Analysis of DHX32 as diagnostic gene and independent prognostic factor of lung squamous cell carcinoma

Analysis of DHX32 as diagnostic gene and independent prognostic factor of lung squamous cell carcinoma | 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 Article Analysis of DHX32 as diagnostic gene and independent prognostic factor of lung squamous cell carcinoma wei cao, Zhuang Zuo, Dacheng Jin, Haochi Li, Weirun Min, Jinlong Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4375543/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: The abnormal of DHX32 was related to the occurrence, development and clinicopathological features of cancer, but DHX32 has not been studied in Lung squamous carcinoma (LUSC). We aimed to investigate the expression , prognosis and biological significance of DHX32 in LUSC. Method: The LUSC-related datasets (GSE30219 and GSE8894) were included in the analysis. Firstly, the expression and diagnosis of DHX32 were analyzed. The differentially expressed genes (DEGs) of GSE30219 were screened. The Cox analysis was used to screen prognostic genes. Based on the risk scores of LUSC samples, the samples were divided into high-low risk groups. The risk model was constructed by cox analysis in GSE30219, and verified in GSE8894. Furthermore, nomogram, immune infiltration analysis and drug sensitivity analysis were constructed. Results: Compared with the control group, the expression of DHX32 in LUSC samples was obviously higher. The survival of high truncation value group was signally higher than that of low truncation value group. Based on 347 DEGs, the 3 prognostic genes ( CLDN11 , PID1 and MAMDC2 ) were screened. In GSE30219 and GSE8894, the risk model had high accuracy and excellent performance. In addition, the riskScore was independent prognostic risk factors for LUSC by cox analysis. Immune infiltration analysis and drug sensitivity analysis showed that memory B cells and five drug had a difference in two risk groups. Conclusion: DHX32 could be used as a diagnostic gene and independent prognostic factor for LUSC, providing the insight for the diagnosis and treatment of LUSC. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Lung squamous carcinoma DHX32 Prognostic factors Risk score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Lung cancer (LC) is one of the deadliest malignancies worldwide and can be divided into two main types: small-cell lung cancer and non-small-cell lung cancer. Non-small cell Lung cancer (NSCLC) accounts for about 85% of lung cancer cases and is mainly divided into lung adenocarcinoma and Lung squamous carcinoma (LUSC) according to pathogenesis and histological morphology [ 1 ] . LUSC accounts for approximately 30% of all lung cancer cases and, globally, causes approximately 400,000 deaths per year [ 2 ] . At present, patients with early non-small cell lung cancer often need surgical resection. For advanced patients who cannot be surgically resected, the best treatment is targeted therapy or immunotherapy combined with chemotherapy [ 3 ] . Compared with lung adenocarcinoma, LUSC has a poor clinical prognosis and lacks targeted drugs. Therefore, it is extremely important to look for potential biomarkers in LUSC and understand their impact on the prognosis of patients with LUSC. DEAH-box helicase 32 (DHX32) is a helicase in the DHX family [ 4 ] that is involved in many RNA-related biological processes, including ribosome biosynthesis, transcription, mRNA splicing, and translation. DHX32 is dysregulated in a variety of diseases, such as down-regulated DHX32 in acute lymphoblastic leukemia but up-regulated in colorectal cancer and breast cancer. In addition, the abnormal expression of DHX32 is closely related to the occurrence and development of cancer. The expression level of DHX32 is correlated with the clinicopathological features of cancer [ 5 ] . However, most studies on DHX32 focus on liver cancer, and there are few studies on LUSC, especially on diagnosis and prognosis. Therefore, this study mainly analyzed the expression, prognosis, and diagnosis of DHX32 in LUSC, as well as its relationship with immunotherapy and drug sensitivity, and constructed a prognostic model to develop anti-cancer treatment strategies based on DHX32 expression and function. 2. Materials and Methods 2.1 Data Extraction The transcriptome and clinical data of the Lung squamous carcinoma (LUSC)-related datasets (GSE30219 and GSE8894) were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/ ). The GSE30219 contained 60 lung tissue samples of LUSC and 14 control samples with survival information. The GSE8894 contained 75 lung tissue samples of LUSC with survival information. 2.2 Difference analysis The expression difference of the DHX32 gene between LUSC and control samples in GSE30219 was analyzed by R ‘rstatix’ software (version 0.7.0). The “limma” R package (version 3.50.1) was used to analyze the differentially expressed genes (DEGs) of GSE30219 (|Log2FC|>1, p.adj < 0.05) [ 6 ] . 2.3 Functional enrichment analysis Based on the median expression of the DHX32 gene and median risk scores, GSE30219 was classed into different expression/risk groups. The R-package ‘clusterProfiler’ (version 4.2.2) was used to perform gene set enrichment analysis (GSEA) on all genes in different groups (P < 0.05) [ 7 ] . 2.4 Immune Analysis The Willcoxon hypothesis test was performed for 22 kinds of immune cell infiltration abundance. The TIDE database ( http://tide.dfci.harvard.edu ) was used to forecast the TIDE value of LUSC patients. In addition, the SubMap algorithm was used to predict the sensitivity of PD-1 and CTLA-4 inhibitors [ 8 ] . 2.5 Construction of risk model The Cox analyses was used to screen prognostic genes by R-packet ‘survival’. The Kaplan-Meier (K-M) curve (survival package (version 0.4.9)) and receiver operating characteristic (ROC) (survival ROC (version 1.0.3)) curve were used to evaluate the model performance [ 9 ] . 2.6 Creation of the Nomogram Univariate and multivariate Cox regression analyses were used for independent prognostic analysis. A nomogram with independent prognostic factors was created to predict 1-/3-/5-year survival. In addition, the Calibration curve and decision curve verified the performance of the model [ 10 ] . 2.7 Drug sensitivity analyses GDSC ( https://www.Cancerrxgene.org ) was used to access the chemotherapy drugs of LUSC. The IC50 of the drug in each sample was calculated using the R-package ‘pRRophetic’ (version 0.5) [ 11 ] . Target genes of drugs with significant sensitivity differences were predicted through the DrugBank database ( https://go.drugbank.com/ ). 2.8 Statistical Analysis The R software processed and analyzed the data. Differences were analyzed via the Wilcoxon rank-sum test. The p < 0.05 represented a significant difference. 