KNL1 is a lung cancer prognostic biomarker associated with the immune microenvironment

KNL1 is a lung cancer prognostic biomarker associated with the immune microenvironment | 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 KNL1 is a lung cancer prognostic biomarker associated with the immune microenvironment YiRan Dong, Ting Wu, Jiayang Chen, Liang Mo, Yong You This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4379762/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: Kinetochore scaffold 1 (KNL1) plays a crucial role in cell cycle regulation and is implicated in lung adenocarcinoma (LUAD) progression, especially in the tumor microenvironment and immunotherapy. Our study aims to investigate KNL1's potential as a therapeutic target for LUAD. Methods: We conducted pan-cancer analysis of KNL1 expression using the CancerSea database and performed survival analysis. Functional studies included GO, KEGG, and GESA analyses, as well as protein interaction network construction. Immune infiltration analysis was conducted using six algorithms from the "IOBR" R package. Therapeutic effects of immune checkpoint inhibitors were predicted using the TIDE and TCIA databases, and drug responses were forecasted using the "Oncopredict" R package. Results: KNL1 was significantly expressed across 22 malignancies, including LUAD, and correlated with worse prognosis. Immune infiltration analysis revealed associations between KNL1 expression and various immune cell types. Higher KNL1 expression was associated with increased susceptibility to CTLA4 inhibitors. Drug prediction suggested potential treatments for LUAD patients with high KNL1 expression. Conclusion: Our findings suggest KNL1 as a potential therapeutic target for LUAD, particularly in immunotherapy, making it a valuable biomarker for treatment strategies in this cancer type. TCGA lung adenocarcinoma KNL1 tumour microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key Points 1. Significant finding: Elevated KNL1 expression correlates with poor prognosis and higher immune cell infiltration in lung adenocarcinoma (LUAD). 2. Research contribution: Identifies KNL1 as a potential biomarker and therapeutic target for LUAD, enhancing understanding of its tumor microenvironment and immunotherapy potential. Introduction Globally, lung cancer stands out as the most frequently diagnosed and deadliest malignancy, with lung adenocarcinoma (LUAD) representing its predominant subtype [ 1 – 3 ] . Before immunotherapy was approved, lung cancer without driver mutations was often treated with cytotoxic chemotherapy; lung adenocarcinomas with driver mutations (EGFR, for example) were typically treated with targeted therapy. [ 4 ] Immunotherapy has significantly increased patient survival and transformed the paradigm of tumour treatment. [ 5 ] Nevertheless, patients are less likely to have long-term improvements from immunotherapy or targeted therapy. [ 6 , 7 ] Therefore, finding novel targets or creating fresh immunotherapy regimens that can be applied to treating LUAD patients is essential. Kinetochore scaffold 1 (KNL1,alternatively referred to as CASC5) is a crucial protein that controls kinetochore assembly and spindle formation. It combines with other proteins to produce complexes that support and preserve centromere structures. [ 8 ] Maintaining appropriate chromosome segregation simultaneously modifies the contact between the kinetochore and spindle microtubules through regulatory mechanisms such as phosphorylation and dephosphorylation. [ 9 ] Furthermore, to maintain the precision and stability of cellular mitosis, KNL1 observes errors and initiates spindle assembly checkpoints. [ 10 , 11 ] This gene may be crucial in immunotherapy and is linked to the clinical prognosis of individuals with prostate cancer. [ 12 ] According to bioinformatics research, KNL1 is significantly expressed in gastric cancer and plays a pivotal role in the tumour development. [ 13 ] The research findings indicates that KNL1 is a significant predictor of prognosis in LUAD and is linked to the miR-139-5p target. [ 14 ] Unfortunately, this research did not thoroughly examine the function of KNL1 expression in LUAD. Primarily relying on data from the TCGA repository, we thoroughly examined the differential expression of KNL1 in LUAD in this study. The prognosis of patients and the infiltration of immune cells were correlated with KNL1 expression, and the functional enrichment and protein interactions network revealed possible signalling pathways of KNL1 associated with tumour cell proliferation. Ultimately, the drug sensitivity prediction findings revealed that various medications had IC50 values that varied between high and low KNL1 expression. Our meticulous analysis of KNL1's role in LUAD provides new targets for LUAD treatment. Methods Data Source The Fig. 1 depicts the comprehensive workflow of the study. The UCSC Xena database provided the mRNA expression profiles, survival statistics, mutation data, and other clinical information of the TCGA-LUAD cohort used in this study. We also utilised the Genotype-Tissue Expression database for pan-cancer analysis. KNL1's impact on pan-cancer biological behaviour was examined using CancerSea ( http://biocc.hrbmu.edu.cn/CancerSEA/home . jsp). In order to confirm consistency, we transformed the batch pair mRNA sequencing data using log2(x + 1). Survival data were employed for survival analysis. Identifying the genes that are expressed differently in lung cancer and survival analysis Using the 'DESeq2' R package, differentially expressed genes were found in 510 LUAD samples and 58 normal samples, with cut-off values of P 2.5. The R programme 'ggplot' was applied to generate box plots representing the DEGs. The association between the mRNA level of KNL1 and the overall survival (OS) of pancreatic cancer patients in TCGA was examined using Kaplan-Meier analysis and the log-rank test; survival curves with p < 0.05 were displayed. In addition, we built and plotted time-dependent ROC curves to analyse KNL1's predictive performance at 1, 3, and 5 years in order to establish whether it may be created as a prognostic indicator for patients. The main evaluation parameter chosen was the the area under the curve (AUC) value. Interactions between proteins and functional enrichment analysis After that, functional enrichment analysis was carried out for DEG. Molecular function (MF), cellular composition (CC), and biological process (BP) were all included in the gene ontology (GO) enrichment study. Signalling pathways in DEGs were analysed using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) functional enrichment and gene set enrichment analysis (GSEA). 