomarker for LUAD.\u003c/p\u003e\n\u003cp\u003eHowever, the mechanism underlying the association between FHL1 and immune infiltrates is unclear. Therefore, our study aimed to investigate the prognostic value of FHL1 and the association between FHL1 and TME based on bioinformatics techniques and multiple public databases. Furthermore, our results may present novel diagnostic and therapeutic targets and immunotherapeutic strategies for LUAD.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIdentification of NSCLC-related and LUAD-related differentially expressed genes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GSE118370, GSE63459, and GSE27262 datasets were acquired from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database, which includes NSCLC tissues and adjacent normal tissues. The raw data were processed and standardized with GEO2R, | log\u003csub\u003e2\u003c/sub\u003e(fold-change) | \u0026gt;1, and adjusted P-value \u0026lt; 0.05, which were considered as DEGs. The common intersection of three dataset DEGs was selected using the Venn diagram. To obtain LUAD-related DEGs, RNA sequencing data were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). The data were analyzed with threshold | log (fold change) | \u0026gt; 1.5 and adjusted P-value \u0026lt; 0.05, and visualization by conducted R packages of \u0026ldquo;ggplot2.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValidation of FHL1 expression level in LUAD and LUSC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene expression datasets of FHL1 in LUAD, LUSC, and normal tissues were derived from the TCGA and GEO databases. Furthermore, the FHL1 protein expression level and distribution and localization between tumor and normal tissues were shown by immunohistochemistry pictures based on CPTAC databases and the HPA (https://www.proteinatlas.org/), respectively. In addition, the corresponding clinical characteristics of FHL1 expression, such as age, sex, smoking status, TNM stage, and tumor location, were also collected from TCGA. Furthermore, receiver operating characteristic (ROC) and Kaplan\u0026ndash;Meier (K-M) analyses were used to evaluate FHL1 expression levels in the diagnosis and prognosis of LUAD and LUSC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFHL1-related Interaction Networks and Functional Enrichment Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene-gene interaction (GGI) and protein-protein interaction (PPI) networks were used to identify potential interaction genes and proteins of FHL1 based on GeneMANIA (http://genemania.org/) and STRING (https://string-db.org/). Correlation analysis further validated the relationship between these proteins and FHL1. Functional enrichment analysis was conducted with the \u0026ldquo;ClusterProfiler\u0026rdquo; package and further visualized using the \u0026ldquo;ggplot2\u0026rdquo; package.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Metascape database was used to investigate the functional enrichment analysis of FHL1-related DEGs in the LUAD dataset and the GO and KEGG analysis visualization by applying the \u0026ldquo;ggplot2\u0026rdquo; package in R.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRelationship between FHL1 expression and immunity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTISCH (http://tisch.comp-genomics.org) was used to assess the abundance of FHL1 expression in different components of the TME, including immune cells, malignant cells, others, and stromal cells. In addition, correlation analysis was performed to investigate the abundance of TIICs with FHL1 expression in LUAD based on the TIMER (https://cistrome.shinyapps.io/timer/) and TISIDB (http://cis.hku.hk/TISIDB/) databases. Furthermore, TISIDB was utilized to investigate the relationship between FHL1 expression and chemokines and immune checkpoints and the prognostic value of the representative immune checkpoints in LUAD. cBioPortal (http://cbioportal.org) was used to evaluate the frequency of genetic alterations in FHL1 and immune checkpoints in LUAD samples, as well as the tendency of co-occurrence and mutual exclusivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using R software v4.0.5\u0026nbsp;and