3. Results 3.1 DHX32 had decent prognostic and diagnostic value in LUSC As shown in Fig. 1A-1B, the expression trend of DHX32 was obviously higher in LUSC group campared to that in control group. The LUSC samples were divided into high and low truncation groups based on the optimal truncation value of DHX32 gene expression in the GSE30219. The survival rate of the high truncation value group was higher (Fig. 1C ) . The area under curve (AUC) of the curve was 0.7, indicating that DHX32 had prognostic diagnostic value for LUSC ( Fig. 1D ) . 3.2 The significant immune differences between DHX32 high-low expression groups GSE30219 was divided into high and low-expression groups according to the median expression of DHX32. Based on two expression groups, GSEA was performed to explore the biological function. The GSEA results showed that all gene was enriched to 39 pathways in total, such as “cell cycle” and “cell adhesion molecules cams”, etc (Fig. 2A) . As shown in Fig. 2B , the infiltration abundance of resting Mast cells and naive CD4 T cells were different between the two groups. Thereafter, DHX32 was weakly correlated with TIDE (cor = 0.117) (Fig. 2C) . Moreover, the expression of CTLA-4 and PD-1 were significantly different in the two expression groups, thereafter, the high-expression group had a significant response to PD1 (Fig. 2D-2E) . 3.3 Establishment and Validation of Risk Model Volcanic map and heat map showed 347 DEGs in two groups, including 101 upregulated genes and 246 down-regulated genes ( Fig. 3A-3B ) . Based on DEGs, prognostic genes were screened by Cox analysis, namely CLDN11 , PID1 , and MAMDC2 ( Fig. 3C-3D ) . The LUSC samples in GSE30219 were divided into high and low-risk groups ( Fig. 3E-3F ) . The sample survival of the high-risk group was lower ( Fig. 3G ) . The 1-/3-/5-year forecast AUC for the training set were 0.85, 0.83, and 0.82, indicating that the model had high prediction accuracy ( Fig. 3H ) . Moreover, we performed validation processing in GSE8894, and the results were consistent with GSE30219 ( Fig. 3I-4L ) . 3.4 Creation of the nomogram The differences in risk scores among subgroups of clinical factors age (> 60, 60 groups were significantly higher than that of the male sample and < = 60 groups, and there was no obvious difference in the risk score between pathological stage N and pathological stage T subgroups, indicating that the age and gender were highly correlated with the risk score (Fig. 4A-4D) . In univariate Cox analysis, riskScore (HR = 2.718) and age (HR = 1.039) could be used as risk factors for prognosis (Fig. 4E) . However, the riskScore (HR = 2.687) was an independent prognostic risk factor for LUSC by multivariate Cox analysis (Fig. 4F) . The nomogram containing the risk Score and age was created (Fig. 4G) . The calibration curves showed that the curves of 1-/ 3-/ 5-years were close to the theoretical value (Fig. 4H) . The result of the decision curve indicated that the model had good performance (Fig. 4I) . 3.5 Memory B cells was identified as differential immune cells Based on the two risk groups, there were 17 pathways were enriched, including “intestinal immune network for IgA production” and “systemic lupus erythematosus”, etc (Fig. 5A) . Because GSEA was enriched in immune-related signaling pathways, we then performed an immune infiltration analysis. The results showed that there were significant differences in memory B cells between the two risk groups (Fig. 5B) . 3.6 Drug sensitivity analysis In total, the IC50 of 5 anti-cancer drugs between the two risk groups were significantly different, namely Bosutinib, Docetaxel, Gefitinib, Erlotinib, and Lapatinib (Fig. 6A) . The 18 targeted genes were predicted based on 5 drugs with significantly different sensitivities, of which two targeted genes (EGFR and BCR) showed significant differences in expression between the two risk groups (Fig. 6B) . Finally, as shown in Fig. 6C , a Sankey diagram was constructed to visualize the correspondence between samples from different groups. Most of the low-risk group samples corresponded to high-expression group samples. 4. Discussion LUSC is the second most common lung malignancy in the world, accounting for 20–30% of non-small cell lung cancer cases and causing approximately 400,000 deaths worldwide each year. LUSC has a low cure rate, with a 5-year survival rate of 18%. The treatment for LUSC mainly includes surgery and chemoradiotherapy, the efficacy for advanced patients is limited, and early diagnosis and treatment are effective ways to improve the survival status of LUSC patients. Compared with lung adenocarcinoma, LUSC has a poor clinical prognosis and lacks targeted drugs. DHX32 is a helicase in the DHX family. The expression level of DHX32 can be found in the tissues of colorectal cancer, breast cancer, and liver cancer, which is higher than that of the adjacent tissues, and can promote the proliferation, migration, and invasion of cancer cells, which is related to the prognosis of patients. However, most studies on DHX32 focus on liver cancer, and there are few studies on LUSC, especially on diagnosis and prognosis. The results of this study showed that DHX32 was up-regulated in LUSC tissues, which was the same as the expression of colorectal cancer and liver cancer, and the opposite of acute lymphoblastic leukemia. The expression level of DHX32 in the LUSC sample group was significantly higher than that in the control group, and the survival rate of the high expression group was significantly higher than that of the low expression group, indicating that DHX32 has diagnostic value. This is similar to the prognostic and diagnostic role of DHX32 in breast cancer [ 12 ] . The Willcoxon hypothesis showed that the infiltration of two kinds of immune cells (Mast cells resting and T cells CD4 naive) in the high-expression group was significantly lower than that in the low-expression group. Mast cells influence the occurrence and development of tumors by promoting inflammation, promoting angiogenesis, regulating immune response, and promoting tumor invasion and metastasis. CD4 + T cells influence tumor development through immunomodulatory effects. Based on the expression levels of immune checkpoint inhibitors PD-1, PD-L1, and CTLA-4 in LUSC, we analyzed the differences between the high and low expression groups, and the results showed that CTLA-4 and PD-1 immune checkpoint inhibitors had significant differences between the high and low expression groups. CTLA-4 mainly inhibits the activation and proliferation of T cells, thereby reducing the body's ability to attack tumor cells. PD-1 mainly inhibits the killing function of T cells, so that tumor cells can evade immune surveillance. In this study, 347 differential genes were screened based on the expression level of DHX32. Univariate Cox regression analysis was used to obtain 11 genes significantly associated with survival. Multivariate Cox regression analysis was used to screen out CLDN11, PID1, MAMDC2, and three prognostic genes. CLDN11 