'clusterProfiler' and 'ggplot2' R packages were used for analysis and visualisation. Furthermore, using the Cytoscape programme (Cytoscape, 3.10.1) and the String database, Protein-Protein Interaction Networks (PPI) with an interaction score > 0.7 were mapped. Analysis of Immune Infiltration and Forecasting the Effectiveness of Immune Checkpoint Inhibitor Treatment Six algorithms—CIBERSORT [ 15 ] , xCell [ 16 ] , MCPcounter [ 17 ] , ESTIMATE [ 18 ] , EPIC [ 19 ] , and quanTIseq [ 20 ] —included in the "IOBR" R package [ 21 ] were used to analyze KNL1's immune infiltration. The groups with high and low expression levels of KNL1 were evaluated for differences in immune cell infiltration. Using the GEPIA2 website, the connection between KNL1 and immune checkpoint-associated genes was examined. Then, the TCIA website was utilized to acquire the IPS of patients with TCGA-LUAD, which allowed for predicting the patient's response to PDL-1 and CLAT-4 inhibitors. Analysis of drug sensitivity Maeser et al. created the "oncopredict" R programme to forecast cancer patients' medication response. [ 22 ] Using training data from the GDSC2 database and oncoPredict, a predictive model for drug response was developed utilizing gene expression profiling data from the TCGA LUAD cohort.. The predictive model mostly uses the default values.( https://cran.rproject.org/web/packages/oncoPredict/oncoPredict.pdf ) The sensitivity of the medicinal products between the high and low KNL1 expression groups was evaluated using an unpaired t-test, resulting in a total of 128 medications. A p < 0.05 significance level was applied. Results 1. Expression and function of KNL1 in pan-cancer First, we examined the KNL1 gene expression in pan-cancer by utilizing the combined GTEx and TCGA databases. The findings revealed that the mRNA level of KNL1 varied significantly among 25 malignancies (Fig. 2 A). When compared to adjacent normal tissue adjacent to the cancer, the mRNA level of KNL1 was highly expressed in 22 tumours and lowly expressed in 3 cancers; nevertheless. But KNL1 did not exhibit a distinct expression pattern. Using the "DESeq2" R programme, TCGA-LUAD data were computed, yielding a total of 1443 differential genes (DEG, LogFc > 2.5, P < 0.001). When LUAD was compared to normal tissue next to the malignancy, KNL1 expression was significantly higher (logFc = 2.549604, p < 0.001). This outcome aligned with the combined database's outcomes (Figure). Using the CancerSEA database, p-values and correlation results were obtained to further analyse the functional state of KNL1 in various malignancies (Fig. 2 B). Interactive bubble plots display the relationship between various functional states and KNL1 in 11 cancer types. We investigated the association between KNL1 expression in LUAD and various functional states. KNL1 expression correlated negatively with inflammation (r = -0.379, p = 0) and positively with cell cycle (r = 0.725, p = 0), DNA damage (r = 0.589, p = 0), DNA repair (r = 0.536, p = 0), and proliferation (r = 0.652, p = 0). 2. KNL1 expression and survival analysis in lung cancer We examined KNL1's expression pattern and evaluated its prognostic significance in lung adenocarcinoma since the TCGA data suggested that KNL1 might be important in the aetiology of the malignancy. Survival analysis was carried out by merging survival data with KNL1 expression data. Using the median cut-off value, patients with lung adenocarcinoma were categorized into groups with high and low KNL1 expression. The OS of the low-expression group was found to be substantially longer compared to that of the high-expression group (p < 0.001), as per the findings of the survival analysis(Fig. 3 A). For the KNL1 expression group, the AUC was 0.6522, 0.638, and 0.5829 for the 1-, 3-, and 5-year OS, respectively(Fig. 3 B). 3. Analysis of functional enrichment and relationships between proteins We carried out functional enrichment and built protein interaction networks to elucidate the underlying molecular pathways through which KNL1 contributes to tumor progression in lung adenocarcinoma. GO enrichment, KEGG pathway analysis and GSEA enrichment were performed using DEG. There were 596 enriched terms in the GO enrichment study results; the top 10 significant entries for cellular components (CC), molecular functions (MF), and biological processes (BP) were displayed individually(Figure 4 A). DEG's biological functions were primarily linked to nuclear division, nuclear chromosome segregation, mitotic sister chromatid segregation, nuclear division, regulation of mitosis, receptor-ligand activity, organic acid binding, serine-type peptidase activity, and hormone activity. It was discovered that DEG was aberrantly expressed primarily in the collagen-containing extracellular matrix, the mitotic zone, and kinetochore. The KEGG enrichment results' top 20 most essential entries are displayed(Fig. 4 B). The cAMP signalling pathways, cytochrome P450 metabolism of exogenous medicines, chemical oncogene receptors, motor proteins, and the cell cycle are among the numerous signalling pathways that impact the enrichment results. Furthermore, GSEA functional enrichment analysis demonstrated the association between DEG and numerous significant gene sets, including vascular morphogenesis, endothelial cell movement, endocytosis, and receptor internalisation. (Fig. 4 C) Notably, the three enrichment results combined imply that differential genes in LUAD may impact the cell cycle and several signalling pathways, impacting the proliferation and migration of tumour cells. We created a PPI by entering the collection of genes derived from the univariate COX regression into the STRING database to learn more about which proteins can interact with KNL1. Most other genes can interact with KNL1(Fig. 4 D). 