corresponding packages. Paired \u003cem\u003et\u003c/em\u003e-tests and Mann-Whitney U tests were performed to examine the differences between tumor samples and normal samples. Kaplan\u0026ndash;Meier (K-M) analyses and log-rank tests were used to evaluate the prognostic value of FHL1. ROC curves were constructed to investigate the FHL1 and its role in sensitivity and specificity of diagnosis. Spearman\u0026rsquo;s correlation analysis was used to assess the correlation between FHL1 and immune infiltration cells, immune checkpoints, and chemokines. Differences were considered statistically significant at P \u0026lt; 0.05, except for the genetic alteration analysis. Q-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIdentification of NSCLC-related DEGs in LUAD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intersection analysis showed that 76 DEGs overlapped among\u0026nbsp;the GSE118370, GSE63459, and GSE27262 datasets (Figure 1A). The top ten targets were \u003cem\u003eVIPR1, ADARB1, PECAM1, CLDN18, NOTCH4, FHL1, TIMP3, TCF21, MACF1,\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e,\u0026nbsp;considered key genes in NSCLC. Notably, FHL1 has been considered as\u0026nbsp;a tumor suppressor\u0026nbsp;gene that exerts an inhibitory effect through various mechanisms underlying cancer growth, invasion, and metastasis[9,12-14]. A recent study found that FHL1 can also play a promoting role in tumors[15]. Therefore, our study aimed to identify the function of FHL1 in NSCLC progression using bioinformatics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDownregulated expression of FHL1 in LUAD and LUSC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptomic data were analyzed to systematically investigate the mRNA expression levels of FHL1 across diverse cancers based on TCGA and GTEx databases (Figure 1B). The results showed that the FHL1 expression level was significantly lower in 17 types of tumors than that in adjacent normal tissues, especially in LUAD and LUSC. GEPIA and GEO databases were used further to validate the findings of TCGA and GTEx web and displayed a low expression of FHL1 in LUAD and LUSC tissues compared with that of normal samples (Figure 1C-D). In addition, UALCAN was performed to examine the protein expression level of FHL1 based on the CPTAC database (Figure 1E),\u0026nbsp;and the\u0026nbsp;results showed that the FHL1 protein was remarkably decreased in tumor tissues\u0026nbsp;compared\u0026nbsp;to that of normal samples. Immunohistochemistry images from the HPA database showed results similar to those\u0026nbsp;of CPTAC (Figure 1F). These findings suggest that both mRNA and protein expression levels of FHL1 are downregulated in LUAD and LUSC compared to those of normal tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation Between FHL1 and Clinical Characteristics in NSCLC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo clarify the association between the transcription of FHL1 and clinicopathological parameters in LUAD and LUSC patients, the information was analyzed using Wilcoxon and logistic regression analysis (Figure 2). The results of multiple subgroup analysis showed that FHL1 expression was significantly associated with age, sex, and smoking status and was downregulated in younger (age \u0026lt; 65\u0026nbsp;years),\u0026nbsp;male, and smoking patients (p \u0026lt; 0.05, respectively). However, FHL1 expression\u0026nbsp;was not correlated with other clinicopathological parameters, such as pathologic stage, TNM stage, and anatomic neoplasm subdivision (Figure 2A). Moreover, no statistical correlation was found between FHL1 expression and clinicopathological characteristics in LUSC, including age, sex, smoking status, TNM stage, pathologic stage, and anatomic neoplasm subdivision (Figure 2B)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiagnosis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eand Prognosis value of FHL1 in LUAD and LUSC\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed to identify the role of FHL1 in distinguishing LUAD and LUSC samples from normal samples. As shown in Figure 3A and B, the area under the curve (AUC) of FHL1 was 0.993 (95% CI, 0.989\u0026ndash;0.998) in LUAD