is a tight-linking protein that is abnormally expressed in many tumors. The abnormal expression of CLDN11 can promote the proliferation, invasion, and metastasis of tumor cells. For example, in breast cancer, high expression of CLDN11 is closely related to tumor malignancy and prognosis [ 13 ] . PID1 plays an important role in cell division and apoptosis. No studies have shown that PID1 is directly involved in the development and development of cancer. MAMDC2 is a coding protein, that is abnormally expressed or mutated in many cancers and has cancer inhibition, cell migration and invasion, cell cycle regulation, and interaction with microtubule-associated proteins in the occurrence and development of cancer. These three independent prognostic genes also play a critical role in LUSC and are expected to be new targets for LUSC. Through correlation analysis between risk score and clinical features, this study found that risk score, age, and gender were correlated with risk score. Univariate Cox regression analysis found that risk score and age were significantly correlated with survival; multivariate Cox regression screened out an independent prognostic factor for risk score, and the study found that risk score was negatively correlated with tumor survival. The lower the risk score, the higher the overall survival probability of 1, 3, and 5 years. The five enrichment pathways in this study have been reported to be closely related to the prognosis of low-grade glioma [ 14 ] , female reproductive tract tumor [ 15 ] , lung cancer [ 16 ] and liver cancer [ 17 ] , respectively. They regulate the occurrence and development of cancer through immune surveillance and escape mechanisms, inflammatory response, promotion of new angiogenesis, and intercellular interactions. This research shows that there are five kinds of drug sensitivity with significant differences, respectively Bosutinib, Docetaxel, Gefitinib and Erlotinib, and Lapatinib. The difference between EGFR and BCR in the target genes of these drugs was significant in high and low-risk groups. Bosutinib is a tyrosine kinase inhibitor that targets receptor tyrosine kinases such as BCR-ABL, KIT, and PDGFR. By inhibiting the activity of these receptors, it blocks the growth and proliferation signal transduction of tumor cells, thus inhibiting tumor growth [ 18 ] . Docetaxel belongs to a class of drugs called paclitaxel, which primarily prevents cell division by inhibiting the dynamic stability of tubulin. It causes tubulin to polymerize into clumped structures, causing cells to fail to form normal spindles during mitosis, thus preventing tumor cells from dividing and proliferating [ 19 ] . Gefitinib is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor. It inhibits the growth and proliferation of tumor cells by selectively inhibiting the tyrosine kinase activity of EGFR and blocking EGFR signaling [ 20 ] . Erlotinib is also an EGFR tyrosine kinase inhibitor, similar to Gefitinib, but more selective to EGFR. It inhibits the growth and proliferation of tumor cells by inhibiting the activity of EGFR and blocking EGFR signaling [ 21 ] . Lapatinib is a multi-target tyrosine kinase inhibitor that simultaneously inhibits EGFR and other receptors such as HER2 and VEGFR. By inhibiting the activity of these receptors, it blocks the growth and proliferation signal transduction of tumor cells, thus inhibiting tumor growth [ 22 ] . 5. Conclusion This study explored the relationship between DHX32 expression, prognosis, diagnosis, immunotherapy, and drug sensitivity in LUSC, and constructed a prognostic risk model for independent prognostic analysis. It is proved that the relationship between DHX32 and LUSC can provide new ideas for clinical practice. However, this study still needs further experimental mechanism research and clinical application exploration, and we will continue to pay attention to the role of DHX32. Declarations Funding This study was supported by grants from the Gansu Provincial People's Hospital Research Funding (22GSSYD-30; 22GSSYD-25, 22GSSYC-9), Gansu Youth Science and Technology Fund (22JR11RA241)， and Science and Technology Department of Gansu Province (22YF7FA095) Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability Statements The data that supports the findings of this study are available in the supplementary material of this article. Author Contribution W.Z. and D.H. wrote the main manuscript text, and W.Z.J prepared Figures 1-6. Y is responsible for design research. All the authors reviewed the manuscript References Siegel R L, Miller K D, Jemal A. Cancer statistics, 2020[J]. CA: a cancer journal for clinicians, 2020, 70(1): 7–30. Xu F, Lin H, He P, et al. 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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-4375543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304272907,"identity":"516293b3-8c08-4302-9fe1-4a9685243155","order_by":0,"name":"wei cao","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"wei","middleName":"","lastName":"cao","suffix":""},{"id":304272908,"identity":"236fd691-1b9b-49f0-b55d-3d11c2719f92","order_by":1,"name":"Zhuang Zuo","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhuang","middleName":"","lastName":"Zuo","suffix":""},{"id":304272909,"identity":"9ed75bc4-bfb6-4040-993a-59a717b624f8","order_by":2,"name":"Dacheng Jin","email":"","orcid":"","institution":"Chest Clinic Center, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dacheng","middleName":"","lastName":"Jin","suffix":""},{"id":304272910,"identity":"607b4e69-d4cc-4918-954f-e2caa7174eec","order_by":3,"name":"Haochi Li","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haochi","middleName":"","lastName":"Li","suffix":""},{"id":304272911,"identity":"47109b5d-5e8b-4a0f-904b-b936b13fcace","order_by":4,"name":"Weirun Min","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weirun","middleName":"","lastName":"Min","suffix":""},{"id":304272912,"identity":"db13e8b9-5a05-450d-af5c-ea2f3bcd92e9","order_by":5,"name":"Jinlong Zhang","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinlong","middleName":"","lastName":"Zhang","suffix":""},{"id":304272913,"identity":"33211ec5-ad4d-496a-a189-903806263a6e","order_by":6,"name":"Zhaohao Lin","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhaohao","middleName":"","lastName":"Lin","suffix":""},{"id":304272914,"identity":"f475663e-f727-4231-b0ba-9f0ae3704a1f","order_by":7,"name":"Yunjiu Gou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYPACNh4G9sbGhx9I08JzuNlYgjSLJNLbBHiIUSjv3mMmzbuDT8bg5sM2BgkGOzndBgJaDM+cAWo5w8ZjcDux7UEBQ7Kx2QFCWmbkALW0gbW0G0gwHEjcRryWmwfbJHiI0SIvAdNyg5FILQY8x4ot5wK1SJ5JBAayARF+kW9v3njjbdsxe77jxx8+/FBhJ0dQi8EBBhZgBB6DcQkoB9vSwMAMTCY1RCgdBaNgFIyCEQsAbCM9brLDV1IAAAAASUVORK5CYII=","orcid":"","institution":"Chest Clinic Center, Gansu Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yunjiu","middleName":"","lastName":"Gou","suffix":""}],"badges":[],"createdAt":"2024-05-06 09:02:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4375543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4375543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56804341,"identity":"318b87cc-692e-40cd-b2cf-364d035dd75e","added_by":"auto","created_at":"2024-05-20 17:09:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDHX32 had decent prognostic and diagnostic value in LUSC. \u003c/strong\u003e(A) Differential expression analysis of DHX32. (B) DHX32 expression matched the box plot. The lines between the boxes indicate the amount of DHX32 gene expression in the same sample in both disease and control groups. (C) High and low expression group K-M curve. (D) ROC curve.