4. Infiltration of immunity Six distinct algorithms were utilized to infer immune cell infiltration patterns utilizing RNA-seq data sourced from TCGA-LUAD, we examined the variations in immune cell infiltration between KNL1 highland expression groups(Fig. 5 ). The findings demonstrated that several immune cells, including CD8 + T cells, natural killer cells, and monocytes, had increased levels of infiltration in the KNL1 high-expression group. Nonetheless, in the KNL1 low expression group, dendritic cells and CD4 + T cells showed more significant degrees of infiltration. Happily, this result is consistent with the functional enrichment results. Another interesting difference in stromal cell infiltration was that fibroblasts and endothelial cells showed higher infiltration in the KNL1 low-expression group. The findings imply that the association between KNL and the invasion of various immune cells may indicate a changed tumor microenvironment. . 5. KNL1 expression and immunological checkpoint-related gene expression are correlated Immune checkpoint inhibitors are a crucial part of immunotherapy, which targets critical molecules in immune checkpoints like CTLA4, PD-1, and PDL-1 to kill tumor cells while also controlling the immune cell population in the tumor microenvironment. Figure 6A shows notable expression variations between the high and low KNL1 expression groups for target genes connected to immune checkpoint inhibitors. Subsequently, a strong association was observed between the expression of target genes relevant to immune checkpoint inhibitors and KNL1 expression in lung cancer, as determined by utilizing the GEPIA2 database(Fig. 6B). Based on these findings, the therapeutic impact of immune checkpoint inhibitors was predicted. The TCIA online website was employed to obtain the Immunophenotypic SCORE of the TCGA-LUAD cohort. These scores were used to indicate the difference in therapeutic efficacy between the high and low KNL1 expression groups for the two commonly used immunosuppressants, PD-1 and CTLA4 inhibitors, and that the CTLA4 inhibitor would be more effective(Fig. 6C). 6. KNL1 and drug sensitivity correlation analysis in LUAD Medication sensitivity prediction using the "oncoPredict" R package was used to predict patient responsiveness to small molecule medication therapy. Utilizing GDSC V2 data as the training set, a model was developed to explore the correlation between KNL1 expression levels and commonly administered treatments for lung cancer, such as erlotinib, paclitaxel, and cisplatin. In the KNL1 high-expression group, all three medications had reduced IC50 values, which shows us that these medications may work better therapeutically in KNL1 high-expression patients(Fig. 7 ). In addition, it was discovered that the KNL1 low expression group had reduced IC50 values for the three small molecule medications AZD2014, Uprosertib, and BMS-754807(Fig. 7 ). We looked for six small molecule chemotherapeutic drugs that might be used to treat LUAD based on KNL1 expression level. Discussion One of the most critical proteins in mitosis is KNL1, which is engaged in the spindle checkpoint (SAC) and is found at the mitophagy. [ 23 ] Interaction with microtubules controls how mitophagy attaches to spindle fibres and performs a crucial regulatory role in mitosis. In order to prevent aberrant chromosomal numbers and inconsistent chromosome segregation, the spindle checkpoint is a crucial intracellular regulatory mechanism. [ 24 ] Because of these properties, the KNL1 protein is essential for cell division. Therefore, we have conducted pertinent research and suspect KNL1 has significance for LUAD. Only a few number of research have shown a relationship between KNL1 and the prognosis of cancer thus far. Using clinical samples, the TCGA and GEO databases, and their research, He et al. investigated the role KNL1 plays in uterine endometrial cancer (UCEC). [ 25 ] They discovered that KNL1 is a biomarker linked to immune infiltration in UCEC that is both prognostic and diagnostic. In a similar vein, Bai et al. discovered that KNL1 was significantly expressed in colorectal cancer (CRC) and that it may boost tumour cell proliferation. [ 26 ] Later, using RNA pull-down and immunoprecipitation assays, additional researchers discovered that lncRNA AC125257.1 controlled KNL1's function in CRC. [ 14 ] KNL1 was also found to be significantly expressed in LUAD, and it was discovered through several databases that KNL1 targets miR-139-5p to enhance the growth of LUAD cells. [ 27 ] Nevertheless, the tumor microenvironment linked to these alterations and KNL1 mutations, which are similarly important for the prognosis and advancement of tumours, were not the subject of this investigation. Initially, we conducted a pan-cancer investigation and discovered that KNL1 was expressed differently in a range of prevalent malignancies in line with other research. Thus, we proposed that KNL1 is crucial for supporting the formation and growth of LUAD and conducted a thorough bioinformatics investigation to support our hypothesis. First, KNL1 survival analyses were performed; K-M survival curves revealed that patients in the KNL1 low-expression group had superior survival. The AUCs for the 1-year and 3-year survival were then found to be greater than 0.6 by the time-dependent ROC curves. These findings imply that the KNL1 gene may be an independent predictor of prognosis for LUAD patients. We kept doing functional enrichment analysis to learn more about how KNL1 influences the survival rate of LUAD patients. The results of GO enrichment analysis indicated that DEG's function was primarily related to nuclear division and sister chromatid segregation. In contrast, the results of the KEGG pathway analysis indicated that DEG was connected to the cAMP signalling pathway. cAMP modulates the transcription of several target genes to control the growth, migration, invasion, and metabolism of various tumour cells. [ 28 ] Vascular endothelial cell migration and vascular morphogenesis were demonstrated by GSEA enrichment. These findings imply that KNL1 is involved in LUAD and regulates angiogenesis to stimulate LUAD migration and proliferation. The tumour microenvironment comprises immune cells, inflammatory agents, cytokines, and other molecules that interact with tumour cells and directly impact tumour formation. We concentrated on CD8 + T cells, natural killer cells, and monocytes that displayed increased levels of infiltration