and 0.998 (95% CI: 0.995\u0026ndash;1.000) in LUSC, indicating that FHL1 may be a strong identification biomarker for LUAD and LUSC. The curves illustrate the association between FHL1 expression and overall survival (OS) and disease-specific survival (DSS), which helps\u0026nbsp;to investigate the prognostic value of FHL1 in LUAD and LUSC based on K-M analysis. Figure 3A shows that LUAD patients with low expression of FHL1 were associated with shorter OS (P = 0.025) and poor DSS (P = 0.054). However, the levels of FHL1 were not significantly correlated with OS and DSS in LUSC patients (P = 0.475, P = 0.533, respectively) (Figure 3B). These findings suggest that low expression of FHL1 could be a promising biomarker to diagnose LUAD and LUSC, as well as the poor prognosis of LUAD patients.\u0026nbsp;Because FHL1 expression is not associated with the prognosis and clinical characteristics of LUSC, the following study focused on the role of FHL1 expression in LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePPI Networks and Functional Annotations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore FHL1-correlated genes and FHL1-binding proteins, GGI and PPI were generated using GeneMANIA and STRING databases (Figure 4A and B). The correlation analysis suggested that the proteins (AKT1, IGFBP5, INPP5A, KCNA5, RBPJ, STAT3, STAT5A, STAT5B,\u0026nbsp;and TTN) in the PPI network\u0026nbsp;had a significant relationship with FHL1 expression, except\u0026nbsp;for RING1 (Figure 4D). Figure 4C shows that FHL1 is associated with the biological functions of DNA-binding transcriptional activator activity, RNA polymerase l-specific, and RNA polymerase II repressing transcription factor binding and participates in the JAK-STAT signaling pathway, interleukin 15 mediated signaling pathway, and response to interleukin-9.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Additionally, volcano plots and heatmaps were generated to identify differentially expressed genes (DEGs, | log (fold change) | \u0026gt; 1.5, and adjusted P-value \u0026lt; 0.05), based on the TCGA database (Figure 5A\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand B). Metascape was used to explore the role of FHL1-related DEGs in LUAD patients (Figure 5C\u003cstrong\u003e-\u003c/strong\u003eF). The results showed that the FHL1-related DEGs were involved in ERKl and ERK2 cascades, cell differentiation, and blood circulation. Biological process analysis further revealed that FHL1-related DEGs of LUAD participated in humoral immune response, vascular processes in\u0026nbsp;the circulatory system,\u0026nbsp;and neutrophil-mediated cytotoxicity. Moreover, KEGG\u0026nbsp;analysis showed that these genes were closely related to the\u0026nbsp;metabolism of xenobiotics by cytochrome P450. A recent study confirmed\u0026nbsp;that cytochrome P450 is a crucial mediator of ferroptosis[16], considered a novel therapeutic strategy to treat NSCLC[17]. Altogether, FHL1 is strongly linked to the\u0026nbsp;immune response. Therefore, the correlation between FHL1 and\u0026nbsp;the anti-cancer immune response was\u0026nbsp;investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation of FHL1 expression with infiltrating immune cells\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the expression status of FHL1 in different cell\u0026nbsp;types, the violin plots showed that FHL1 expression was the most frequent in immune cells, while only parts of stromal cells, malignant cells, and other cells\u0026nbsp;were expressed (Figure 6A). Furthermore, establishing scatterplots to evaluate the correlation of FHL1 expression with the TIICs (Figure 6B)\u0026nbsp;illustrates\u0026nbsp;that FHL1 expression\u0026nbsp;was positively associated with the level of B cells (r = 0.157, P = 5.21e-04), CD8\u003csup\u003e+\u003c/sup\u003eT\u0026nbsp;cells (r = 0.207, P = 4.05e-06), CD4\u003csup\u003e+\u003c/sup\u003eT\u0026nbsp;cells (r = 0.279, P = 4.41e-10), macrophages (r = 0.433, P = 1.45e-23), neutrophils (r = 0.275, P = 7.44e-10), and dendritic\u0026nbsp;cells (r = 0.31, P = 2.40e-12), while being negatively related to tumor purity (r =\u0026nbsp;\u0026minus;0.313, P = 1.03e-12). The enrichment score boxplot further validated