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/76824ef0d3c02f1b76303999.jpg"},{"id":56804342,"identity":"ebc6ab4e-bbb3-41d4-87aa-f4d9f3403a6c","added_by":"auto","created_at":"2024-05-20 17:09:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe significant immune differences between DHX32 high-low expression groups. \u003c/strong\u003e(A) GSEA enrichment analysis results. (B) Difference analysis of immune cell infiltration. The Willcoxon hypothesis test was performed on each infiltrated immune cell, and the group box plot was drawn using R-packet ggpurbr. (C) Differential analysis of immune checkpoint inhibitors. (D) Correlation scatter plot. Based on the gene expression matrix of LUSC samples in the training set, the TIDE database was used (Tumor Immune Dysfunction and Exclusion, http://tide.dfci.harvard.edu) prediction of high and low expression group LUSC TIDE value, analyze its relevance to risk score. The red dots represent the corresponding relationship between patient gene expression and TIDE, and the blue slash lines represent the trend lines. (E) The subMap algorithm was used in the training set to predict the sensitivity of high and low-expression groups to PD-1 and CTLA-4 inhibitors to indirectly predict immunotherapy response.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/cbd01e92f5f248edcfc583a6.jpg"},{"id":56804792,"identity":"39659985-25da-4bea-8587-a7c077815acd","added_by":"auto","created_at":"2024-05-20 17:17:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstablishment and Validation of Risk Model. \u003c/strong\u003e(A) Differential expression analysis volcano map. The tags show the top 10 genes of significant and down-regulated genes (sorted from largest to smallest by |Log2FC| and from smallest to largest by p.adj). (B) The expression matrix of the top 10 genes was selected to draw the differential expression heat map. (C) Unifactor Cox analysis results in forest map. (D) Multivariate Cox analysis results in Forest map. (E) Risk score distribution map. (F) Survival state distribution map. (G) K-M curve. (H) ROC curve. (I) Risk score distribution map. (J) Recurrence profile map. (K) K-M curve. (L) ROC curve.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/5b3003311430c0755c790293.jpg"},{"id":56804345,"identity":"dbfc384a-f1ab-426f-86e9-14aa47ca32b0","added_by":"auto","created_at":"2024-05-20 17:09:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between clinical features and risk scores, independent prognostic analysis, and validation. \u003c/strong\u003e(A) Analysis of differences in risk scores among age subgroups. (B) Analysis of differences in risk scores between sex subgroups. (C) Analysis of the difference of risk scores among N subgroups in pathological stage. (D) Analysis of the difference in risk scores among pathological stage T subgroups. (E) Unifactor Cox regression analysis of forest map. (F) Multivariate Cox analysis results in Forest map. (G) Nomogram. The nomogram consists of four parts: the first part is points, which represent the individual score for a given risk score. The second part is the variable, the risk score. There is a line segment behind the variable. The value range represents the total contribution value of the variable to the outcome event, and the scale on the line segment represents different values of the variable. The third part is total points, which represents the total score of a single variable value. The fourth part is the predicted value of 1, 3, and 5 years survival probability, which represents the survival probability of patients after the total score is converted. (H) In the model based on the nomogram, R-packet RMS was used to predict the survival probability of patients in the training set for 1, 3, and 5 years, and the results were drawn as correction curves to observe the consistency of the predicted survival probability and the actual survival probability. (I) Decision curve. You can see that Age, risk Score, and Prog Model are all on the top right of the reference curve, indicating that the model has good performance.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/56b621a0b95b1062a0d6f542.jpg"},{"id":56804344,"identity":"c0e8efca-4c5c-467c-be36-1d99fb42d583","added_by":"auto","created_at":"2024-05-20 17:09:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of immune cell infiltration. \u003c/strong\u003e(A) The R-package clusterProfiler was used to conduct GSEA enrichment analysis for all genes in the high-low risk group in the training set, and the threshold was P\u0026lt;0.05. (B) Difference analysis of immune cell infiltration. The Willcoxon hypothesis test was performed on each infiltrated immune cell, and the group box plot was drawn using R-packet ggpurbr.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/a98ac3205a3ab7db3c58ff47.jpg"},{"id":56804793,"identity":"de683361-378d-4ac6-99d7-e9af3c02efb2","added_by":"auto","created_at":"2024-05-20 17:17:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis. \u003c/strong\u003e(A) The IC\u003csub\u003e50\u003c/sub\u003e of common chemotherapeutic and molecular-targeted drugs in each sample was calculated using R-packet pRRophetic, the sensitivity of each drug was tested by the willcoxon hypothesis, and the grouping box plot was drawn using R-packet ggpurbr. (B) Analysis of drug target gene difference in high and low-risk groups. (C) The correlation between risk score and DHX32 gene expression.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/0116da9d6c3e2d75900c1ab3.jpg"},{"id":61057749,"identity":"42064ac0-2a97-413c-9908-7b61b68aa5e0","added_by":"auto","created_at":"2024-07-25 05:54:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/582f2caf-cac9-4013-8381-d1ea7826e8c8.pdf"},{"id":56804347,"identity":"23b8ccde-542f-4d7e-88ba-c8a66c1c449b","added_by":"auto","created_at":"2024-05-20 17:09:53","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":48488974,"visible":true,"origin":"","legend":"","description":"","filename":"Data.zip","url":"https://assets-eu.researchsquare.com/files/rs-4375543/v1/fbceca7228426a946b1fa880.