in the KNL1 high expression group using the six algorithms provided in the "IOBR" R package. Furthermore, the KNL1 low expression group exhibited increased levels of infiltration by dendritic cells and CD4 + T cells. An essential part of the tumor microenvironment is played by CD4 + T, CD8 + T, and NK cells, which is critical to the efficacy of immunotherapy. Among the most significant anti-tumor effector cells in the immunotherapy process are CD8 + T cells, which can kill tumor cells directly. [ 29 , 30 ] [30, 31] Additionally, more recent proof exists that CD4 + T cells have anti-tumor properties. [ 31 ] Immune checkpoint inhibitor efficacy predictions revealed that CTLA4 inhibitors had superior therapeutic benefits in the KNL1 low-expression group. These findings imply that KNL1 can control the immunological milieu, which could lead to the development of novel targeted medications for the immunotherapy of specific tumors and help many cancer patients. Finally, we hope to find effective small-molecule drugs using the "oncoPredict" R package. The analysis indicate that there is a notable variance in the IC50 values of drugs commonly used in lung cancer treatment. such as eribaltaxel, paclitaxel, and cisplatin, between the groups with high and low KNL1 expression. This implies that selecting medications based on individuals' KNL1 expression levels might be a fair drug selection method. Our experimental findings offer a novel approach and medication selection strategie for clinical drug delivery. Overall, our comprehensive study showed that KNL1 can stimulate the incidence and progression of LUAD. These findings broaden immunotherapy's scope and offer suggestions for future possibilities in research and clinical treatment approaches. Even while research using the TCGA database, which is accessible to the public, is valuable, additional experimental investigations are necessary to follow up and obtain a better understanding of the role KNL1 plays in LUAD. Declarations concise and informative title : Bioinformatics study of KNL1 in LUAD Conflict of interest statement for all authors :The authors have no potential conflicts of interests. Authors’ Contributions: YiRan Dong: Writing – review & editing, Writing – original draft, Validation, Conceptualization. Ting Wu: Visualization, Methodology. JaYang Chen : Visualization, Methodology. Liang Mo: Visualization, Validation, Supervision, Funding acquisition, Conceptualization. Yong You: Visualization, Validation, Supervision, Funding acquisition, Conceptualization. Funding: The authors are grateful for the financial support provided by Hunan Provincial Natural Science Foundation (2021JJ30618, 2022JJ50158) and Hunan Provincial Clinical Medical Technology Innovation Guidance Program (2020SK51825). Limitations section ： We used primary data from 424 TCGA-LUAD samples, but we did not conduct additional experiments for validation. Acknowledgement We acknowledge our utilization of R software and Cytoscape software. The findings partly rely on data obtained from TCGA, CancerSea, String, and GDSC2 databases. We express gratitude to the platforms and the researchers who contributed their data. DATA AVAILABILITY STATEMENT The data generated in this study are available upon request from the corresponding author. References Siegel RL, Miller KD, Fuchs HE, 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-4379762","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305056419,"identity":"3e7535e5-0bd8-4961-bb83-bb6ee8711edd","order_by":0,"name":"YiRan Dong","email":"","orcid":"","institution":"University of South China Hengyang Medical School","correspondingAuthor":false,"prefix":"","firstName":"YiRan","middleName":"","lastName":"Dong","suffix":""},{"id":305056420,"identity":"d1e1d9c3-3a70-44ae-8499-72e3c62c9c8b","order_by":1,"name":"Ting Wu","email":"","orcid":"","institution":"University of South China Hengyang Medical 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You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACfmbGxgcJBhJy9vcfH4AIHSCgRbK9+bDBhwoLY4YDaQnEaTE4cyxNcMaZisSGAzkGxGlhuJFjxszbJpHY2HDmm+SPGgY5vhsJjJ8L8OhgnJFj9hioxbiZsXebNM8xBmPJGwnM0jPwaGGWyDE3BmqRbWPm3SbN2MCQuOFGAhszDx4tbBI5ZtJALYw9bDzPJH82MNQT1MLDcyxNcsYZCcUZPDxsErwNDAkGhLRIsIMDWcLYQILN2JrnmIThzDMPm6XxabE/DI7KOjkDCeaHN3/U2MjzHU8++BmfFgxbgRgYCKNgFIyCUTAKKAMAIfZK1lbV7BgAAAAASUVORK5CYII=","orcid":"","institution":"University of South China Hengyang Medical School","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"You","suffix":""}],"badges":[],"createdAt":"2024-05-07 02:45:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4379762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4379762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57448602,"identity":"47ec50fc-1ad1-48c7-9811-9de97a8c86e5","added_by":"auto","created_at":"2024-05-30 19:59:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":439369,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed workflow of this experiment\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/97cc87b6eb7fffdb2d6f09e7.jpeg"},{"id":57448601,"identity":"d39ed917-34e7-4bef-aa61-8ddb1e7cb034","added_by":"auto","created_at":"2024-05-30 19:59:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":780096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eDifferential expression of KNL1 across different cancer types based on pan-cancer analysis.\u003cstrong\u003eB \u003c/strong\u003eTime dependent ROC curves verifying the predictive efficiency of the KNL1 expression. \u003cstrong\u003eC \u003c/strong\u003eFunctional states of KNL1 and its association with 11 different types of cancers in the CancerSEA database.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/065cd691e0eac63812355188.jpeg"},{"id":57448603,"identity":"3196aec6-1fc6-4c01-b506-39b917316b79","added_by":"auto","created_at":"2024-05-30 19:59:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eK-M survival curves based on KNL1\u003cstrong\u003e B T\u003c/strong\u003eime-dependent ROC curves\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/75b7c982b1a4c7779a556d40.jpeg"},{"id":57448605,"identity":"f04a34ac-761e-45a0-829d-5245ec53a192","added_by":"auto","created_at":"2024-05-30 19:59:00","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":348752,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of related genes, pathways and cellular functions of KNL1.