that all six types of immune cells had a higher degree of immune infiltration in the high FHL1 expression group than that\u0026nbsp;in the low FHL1 expression group (Figure 6C). Further, the correlation analysis based on the TISIDB database was used to confirm\u0026nbsp;that the level of FHL1\u0026nbsp;was clearly correlated with TILs in diverse cancers (Figure 7A). As shown in\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFigure 7B, 26 TILs were closely related to FHL1 expression in LUAD. Specifically, FHL1 was significantly positively associated with 24 types of TILs\u0026nbsp;but negatively associated with active CD4+T cells and CD56 dim cells in LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation of FHL1 expression with immune molecules\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTILs are important constituents of the tumor immune microenvironment and play a crucial role in antitumor efficacy and prognostic ability[18,19]. Immune checkpoints inhibit the anti-tumor immune response of TILs, contributing to tumor cell immune escape. To identify whether FHL1 impacts TIL infiltration via immune checkpoints, a correlation analysis between FHL1 expression and 47 immune checkpoint genes was performed (Figure 8A). The results showed that FHL1 was associated with most immune checkpoint genes, including\u0026nbsp;CD274, CD48, CD80, VTCN, and PVR.\u003c/p\u003e\n\u003cp\u003eThen, correlation analysis was performed to explore the relationship between FHL1 expression and chemokines (Figure 8B). The results showed that FHL1 expression levels were markedly associated with CCL5 (r = 0.097, P = 0.0267), CCL17 (r = -0.138, P = 0.00166), CCL20 (r = -0.172, P = 8.31e\u0026minus;05), and CXCL8 (r = -0.104, P = 0.018). These results revealed that FHL1 participated widely in regulating immune molecules, thereby affecting immune\u0026nbsp;cell infiltration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelation of the genomic alteration between FHL1 and immune checkpoint\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther, investigation\u0026nbsp;of the prognosis of immune\u0026nbsp;checkpoints based on TISIDB showed that PD-L1, PD-L2, CD80, CD86, VSIR, PVR, LGALS9,\u0026nbsp;and CD48 were downregulated, while VTCIN, CD112, TNFSF4, CD70, and TNFSF18 were upregulated in LUAD (Figure 9). Notably, not all the different expressions\u0026nbsp;of immune checkpoints have a prognostic role in LUAD. Briefly, low expression of CD80 and CD48 and high expression of VTCN1 indicated high OS and/or DFS, but downregulated PVR and was significantly related to poor OS and DFS (P = 0.012, 0.034 respectively).\u003c/p\u003e\n\u003cp\u003eMoreover, mutation analysis revealed that FHL1 was altered in 2.3% of all study subjects, including missense mutations, splice mutations, truncating mutations, structural variants, amplifications, and deep deletions (Figure 10A). Figure 10B shows the correlation between FHL1 and immune checkpoints. In addition, alterations in PVR, NECTIN2, and HHLA2 have a co-occurrence tendency with FHL1 alterations. These results indicate that FHL1 may participate in regulating immune checkpoints in LUAD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough surgical and targeted therapies have significantly improved, the mortality of LUAD continues to be high in the past decades, which seriously threatens human health. Therefore, as a novel cancer treatment, immunotherapy has attracted wide attention. Recently, an increasing number of studies have confirmed that immunotherapy is a successful option for LUAD patients[20,21]. Previous studies have validated that FHL1 is downregulated in LUAD, and it is closely related to tumor invasion and metastasis[7,9]. However, the correlation between FHL1 expression and immune infiltration has not been thoroughly investigated in LUAD. In this study, we analyzed the prognostic value of FHL1 and the relationship between FHL1 expression and immune cell infiltration based on bioinformatic analysis.