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of DHX32 as diagnostic gene and independent prognostic factor of lung squamous cell carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer (LC) is one of the deadliest malignancies worldwide and can be divided into two main types: small-cell lung cancer and non-small-cell lung cancer. Non-small cell Lung cancer (NSCLC) accounts for about 85% of lung cancer cases and is mainly divided into lung adenocarcinoma and Lung squamous carcinoma (LUSC) according to pathogenesis and histological morphology\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. LUSC accounts for approximately 30% of all lung cancer cases and, globally, causes approximately 400,000 deaths per year\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. At present, patients with early non-small cell lung cancer often need surgical resection. For advanced patients who cannot be surgically resected, the best treatment is targeted therapy or immunotherapy combined with chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Compared with lung adenocarcinoma, LUSC has a poor clinical prognosis and lacks targeted drugs. Therefore, it is extremely important to look for potential biomarkers in LUSC and understand their impact on the prognosis of patients with LUSC.\u003c/p\u003e \u003cp\u003eDEAH-box helicase 32 (DHX32) is a helicase in the DHX family\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e that is involved in many RNA-related biological processes, including ribosome biosynthesis, transcription, mRNA splicing, and translation. DHX32 is dysregulated in a variety of diseases, such as down-regulated DHX32 in acute lymphoblastic leukemia but up-regulated in colorectal cancer and breast cancer. In addition, the abnormal expression of DHX32 is closely related to the occurrence and development of cancer. The expression level of DHX32 is correlated with the clinicopathological features of cancer\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, most studies on DHX32 focus on liver cancer, and there are few studies on LUSC, especially on diagnosis and prognosis. Therefore, this study mainly analyzed the expression, prognosis, and diagnosis of DHX32 in LUSC, as well as its relationship with immunotherapy and drug sensitivity, and constructed a prognostic model to develop anti-cancer treatment strategies based on DHX32 expression and function.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Extraction\u003c/h2\u003e \u003cp\u003eThe transcriptome and clinical data of the Lung squamous carcinoma (LUSC)-related datasets (GSE30219 and GSE8894) were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE30219 contained 60 lung tissue samples of LUSC and 14 control samples with survival information. The GSE8894 contained 75 lung tissue samples of LUSC with survival information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Difference analysis\u003c/h2\u003e \u003cp\u003eThe expression difference of the DHX32 gene between LUSC and control samples in GSE30219 was analyzed by R \u0026lsquo;rstatix\u0026rsquo; software (version 0.7.0). The \u0026ldquo;limma\u0026rdquo; R package (version 3.50.1) was used to analyze the differentially expressed genes (DEGs) of GSE30219 (|Log2FC|\u0026gt;1, p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eBased on the median expression of the DHX32 gene and median risk scores, GSE30219 was classed into different expression/risk groups. The R-package \u0026lsquo;clusterProfiler\u0026rsquo; (version 4.2.2) was used to perform gene set enrichment analysis (GSEA) on all genes in different groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Immune Analysis\u003c/h2\u003e \u003cp\u003eThe Willcoxon hypothesis test was performed for 22 kinds of immune cell infiltration abundance. The TIDE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to forecast the TIDE value of LUSC patients. In addition, the SubMap algorithm was used to predict the sensitivity of PD-1 and CTLA-4 inhibitors\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction of risk model\u003c/h2\u003e \u003cp\u003eThe Cox analyses was used to screen prognostic genes by R-packet \u0026lsquo;survival\u0026rsquo;. The Kaplan-Meier (K-M) curve (survival package (version 0.4.9)) and receiver operating characteristic (ROC) (survival ROC (version 1.0.3)) curve were used to evaluate the model performance\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Creation of the Nomogram\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were used for independent prognostic analysis. A nomogram with independent prognostic factors was created to predict 1-/3-/5-year survival. In addition, the Calibration curve and decision curve verified the performance of the model\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Drug sensitivity analyses\u003c/h2\u003e \u003cp\u003eGDSC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.Cancerrxgene.org\u003c/span\u003e\u003cspan address=\"https://www.Cancerrxgene.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to access the chemotherapy drugs of LUSC. The IC50 of the drug in each sample was calculated using the R-package \u0026lsquo;pRRophetic\u0026rsquo; (version 0.5)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Target genes of drugs with significant sensitivity differences were predicted through the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe R software processed and analyzed the data. Differences were analyzed via the Wilcoxon rank-sum test. The p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 represented a significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.1 DHX32 had decent prognostic and diagnostic value in LUSC\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;1A-1B, the expression trend of DHX32 was obviously higher in LUSC group campared to that in control group. The LUSC samples were divided into high and low truncation groups based on the optimal truncation value of DHX32 gene expression in the GSE30219. The survival rate of the high truncation value group was higher (Fig.\u0026nbsp;1C\u003cstrong\u003e)\u003c/strong\u003e. The area under curve (AUC) of the curve was 0.7, indicating that DHX32 had prognostic diagnostic value for LUSC \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;1D\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 The significant immune differences between DHX32 high-low expression groups\u003c/h2\u003e\n \u003cp\u003eGSE30219 was divided into high and low-expression groups according to the median expression of DHX32. Based on two expression groups, GSEA was performed to explore the biological function. The GSEA results showed that all gene was enriched to 39 pathways in total, such as \u0026ldquo;cell cycle\u0026rdquo; and \u0026ldquo;cell adhesion molecules cams\u0026rdquo;, etc \u003cstrong\u003e(Fig.\u0026nbsp;2A)\u003c/strong\u003e. As shown in \u003cstrong\u003eFig.\u0026nbsp;2B\u003c/strong\u003e, the infiltration abundance of resting Mast cells and naive CD4 T cells were different between the two groups. Thereafter, DHX32 was weakly correlated with TIDE (cor\u0026thinsp;=\u0026thinsp;0.117) \u003cstrong\u003e(Fig.