\u003cstrong\u003eA\u003c/strong\u003ethe top 10 significant entries for CC, MF, and BP of DEGs. \u003cstrong\u003eB \u003c/strong\u003eTop ten KEGG pathway enrichment analysis of DEGs. \u003cstrong\u003eC \u003c/strong\u003eTop twelve GSEA enrichment analysis of DEGs\u003cstrong\u003e ,\u003c/strong\u003e arranged by the absolute value of NES.\u003cstrong\u003e D \u003c/strong\u003ePPI of gene , obtained from univariate COX regression.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/5b3fabe46f8038bd3257c96b.jpeg"},{"id":57448604,"identity":"cbbbbb6a-6860-4adf-be53-ad7cf0bb07c4","added_by":"auto","created_at":"2024-05-30 19:59:00","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4198110,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation into the relationship between immune cell infiltration and KNL1 expression in lung cancer. Orange color denotes the KNL1 low expression group and purple the KNL1 high expression group.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/711a094d9210641eda714e84.jpeg"},{"id":57448606,"identity":"ef085086-a9af-435e-8550-2e9911426d36","added_by":"auto","created_at":"2024-05-30 19:59:00","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2674798,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Checkpoint Inhibitor Efficaciousness Prediction \u003cstrong\u003eA\u003c/strong\u003eIn the TCGA-LUAD cohort three significant immune checkpoint genes (PD-1, PDL-1, and CTLA4) displayed differences between high and low KNL1 expression groups. \u003cstrong\u003eB\u003c/strong\u003eThe expression of KNL1 and the expression of the three critical immune checkpoint genes in LUAD \u003cstrong\u003eC\u003c/strong\u003e Immune Checkpoint Inhibitor Efficacy were correlated, according to the GEPIA website. Disparity between groups with high and low KNL1 expression\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/1aca4197d64f301d6218120f.jpeg"},{"id":57448607,"identity":"7a6e3140-00ba-4613-ab9b-f8f8c96d26ba","added_by":"auto","created_at":"2024-05-30 19:59:01","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":53824,"visible":true,"origin":"","legend":"\u003cp\u003eSix small molecule chemotherapeutic drugs that might be used to treat LUAD based on KNL1 expression level, obtained from \"oncoPredict\" R package.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/3b70233e83c74b4c2b8098e4.jpeg"},{"id":64822618,"identity":"42cd7466-74f7-41b4-a2f4-6e5a523d588c","added_by":"auto","created_at":"2024-09-19 08:01:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9048455,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4379762/v1/b6496923-e131-4d42-8dd9-8f88789ca32e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"KNL1 is a lung cancer prognostic biomarker associated with the immune microenvironment","fulltext":[{"header":"Key Points","content":"\u003cp\u003e1. Significant finding: Elevated KNL1 expression correlates with poor prognosis and higher immune cell infiltration in lung adenocarcinoma (LUAD).\u003c/p\u003e\n\u003cp\u003e2. Research contribution: Identifies KNL1 as a potential biomarker and therapeutic target for LUAD, enhancing understanding of its tumor microenvironment and immunotherapy potential.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGlobally, lung cancer stands out as the most frequently diagnosed and deadliest malignancy, with lung adenocarcinoma (LUAD) representing its predominant subtype\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Before immunotherapy was approved, lung cancer without driver mutations was often treated with cytotoxic chemotherapy; lung adenocarcinomas with driver mutations (EGFR, for example) were typically treated with targeted therapy.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e Immunotherapy has significantly increased patient survival and transformed the paradigm of tumour treatment.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e Nevertheless, patients are less likely to have long-term improvements from immunotherapy or targeted therapy.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e Therefore, finding novel targets or creating fresh immunotherapy regimens that can be applied to treating LUAD patients is essential.\u003c/p\u003e \u003cp\u003eKinetochore scaffold 1 (KNL1,alternatively referred to as CASC5) is a crucial protein that controls kinetochore assembly and spindle formation. It combines with other proteins to produce complexes that support and preserve centromere structures.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e Maintaining appropriate chromosome segregation simultaneously modifies the contact between the kinetochore and spindle microtubules through regulatory mechanisms such as phosphorylation and dephosphorylation.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e Furthermore, to maintain the precision and stability of cellular mitosis, KNL1 observes errors and initiates spindle assembly checkpoints.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e This gene may be crucial in immunotherapy and is linked to the clinical prognosis of individuals with prostate cancer.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e According to bioinformatics research, KNL1 is significantly expressed in gastric cancer and plays a pivotal role in the tumour development.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e The research findings indicates that KNL1 is a significant predictor of prognosis in LUAD and is linked to the miR-139-5p target.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e Unfortunately, this research did not thoroughly examine the function of KNL1 expression in LUAD.