\u003c/p\u003e\n\u003cp\u003eThis study revealed that FHL1\u0026nbsp;expression was\u0026nbsp;significantly lower in LUAD tissues than that in normal tissues, and the expression level of FHL1 was affected by age, sex, and smoking status. Currently, the role of FHL1 in LUAD has not been thoroughly investigated. Previous trials\u0026nbsp;suggested that FHL1\u0026nbsp;participates in cancer cell growth, migration, and invasion, which caused a worse prognosis[9,22]. K-M and ROC analyses were conducted to confirm the diagnostic and prognostic value of FHL1 in clinical\u0026nbsp;settings. The results of the K-M analysis showed that low expression of FHL1 indicated poor OS and short DSS, consistent with the results of\u0026nbsp;previous studies[9,22]. ROC analysis showed that FHL1 had a substantial AUC value, which played an important role in the diagnosis of LUAD. Therefore, this study suggests that FHL1 is a powerful diagnostic and prognostic biomarker. To further investigate the functions of FHL1 in LUAD, GO and KEGG analyses were performed to reveal that FHL1 regulates cell growth via DNA-binding transcription activator activity, RNA polymerase l-specific, and RNA polymerase II repressing transcription factor binding, as well as regulates human immunity through interleukin 15 mediated signaling pathway, and response to interleukin-9. Moreover, the FHL1-related DEGs of LUAD were co-regulated in humoral immune response and neutrophil-mediated cytotoxicity. These results show that FHL1 expression may play a crucial role in tumor growth and immune cell infiltration. However, the underlying mechanism by\u0026nbsp;which FHL1 expression\u0026nbsp;affects immune cells in the TME has not\u0026nbsp;yet been reported. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTIICs play a critical role in tumor progression through complex intercellular interaction networks and are associated with clinical prognosis. TISCH analysis results showed that FHL1 expression was more abundant in immune cells than that in stromal cells, malignant cells, and other cells in\u0026nbsp;the TME. Regarding TIMER databases, FHL1 downregulation might\u0026nbsp;be closely correlated with the low degree of TIL infiltration, especially for B cells, CD8\u003csup\u003e+\u003c/sup\u003eT cells, CD4\u003csup\u003e+\u003c/sup\u003eT cells, macrophages, neutrophils, and DCs.\u0026nbsp;Co-expression analyses based on TISIDB\u0026nbsp;further validated the results of the TIMER database, revealing that FHL1 expression was positively correlated with CD8\u003csup\u003e+\u003c/sup\u003eT cells, NK cells, and DCs. High CD8\u003csup\u003e+\u003c/sup\u003eT cell infiltration levels were considered to be associated with excellent prognosis and more prolonged survival because CD8\u003csup\u003e+\u003c/sup\u003eT cells play\u0026nbsp;an important role in killing tumor cells[23]. Previous research\u0026nbsp;has confirmed that NK cells\u0026nbsp;release perforin and granzymes and excrete various cytokines to exert an antitumor effect[24]. DCs, as\u0026nbsp;professional antigen-presenting cells,\u0026nbsp;participate in T cell polarization and Th1 differentiation[25]. Altogether, FHL1 expression affects the level of TIL infiltration, which promotes the construction\u0026nbsp;of an immunosuppressive environment.\u003c/p\u003e\n\u003cp\u003eMoreover, to investigate the influence of FHL1 in\u0026nbsp;the TME, including immune molecules, the TISIDB database was used to analyze the correlation between FHL1 expression and representative chemokines (such as CCL5, CCL17, CCL20, and CXCL8) and immune inhibitors (such as CD80, CD48, VTCN, and PVR). Notably, CCL20\u0026nbsp;has been confirmed to enhance the migration and proliferation of A549 cells and\u0026nbsp;recruit TAM cells[26].\u0026nbsp;Liu, \u003cem\u003eet al\u003c/em\u003e [27] found that CXCL8 was overexpressed in LUAD and acted as a poor prognostic factor promoting tumor progression, consistent with our results.\u0026nbsp;Intriguingly, beyond\u0026nbsp;the classic immune checkpoints PD-L1 and CTLA-4, FHL1 expression was significantly associated with CD80, CD48, and VTCN1,\u0026nbsp;closely related to patient outcomes. CD80 acts as a surface ligand on immune cells, and CTLA-4 negatively regulates T-cell activation[28]. CD48 is a member of the signaling lymphocyte activation molecule family and participates in the adhesion and activation of immune cells[29]. VTCN1 belongs to the B7 family protein,\u0026nbsp;is upregulated in LUAD, and negatively regulates T‐cell immunity by restraining T‐cell proliferation, cytokine secretion, and the development of cytotoxicity[30]. Therefore, FHL1 expression affects the function of tumor-antagonizing immune cells, weakening the effects of immune surveillance\u0026nbsp;and contributing to immune escape and tumor progression.