\u0026nbsp;2C)\u003c/strong\u003e. Moreover, the expression of CTLA-4 and PD-1 were significantly different in the two expression groups, thereafter, the high-expression group had a significant response to PD1 \u003cstrong\u003e(Fig.\u0026nbsp;2D-2E)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.3 Establishment and Validation of Risk Model\u003c/h2\u003e\n \u003cp\u003eVolcanic map and heat map showed 347 DEGs in two groups, including 101 upregulated genes and 246 down-regulated genes \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3A-3B\u003cstrong\u003e)\u003c/strong\u003e. Based on DEGs, prognostic genes were screened by Cox analysis, namely \u003cem\u003eCLDN11\u003c/em\u003e, \u003cem\u003ePID1\u003c/em\u003e, and \u003cem\u003eMAMDC2\u003c/em\u003e \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3C-3D\u003cstrong\u003e)\u003c/strong\u003e. The LUSC samples in GSE30219 were divided into high and low-risk groups \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3E-3F\u003cstrong\u003e)\u003c/strong\u003e. The sample survival of the high-risk group was lower \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3G\u003cstrong\u003e)\u003c/strong\u003e. The 1-/3-/5-year forecast AUC for the training set were 0.85, 0.83, and 0.82, indicating that the model had high prediction accuracy \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3H\u003cstrong\u003e)\u003c/strong\u003e. Moreover, we performed validation processing in GSE8894, and the results were consistent with GSE30219 \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3I-4L\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.4 Creation of the nomogram\u003c/h2\u003e\n \u003cp\u003eThe differences in risk scores among subgroups of clinical factors age (\u0026gt;\u0026thinsp;60, \u0026lt;=60), gender (male (M), female (F)), pathological stage N (N0, N1), and pathological stage T (T2-T4, T1) were compared. The risk score of the female sample and \u0026gt;\u0026thinsp;60 groups were significantly higher than that of the male sample and \u0026lt;\u0026thinsp;=\u0026thinsp;60 groups, and there was no obvious difference in the risk score between pathological stage N and pathological stage T subgroups, indicating that the age and gender were highly correlated with the risk score \u003cstrong\u003e(Fig.\u0026nbsp;4A-4D)\u003c/strong\u003e. In univariate Cox analysis, riskScore (HR\u0026thinsp;=\u0026thinsp;2.718) and age (HR\u0026thinsp;=\u0026thinsp;1.039) could be used as risk factors for prognosis \u003cstrong\u003e(Fig.\u0026nbsp;4E)\u003c/strong\u003e. However, the riskScore (HR\u0026thinsp;=\u0026thinsp;2.687) was an independent prognostic risk factor for LUSC by multivariate Cox analysis \u003cstrong\u003e(Fig.\u0026nbsp;4F)\u003c/strong\u003e. The nomogram containing the risk Score and age was created \u003cstrong\u003e(Fig.\u0026nbsp;4G)\u003c/strong\u003e. The calibration curves showed that the curves of 1-/ 3-/ 5-years were close to the theoretical value \u003cstrong\u003e(Fig.\u0026nbsp;4H)\u003c/strong\u003e. The result of the decision curve indicated that the model had good performance \u003cstrong\u003e(Fig.\u0026nbsp;4I)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.5 Memory B cells was identified as differential immune cells\u003c/h2\u003e\n \u003cp\u003eBased on the two risk groups, there were 17 pathways were enriched, including \u0026ldquo;intestinal immune network for IgA production\u0026rdquo; and \u0026ldquo;systemic lupus erythematosus\u0026rdquo;, etc \u003cstrong\u003e(Fig.\u0026nbsp;5A)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eBecause GSEA was enriched in immune-related signaling pathways, we then performed an immune infiltration analysis. The results showed that there were significant differences in memory B cells between the two risk groups \u003cstrong\u003e(Fig.\u0026nbsp;5B)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.6 Drug sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eIn total, the IC50 of 5 anti-cancer drugs between the two risk groups were significantly different, namely Bosutinib, Docetaxel, Gefitinib, Erlotinib, and Lapatinib \u003cstrong\u003e(Fig.\u0026nbsp;6A)\u003c/strong\u003e. The 18 targeted genes were predicted based on 5 drugs with significantly different sensitivities, of which two targeted genes (EGFR and BCR) showed significant differences in expression between the two risk groups \u003cstrong\u003e(Fig.\u0026nbsp;6B)\u003c/strong\u003e. Finally, as shown in \u003cstrong\u003eFig.\u0026nbsp;6C\u003c/strong\u003e, a Sankey diagram was constructed to visualize the correspondence between samples from different groups. Most of the low-risk group samples corresponded to high-expression group samples.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLUSC is the second most common lung malignancy in the world, accounting for 20\u0026ndash;30% of non-small cell lung cancer cases and causing approximately 400,000 deaths worldwide each year. LUSC has a low cure rate, with a 5-year survival rate of 18%. The treatment for LUSC mainly includes surgery and chemoradiotherapy, the efficacy for advanced patients is limited, and early diagnosis and treatment are effective ways to improve the survival status of LUSC patients. Compared with lung adenocarcinoma, LUSC has a poor clinical prognosis and lacks targeted drugs. DHX32 is a helicase in the DHX family. The expression level of DHX32 can be found in the tissues of colorectal cancer, breast cancer, and liver cancer, which is higher than that of the adjacent tissues, and can promote the proliferation, migration, and invasion of cancer cells, which is related to the prognosis of patients. However, most studies on DHX32 focus on liver cancer, and there are few studies on LUSC, especially on diagnosis and prognosis.\u003c/p\u003e \u003cp\u003eThe results of this study showed that DHX32 was up-regulated in LUSC tissues, which was the same as the expression of colorectal cancer and liver cancer, and the opposite of acute lymphoblastic leukemia. The expression level of DHX32 in the LUSC sample group was significantly higher than that in the control group, and the survival rate of the high expression group was significantly higher than that of the low expression group, indicating that DHX32 has diagnostic value. This is similar to the prognostic and diagnostic role of DHX32 in breast cancer\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Willcoxon hypothesis showed that the infiltration of two kinds of immune cells (Mast cells resting and T cells CD4 naive) in the high-expression group was significantly lower than that in the low-expression group. Mast cells influence the occurrence and development of tumors by promoting inflammation, promoting angiogenesis, regulating immune response, and promoting tumor invasion and metastasis. CD4\u003csup\u003e+\u003c/sup\u003eT cells influence tumor development through immunomodulatory effects.