\u003c/p\u003e \u003cp\u003ePrimarily relying on data from the TCGA repository, we thoroughly examined the differential expression of KNL1 in LUAD in this study. The prognosis of patients and the infiltration of immune cells were correlated with KNL1 expression, and the functional enrichment and protein interactions network revealed possible signalling pathways of KNL1 associated with tumour cell proliferation. Ultimately, the drug sensitivity prediction findings revealed that various medications had IC50 values that varied between high and low KNL1 expression. Our meticulous analysis of KNL1's role in LUAD provides new targets for LUAD treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the comprehensive workflow of the study. The UCSC Xena database provided the mRNA expression profiles, survival statistics, mutation data, and other clinical information of the TCGA-LUAD cohort used in this study. We also utilised the Genotype-Tissue Expression database for pan-cancer analysis. KNL1's impact on pan-cancer biological behaviour was examined using CancerSea (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocc.hrbmu.edu.cn/CancerSEA/home\u003c/span\u003e\u003cspan address=\"http://biocc.hrbmu.edu.cn/CancerSEA/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. jsp). In order to confirm consistency, we transformed the batch pair mRNA sequencing data using log2(x\u0026thinsp;+\u0026thinsp;1). Survival data were employed for survival analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying the genes that are expressed differently in lung cancer and survival analysis\u003c/h2\u003e \u003cp\u003eUsing the 'DESeq2' R package, differentially expressed genes were found in 510 LUAD samples and 58 normal samples, with cut-off values of P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2 fold change (FC)| \u0026gt; 2.5. The R programme 'ggplot' was applied to generate box plots representing the DEGs. The association between the mRNA level of KNL1 and the overall survival (OS) of pancreatic cancer patients in TCGA was examined using Kaplan-Meier analysis and the log-rank test; survival curves with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were displayed. In addition, we built and plotted time-dependent ROC curves to analyse KNL1's predictive performance at 1, 3, and 5 years in order to establish whether it may be created as a prognostic indicator for patients. The main evaluation parameter chosen was the the area under the curve (AUC) value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInteractions between proteins and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eAfter that, functional enrichment analysis was carried out for DEG. Molecular function (MF), cellular composition (CC), and biological process (BP) were all included in the gene ontology (GO) enrichment study. Signalling pathways in DEGs were analysed using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) functional enrichment and gene set enrichment analysis (GSEA). 'clusterProfiler' and 'ggplot2' R packages were used for analysis and visualisation. Furthermore, using the Cytoscape programme (Cytoscape, 3.10.1) and the String database, Protein-Protein Interaction Networks (PPI) with an interaction score\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were mapped.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Immune Infiltration and Forecasting the Effectiveness of Immune Checkpoint Inhibitor Treatment\u003c/h2\u003e \u003cp\u003eSix algorithms\u0026mdash;CIBERSORT\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, xCell\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, MCPcounter\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, ESTIMATE\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, EPIC\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, and quanTIseq\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;included in the \"IOBR\" R package\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e were used to analyze KNL1's immune infiltration. The groups with high and low expression levels of KNL1 were evaluated for differences in immune cell infiltration. Using the GEPIA2 website, the connection between KNL1 and immune checkpoint-associated genes was examined. Then, the TCIA website was utilized to acquire the IPS of patients with TCGA-LUAD, which allowed for predicting the patient's response to PDL-1 and CLAT-4 inhibitors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of drug sensitivity\u003c/h2\u003e \u003cp\u003eMaeser et al. created the \"oncopredict\" R programme to forecast cancer patients' medication response.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e Using training data from the GDSC2 database and oncoPredict, a predictive model for drug response was developed utilizing gene expression profiling data from the TCGA LUAD cohort.. The predictive model mostly uses the default values.(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.rproject.org/web/packages/oncoPredict/oncoPredict.pdf\u003c/span\u003e\u003cspan address=\"https://cran.rproject.org/web/packages/oncoPredict/oncoPredict.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) The sensitivity of the medicinal products between the high and low KNL1 expression groups was evaluated using an unpaired t-test, resulting in a total of 128 medications. A p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance level was applied.\u003c/p\u003e"},{"header":"Results","content":"\u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1. Expression and function of KNL1 in pan-cancer\u003c/h2\u003e \u003cp\u003eFirst, we examined the KNL1 gene expression in pan-cancer by utilizing the combined GTEx and TCGA databases. The findings revealed that the mRNA level of KNL1 varied significantly among 25 malignancies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When compared to adjacent normal tissue adjacent to the cancer, the mRNA level of KNL1 was highly expressed in 22 tumours and lowly expressed in 3 cancers; nevertheless. But KNL1 did not exhibit a distinct expression pattern. Using the \"DESeq2\" R programme, TCGA-LUAD data were computed, yielding a total of 1443 differential genes (DEG, LogFc\u0026thinsp;\u0026gt;\u0026thinsp;2.