\u003c/p\u003e\n\u003cp\u003eInterestingly, mutation analysis further revealed\u0026nbsp;that PVR and NECTIN2 and HHLA2\u0026nbsp;alterations co-occurred with\u0026nbsp;the FHL1 mutation.\u0026nbsp;PVR and NECTIN2 inhibit\u0026nbsp;the activation of T and NK cells by\u0026nbsp;interacting with T cell immunoglobulin and ITIM domain (TIGIT)[31]. Furthermore, HHLA2 expression is closely correlated with CD8 T-cell infiltration status[32]. A recent study reported that HHLA2 was widely expressed in patients with PD-1-negative NSCLC, which indicated that HHLA2 might be a potential target for patients who do not respond to PD-1 pathway blockade[33]. These findings partly demonstrate the mechanism by\u0026nbsp;which FHL1\u0026nbsp;regulates the expression of immune checkpoints in LUAD.\u0026nbsp;Therefore, FHL1 can function as a novel target to investigate\u0026nbsp;the immunosuppressive status\u0026nbsp;of LUAD.\u003c/p\u003e\n\u003cp\u003eAltogether, this study reported that FHL1 regulates anti-tumor immune responses\u0026nbsp;through various mechanisms, including recruitment of different immune\u0026nbsp;cells in\u0026nbsp;the TME, thereby\u0026nbsp;affecting the expression of chemokines and immune inhibitors, and the co-occurrence mutation of some immune checkpoints. These results suggest that FHL1 may impact patient outcomes by modulating the immunosuppressive microenvironment and could be a promising immunotherapeutic target for LUAD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite comprehensive and systematic evaluation\u0026nbsp;of the role of FHL1 in LUAD, based on different public databases, there are several limitations. First, the row data were collected from public repositories,\u0026nbsp;and the quality standards were nonuniform, which might\u0026nbsp;have influenced the study outcomes. Second, the perspective of transcriptome and genome levels could not reflect overall aspects of immune status, and there is a lack of direct evidence to demonstrate the role of FHL1 expression\u0026nbsp;in regulating the immune response impacting patient\u0026nbsp;prognosis. Third, more \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments are needed to further validate the mechanism of FHL1 expression in LUAD. In the follow-up research, we will further detect the underlying function of FHL1 in LUAD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study demonstrated that FHL1 was expressed at low levels in LUAD and revealed a correlation between FHL1 expression and tumor progression. Furthermore, FHL1 expression was strongly diagnosed as a prognostic factor for LUAD. In addition, FHL1 expression was related to immune cell infiltration, immunoinhibition, and chemokines, indicating that FHL1 is a better immunotherapy target. Therefore, FHL1 is considered a potential prognostic biomarker and immunotherapy target for LUAD.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFHL1, four and a half LIM domain protein 1; TCGA: The Cancer Genome Atlas; TIICs: tumor-infiltration immune cells; LUAD, lung adenocarcinoma; LUSC: lung squamous cell carcinoma; DEGs: differentially expression genes; NSCLC: non-small cell lung cancer; BP: Biological processes; MF: Molecular function; CC: Cellular component; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; OS: Overall survival; ROC: Receiver operating characteristic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJingtao Zhang wrote the original draft, Fei Xu and Minghao Guo prepared the figures and tables, Jing Zhang analyzed the data, Guangming Zhang downloaded the raw data from\u0026nbsp;TCGA and GEO\u0026nbsp;databases, Ning Sun reviewed the relevant literature, Wenqiang Cui proofread the manuscript, and Fei Xu edited the draft and made revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (grant No. 82004281 and 82001190), the China Postdoctoral Science Foundation (grant No. 2021T140427 and 2021M691986),\u0026nbsp;and the Development Plan of Shandong Medical and Health Technology (grant No. 2019WS581).