\u003c/p\u003e \u003cp\u003eBased on the expression levels of immune checkpoint inhibitors PD-1, PD-L1, and CTLA-4 in LUSC, we analyzed the differences between the high and low expression groups, and the results showed that CTLA-4 and PD-1 immune checkpoint inhibitors had significant differences between the high and low expression groups. CTLA-4 mainly inhibits the activation and proliferation of T cells, thereby reducing the body's ability to attack tumor cells. PD-1 mainly inhibits the killing function of T cells, so that tumor cells can evade immune surveillance.\u003c/p\u003e \u003cp\u003eIn this study, 347 differential genes were screened based on the expression level of DHX32. Univariate Cox regression analysis was used to obtain 11 genes significantly associated with survival. Multivariate Cox regression analysis was used to screen out CLDN11, PID1, MAMDC2, and three prognostic genes. CLDN11 is a tight-linking protein that is abnormally expressed in many tumors. The abnormal expression of CLDN11 can promote the proliferation, invasion, and metastasis of tumor cells. For example, in breast cancer, high expression of CLDN11 is closely related to tumor malignancy and prognosis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. PID1 plays an important role in cell division and apoptosis. No studies have shown that PID1 is directly involved in the development and development of cancer. MAMDC2 is a coding protein, that is abnormally expressed or mutated in many cancers and has cancer inhibition, cell migration and invasion, cell cycle regulation, and interaction with microtubule-associated proteins in the occurrence and development of cancer. These three independent prognostic genes also play a critical role in LUSC and are expected to be new targets for LUSC.\u003c/p\u003e \u003cp\u003eThrough correlation analysis between risk score and clinical features, this study found that risk score, age, and gender were correlated with risk score. Univariate Cox regression analysis found that risk score and age were significantly correlated with survival; multivariate Cox regression screened out an independent prognostic factor for risk score, and the study found that risk score was negatively correlated with tumor survival. The lower the risk score, the higher the overall survival probability of 1, 3, and 5 years.\u003c/p\u003e \u003cp\u003eThe five enrichment pathways in this study have been reported to be closely related to the prognosis of low-grade glioma \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, female reproductive tract tumor\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, lung cancer\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and liver cancer\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, respectively. They regulate the occurrence and development of cancer through immune surveillance and escape mechanisms, inflammatory response, promotion of new angiogenesis, and intercellular interactions.\u003c/p\u003e \u003cp\u003eThis research shows that there are five kinds of drug sensitivity with significant differences, respectively Bosutinib, Docetaxel, Gefitinib and Erlotinib, and Lapatinib. The difference between EGFR and BCR in the target genes of these drugs was significant in high and low-risk groups. Bosutinib is a tyrosine kinase inhibitor that targets receptor tyrosine kinases such as BCR-ABL, KIT, and PDGFR. By inhibiting the activity of these receptors, it blocks the growth and proliferation signal transduction of tumor cells, thus inhibiting tumor growth\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Docetaxel belongs to a class of drugs called paclitaxel, which primarily prevents cell division by inhibiting the dynamic stability of tubulin. It causes tubulin to polymerize into clumped structures, causing cells to fail to form normal spindles during mitosis, thus preventing tumor cells from dividing and proliferating\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Gefitinib is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor. It inhibits the growth and proliferation of tumor cells by selectively inhibiting the tyrosine kinase activity of EGFR and blocking EGFR signaling\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Erlotinib is also an EGFR tyrosine kinase inhibitor, similar to Gefitinib, but more selective to EGFR. It inhibits the growth and proliferation of tumor cells by inhibiting the activity of EGFR and blocking EGFR signaling\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Lapatinib is a multi-target tyrosine kinase inhibitor that simultaneously inhibits EGFR and other receptors such as HER2 and VEGFR. By inhibiting the activity of these receptors, it blocks the growth and proliferation signal transduction of tumor cells, thus inhibiting tumor growth\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study explored the relationship between DHX32 expression, prognosis, diagnosis, immunotherapy, and drug sensitivity in LUSC, and constructed a prognostic risk model for independent prognostic analysis. It is proved that the relationship between DHX32 and LUSC can provide new ideas for clinical practice. However, this study still needs further experimental mechanism research and clinical application exploration, and we will continue to pay attention to the role of DHX32.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Gansu Provincial People's Hospital\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch Funding (22GSSYD-30; 22GSSYD-25, 22GSSYC-9), Gansu Youth Science and Technology Fund (22JR11RA241)，\u0026nbsp;and Science and Technology Department of Gansu Province (22YF7FA095)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available in the supplementary material of this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.Z. and D.H. wrote the main manuscript text, and W.Z.J prepared Figures 1-6. Y is responsible for design research. All the authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel R L, Miller K D, Jemal A. Cancer statistics, 2020[J]. CA: a cancer journal for clinicians, 2020, 70(1): 7\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eXu F, Lin H, He P, et al. A TP53-associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma[J]. Oncoimmunology, 2020, 9(1): 1731943.\u003c/li\u003e\n\u003cli\u003eZhao Z, Cai Q, Zhang P, et al. N6-Methyladenosine RNA Methylation Regulator-Related Alternative Splicing (AS) Gene Signature Predicts Non-Small Cell Lung Cancer Prognosis[J]. Frontiers in Molecular Biosciences, 2021, 8: 657087.\u003c/li\u003e\n\u003cli\u003eCai M-J, Zhu J-H, He J-Q, et al. Silencing of DHX32 increases the proliferation of liver cancer cells[J]. Translational Cancer Research, 2020, 9(3): 1833\u0026ndash;1842.