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When LUAD was compared to normal tissue next to the malignancy, KNL1 expression was significantly higher (logFc\u0026thinsp;=\u0026thinsp;2.549604, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This outcome aligned with the combined database's outcomes (Figure). Using the CancerSEA database, p-values and correlation results were obtained to further analyse the functional state of KNL1 in various malignancies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Interactive bubble plots display the relationship between various functional states and KNL1 in 11 cancer types. We investigated the association between KNL1 expression in LUAD and various functional states. KNL1 expression correlated negatively with inflammation (r = -0.379, p\u0026thinsp;=\u0026thinsp;0) and positively with cell cycle (r\u0026thinsp;=\u0026thinsp;0.725, p\u0026thinsp;=\u0026thinsp;0), DNA damage (r\u0026thinsp;=\u0026thinsp;0.589, p\u0026thinsp;=\u0026thinsp;0), DNA repair (r\u0026thinsp;=\u0026thinsp;0.536, p\u0026thinsp;=\u0026thinsp;0), and proliferation (r\u0026thinsp;=\u0026thinsp;0.652, p\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2. KNL1 expression and survival analysis in lung cancer\u003c/h2\u003e \u003cp\u003eWe examined KNL1's expression pattern and evaluated its prognostic significance in lung adenocarcinoma since the TCGA data suggested that KNL1 might be important in the aetiology of the malignancy. Survival analysis was carried out by merging survival data with KNL1 expression data. Using the median cut-off value, patients with lung adenocarcinoma were categorized into groups with high and low KNL1 expression. The OS of the low-expression group was found to be substantially longer compared to that of the high-expression group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as per the findings of the survival analysis(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For the KNL1 expression group, the AUC was 0.6522, 0.638, and 0.5829 for the 1-, 3-, and 5-year OS, respectively(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3. Analysis of functional enrichment and relationships between proteins\u003c/h2\u003e \u003cp\u003eWe carried out functional enrichment and built protein interaction networks to elucidate the underlying molecular pathways through which KNL1 contributes to tumor progression in lung adenocarcinoma. GO enrichment, KEGG pathway analysis and GSEA enrichment were performed using DEG. There were 596 enriched terms in the GO enrichment study results; the top 10 significant entries for cellular components (CC), molecular functions (MF), and biological processes (BP) were displayed individually(Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). DEG's biological functions were primarily linked to nuclear division, nuclear chromosome segregation, mitotic sister chromatid segregation, nuclear division, regulation of mitosis, receptor-ligand activity, organic acid binding, serine-type peptidase activity, and hormone activity. It was discovered that DEG was aberrantly expressed primarily in the collagen-containing extracellular matrix, the mitotic zone, and kinetochore. The KEGG enrichment results' top 20 most essential entries are displayed(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The cAMP signalling pathways, cytochrome P450 metabolism of exogenous medicines, chemical oncogene receptors, motor proteins, and the cell cycle are among the numerous signalling pathways that impact the enrichment results. Furthermore, GSEA functional enrichment analysis demonstrated the association between DEG and numerous significant gene sets, including vascular morphogenesis, endothelial cell movement, endocytosis, and receptor internalisation. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) Notably, the three enrichment results combined imply that differential genes in LUAD may impact the cell cycle and several signalling pathways, impacting the proliferation and migration of tumour cells. We created a PPI by entering the collection of genes derived from the univariate COX regression into the STRING database to learn more about which proteins can interact with KNL1. Most other genes can interact with KNL1(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4. Infiltration of immunity\u003c/h2\u003e \u003cp\u003eSix distinct algorithms were utilized to infer immune cell infiltration patterns utilizing RNA-seq data sourced from TCGA-LUAD, we examined the variations in immune cell infiltration between KNL1 highland expression groups(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The findings demonstrated that several immune cells, including CD8\u0026thinsp;+\u0026thinsp;T cells, natural killer cells, and monocytes, had increased levels of infiltration in the KNL1 high-expression group. Nonetheless, in the KNL1 low expression group, dendritic cells and CD4\u0026thinsp;+\u0026thinsp;T cells showed more significant degrees of infiltration. Happily, this result is consistent with the functional enrichment results. Another interesting difference in stromal cell infiltration was that fibroblasts and endothelial cells showed higher infiltration in the KNL1 low-expression group. The findings imply that the association between KNL and the invasion of various immune cells may indicate a changed tumor microenvironment. .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5. KNL1 expression and immunological checkpoint-related gene expression are correlated\u003c/h2\u003e \u003cp\u003eImmune checkpoint inhibitors are a crucial part of immunotherapy, which targets critical molecules in immune checkpoints like CTLA4, PD-1, and PDL-1 to kill tumor cells while also controlling the immune cell population in the tumor microenvironment. Figure\u0026nbsp;6A shows notable expression variations between the high and low KNL1 expression groups for target genes connected to immune checkpoint inhibitors. Subsequently, a strong association was observed between the expression of target genes relevant to immune checkpoint inhibitors and KNL1 expression in lung cancer, as determined by utilizing the GEPIA2 database(Fig.\u0026nbsp;6B). Based on these findings, the therapeutic impact of immune checkpoint inhibitors was predicted. The TCIA online website was employed to obtain the Immunophenotypic SCORE of the TCGA-LUAD cohort. These scores were used to indicate the difference in therapeutic efficacy between the high and low KNL1 expression groups for the two commonly used immunosuppressants, PD-1 and CTLA4 inhibitors, and that the CTLA4 inhibitor would be more effective(Fig.