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJemal, A., Siegel, R., Xu, J. \u0026amp; Ward, E. Cancer statistics, 2010. CA: a cancer journal for clinicians. 2010; 60 (5): 277-300.\u003c/li\u003e\n\u003cli\u003eSiegel, R. L., Miller, K. D. \u0026amp; Jemal, A. Cancer statistics, 2018. CA: a cancer journal for clinicians. 2018; 68 (1): 7-30.\u003c/li\u003e\n\u003cli\u003eChen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F.\u003cem\u003e et al.\u003c/em\u003e Cancer statistics in China, 2015. CA: a cancer journal for clinicians. 2016; 66 (2): 115-132.\u003c/li\u003e\n\u003cli\u003eLi, B., Cui, Y., Diehn, M. \u0026amp; Li, R. Development and Validation of an Individualized Immune Prognostic Signature in Early-Stage Nonsquamous Non-Small Cell Lung Cancer. JAMA Oncol. 2017; 3 (11): 1529-1537.\u003c/li\u003e\n\u003cli\u003eJohannessen, M., M\u0026oslash;ller, S., Hansen, T., Moens, U. \u0026amp; Van Ghelue, M. The multifunctional roles of the four-and-a-half-LIM only protein FHL2. 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Clinical cancer research : an official journal of the American Association for Cancer Research. 2018; 24 (8): 1954-1964.\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":"FHL1, LUAD, tumor-infiltrating immune cells, biomarker, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-1239170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1239170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e We aimed to investigate the prognostic value of Four and a half LIM domain protein 1 (FHL1) and its correlation with FHL1 and tumor-infiltration immune cells (TIICs) in lung adenocarcinoma (LUAD). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eFHL1 expression status and its influence on clinical characteristics in LUAD and Lung squamous cell carcinoma (LUSC) were collected based on GEPIA, TCGA, GEO, CPTAC, the HPA database, and the GTEx Portal. The ROC curve and Kaplan-Meier plots were used to assess the value of FHL1 expression levels in the diagnosis and prognosis of LUAD and LUSC. The interaction network revealed the related genes and proteins of FHL1 by GeneMANIA and STRING. The functional enrichment analysis based on FHL1 and FHL1-related differentially expression genes (DEGs) was conducted by the “clusterProfile” package and Metascape, respectively. The correlation analysis between FHL1 expression and tumor immunity was performed using TISIH, TIMER, and TISIDB. cBioPortal was used to investigate the mutation status between FHL1 and representative immune checkpoints. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe results showed that FHL1 expression was significantly lower in tumors relative to adjacent standard samples, and downregulated FHL1 predicted a worse prognosis for LUAD than that for LUSC. Additionally, FHL1 participated in the interleukin 15 mediated signaling pathway, response to interleukin-9, and neutrophil-mediated cytotoxicity. It was also positively correlated with TIICs (B cells, CD8\u003csup\u003e+\u003c/sup\u003eT, CD4\u003csup\u003e+\u003c/sup\u003eT cells, macrophages, neutrophils, and DC), immune checkpoints (CD80, CD48, VTCN, and PVR), and chemokines (CCL5, CCL17, CCL20 and CXCL8). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e FHL1 is a powerful prognostic biomarker of immune infiltration.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Four and a half LIM domain protein 1 as a Novel Prognostic Biomarker and Correlation with Immune Infiltration Levels in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-25 15:27:25","doi":"10.21203/rs.3.rs-1239170/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":"f85a39b2-c335-412e-a0a3-98bf818c63fb","owner":[],"postedDate":"January 25th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-01-25T15:27:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-25 15:27:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1239170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1239170","identity":"rs-1239170","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}