\u003c/li\u003e\n\u003cli\u003eWei Q, Geng J, Chen Y, et al. Structure and function of DEAH-box helicase 32 and its role in cancer[J]. Oncology Letters, 2021, 21(5): 382.\u003c/li\u003e\n\u003cli\u003eZou Z, Chai Y, Li Q, et al. Establishment of lactate-metabolism-related signature to predict prognosis and immunotherapy response in patients with colon adenocarcinoma[J]. Frontiers in Oncology, 2022, 12: 958221.\u003c/li\u003e\n\u003cli\u003eWu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innovation (Cambridge (Mass.)), 2021, 2(3): 100141.\u003c/li\u003e\n\u003cli\u003eWang Q, Li M, Yang M, et al. Analysis of immune-related signatures of lung adenocarcinoma identified two distinct subtypes: implications for immune checkpoint blockade therapy[J]. Aging, 2020, 12(4): 3312\u0026ndash;3339.\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves[J]. BMC bioinformatics, 2011, 12: 77.\u003c/li\u003e\n\u003cli\u003eSun W, Xu Y, Zhao B, et al. The prognostic value and immunological role of angiogenesis-related patterns in colon adenocarcinoma[J]. Frontiers in Oncology, 2022, 12: 1003440.\u003c/li\u003e\n\u003cli\u003eJiang H-Z, Yang B, Jiang Y-L, et al. Development and validation of prognostic models for colon adenocarcinoma based on combined immune-and metabolism-related genes[J]. Frontiers in Oncology, 2022, 12: 1025397.\u003c/li\u003e\n\u003cli\u003eWang M, Zhang G, Wang Y, et al. DHX32 expression is an indicator of poor breast cancer prognosis[J]. Oncology Letters, 2017, 13(2): 942\u0026ndash;948.\u003c/li\u003e\n\u003cli\u003eLiu K, Wang Y, Shao W, et al. Unveiling the oncogenic role of CLDN11-secreting fibroblasts in gastric cancer peritoneal metastasis through single-cell sequencing and experimental approaches[J]. International Immunopharmacology, 2024, 129: 111647.\u003c/li\u003e\n\u003cli\u003eYang J, Shen L, Yang J, et al. Complement and coagulation cascades are associated with prognosis and the immune microenvironment of lower-grade glioma[J]. Translational Cancer Research, 2024, 13(1): 112\u0026ndash;136.\u003c/li\u003e\n\u003cli\u003eWang K, Wang S, Ding Y, et al. Exploring the Molecular Mechanisms and Shared Gene Signatures Between Systemic Lupus Erythematosus and Bladder Urothelial Carcinoma[J]. International Journal of General Medicine, 2024, 17: 705\u0026ndash;723.\u003c/li\u003e\n\u003cli\u003eLee D, Lee P C-W, Hong J H. UBA6 Inhibition Accelerates Lysosomal TRPML1 Depletion and Exosomal Secretion in Lung Cancer Cells[J]. International Journal of Molecular Sciences, 2024, 25(5): 2843.\u003c/li\u003e\n\u003cli\u003eWang J, Hu Y, Zhao K, et al. Comprehensive Analysis of the Expression of Cell Adhesion Molecules Genes in Hepatocellular Carcinoma and their Prognosis, and Biological Significance[J]. Frontiers in Bioscience (Landmark Edition), 2024, 29(2): 76.\u003c/li\u003e\n\u003cli\u003eRassi F E, Khoury H J. Bosutinib: a SRC-ABL tyrosine kinase inhibitor for treatment of chronic myeloid leukemia[J]. Pharmacogenomics and Personalized Medicine, 2013, 6: 57\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eGupta R, Kadhim M M, Turki Jalil A, et al. The interactions of docetaxel with tumor microenvironment[J]. International Immunopharmacology, 2023, 119: 110214.\u003c/li\u003e\n\u003cli\u003eKumar P, Mangla B, Javed S, et al. Gefitinib: An Updated Review of its Role in the Cancer Management, its Nanotechnological Interventions, Recent Patents and Clinical Trials[J]. Recent Patents on Anti-Cancer Drug Discovery, 2023, 18(4): 448\u0026ndash;469.\u003c/li\u003e\n\u003cli\u003eYang Z, Hackshaw A, Feng Q, et al. Comparison of gefitinib, erlotinib and afatinib in non-small cell lung cancer: A meta-analysis[J]. International Journal of Cancer, 2017, 140(12): 2805\u0026ndash;2819.\u003c/li\u003e\n\u003cli\u003eGv B, Sk Y, S C, et al. Lapatinib nano-delivery systems: a promising future for breast cancer treatment[J]. Expert opinion on drug delivery, Expert Opin Drug Deliv, 2018, 15(5).\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":true,"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":"Lung squamous carcinoma, DHX32, Prognostic factors, Risk score","lastPublishedDoi":"10.21203/rs.3.rs-4375543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4375543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The abnormal of DHX32 was related to the occurrence, development and clinicopathological features of cancer, but DHX32 has not been studied in Lung squamous carcinoma (LUSC). We aimed to investigate the expression , prognosis and biological significance of DHX32 in LUSC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e The LUSC-related datasets (GSE30219 and GSE8894) were included in the analysis. Firstly, the expression and diagnosis of DHX32 were analyzed. The\u0026nbsp;differentially expressed genes (DEGs)\u0026nbsp;of GSE30219 were screened. The Cox \u0026nbsp;analysis was used to screen prognostic genes. Based on the risk scores of LUSC samples, the samples were divided into high-low risk groups. The risk model was constructed by cox analysis in GSE30219, and verified in GSE8894. Furthermore, nomogram, immune infiltration analysis and drug sensitivity analysis were constructed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u0026nbsp;Compared with the control group, the expression of DHX32 in LUSC samples was obviously higher. The survival of high\u0026nbsp;truncation value\u0026nbsp;group was signally higher than that of low\u0026nbsp;truncation value\u0026nbsp;group.\u0026nbsp;Based on 347 DEGs, the 3 prognostic genes (\u003cem\u003eCLDN11\u003c/em\u003e, \u003cem\u003ePID1\u003c/em\u003e and \u003cem\u003eMAMDC2\u003c/em\u003e) were screened. In\u0026nbsp;GSE30219 and GSE8894,\u0026nbsp;the risk model had high accuracy and excellent performance. In addition, the riskScore was independent prognostic risk factors for LUSC by\u0026nbsp;cox analysis. Immune infiltration analysis and\u0026nbsp;drug sensitivity analysis showed that memory B cells and five drug had a difference in two risk groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e DHX32 could be used as a diagnostic gene and independent prognostic factor for LUSC, providing the insight for the diagnosis and treatment of LUSC.\u003c/p\u003e","manuscriptTitle":"Analysis of DHX32 as diagnostic gene and independent prognostic factor of lung squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-20 17:09:48","doi":"10.21203/rs.3.rs-4375543/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":"60bd3fec-3bf2-4c32-a8c1-e8c147af62ea","owner":[],"postedDate":"May 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32121255,"name":"Biological sciences/Cancer"},{"id":32121256,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-07-25T05:46:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-20 17:09:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4375543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4375543","identity":"rs-4375543","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}