\u0026nbsp;6C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e6. KNL1 and drug sensitivity correlation analysis in LUAD\u003c/h2\u003e \u003cp\u003eMedication sensitivity prediction using the \"oncoPredict\" R package was used to predict patient responsiveness to small molecule medication therapy. Utilizing GDSC V2 data as the training set, a model was developed to explore the correlation between KNL1 expression levels and commonly administered treatments for lung cancer, such as erlotinib, paclitaxel, and cisplatin. In the KNL1 high-expression group, all three medications had reduced IC50 values, which shows us that these medications may work better therapeutically in KNL1 high-expression patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In addition, it was discovered that the KNL1 low expression group had reduced IC50 values for the three small molecule medications AZD2014, Uprosertib, and BMS-754807(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). We looked for six small molecule chemotherapeutic drugs that might be used to treat LUAD based on KNL1 expression level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOne of the most critical proteins in mitosis is KNL1, which is engaged in the spindle checkpoint (SAC) and is found at the mitophagy.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e Interaction with microtubules controls how mitophagy attaches to spindle fibres and performs a crucial regulatory role in mitosis. In order to prevent aberrant chromosomal numbers and inconsistent chromosome segregation, the spindle checkpoint is a crucial intracellular regulatory mechanism.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e Because of these properties, the KNL1 protein is essential for cell division. Therefore, we have conducted pertinent research and suspect KNL1 has significance for LUAD. Only a few number of research have shown a relationship between KNL1 and the prognosis of cancer thus far. Using clinical samples, the TCGA and GEO databases, and their research, He et al. investigated the role KNL1 plays in uterine endometrial cancer (UCEC).\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e They discovered that KNL1 is a biomarker linked to immune infiltration in UCEC that is both prognostic and diagnostic. In a similar vein, Bai et al. discovered that KNL1 was significantly expressed in colorectal cancer (CRC) and that it may boost tumour cell proliferation.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e Later, using RNA pull-down and immunoprecipitation assays, additional researchers discovered that lncRNA AC125257.1 controlled KNL1's function in CRC.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003eKNL1 was also found to be significantly expressed in LUAD, and it was discovered through several databases that KNL1 targets miR-139-5p to enhance the growth of LUAD cells.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e Nevertheless, the tumor microenvironment linked to these alterations and KNL1 mutations, which are similarly important for the prognosis and advancement of tumours, were not the subject of this investigation.\u003c/p\u003e \u003cp\u003eInitially, we conducted a pan-cancer investigation and discovered that KNL1 was expressed differently in a range of prevalent malignancies in line with other research. Thus, we proposed that KNL1 is crucial for supporting the formation and growth of LUAD and conducted a thorough bioinformatics investigation to support our hypothesis. First, KNL1 survival analyses were performed; K-M survival curves revealed that patients in the KNL1 low-expression group had superior survival. The AUCs for the 1-year and 3-year survival were then found to be greater than 0.6 by the time-dependent ROC curves. These findings imply that the KNL1 gene may be an independent predictor of prognosis for LUAD patients.\u003c/p\u003e \u003cp\u003eWe kept doing functional enrichment analysis to learn more about how KNL1 influences the survival rate of LUAD patients. The results of GO enrichment analysis indicated that DEG's function was primarily related to nuclear division and sister chromatid segregation. In contrast, the results of the KEGG pathway analysis indicated that DEG was connected to the cAMP signalling pathway. cAMP modulates the transcription of several target genes to control the growth, migration, invasion, and metabolism of various tumour cells.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e Vascular endothelial cell migration and vascular morphogenesis were demonstrated by GSEA enrichment. These findings imply that KNL1 is involved in LUAD and regulates angiogenesis to stimulate LUAD migration and proliferation.\u003c/p\u003e \u003cp\u003eThe tumour microenvironment comprises immune cells, inflammatory agents, cytokines, and other molecules that interact with tumour cells and directly impact tumour formation. We concentrated on CD8\u0026thinsp;+\u0026thinsp;T cells, natural killer cells, and monocytes that displayed increased levels of infiltration in the KNL1 high expression group using the six algorithms provided in the \"IOBR\" R package. Furthermore, the KNL1 low expression group exhibited increased levels of infiltration by dendritic cells and CD4\u0026thinsp;+\u0026thinsp;T cells. An essential part of the tumor microenvironment is played by CD4\u0026thinsp;+\u0026thinsp;T, CD8\u0026thinsp;+\u0026thinsp;T, and NK cells, which is critical to the efficacy of immunotherapy. Among the most significant anti-tumor effector cells in the immunotherapy process are CD8\u0026thinsp;+\u0026thinsp;T cells, which can kill tumor cells directly.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e [30, 31] Additionally, more recent proof exists that CD4\u0026thinsp;+\u0026thinsp;T cells have anti-tumor properties.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e Immune checkpoint inhibitor efficacy predictions revealed that CTLA4 inhibitors had superior therapeutic benefits in the KNL1 low-expression group. These findings imply that KNL1 can control the immunological milieu, which could lead to the development of novel targeted medications for the immunotherapy of specific tumors and help many cancer patients.\u003c/p\u003e \u003cp\u003eFinally, we hope to find effective small-molecule drugs using the \"oncoPredict\" R package. The analysis indicate that there is a notable variance in the IC50 values of drugs commonly used in lung cancer treatment. such as eribaltaxel, paclitaxel, and cisplatin, between the groups with high and low KNL1 expression. This implies that selecting medications based on individuals' KNL1 expression levels might be a fair drug selection method. Our experimental findings offer a novel approach and medication selection strategie for clinical drug delivery.\u003c/p\u003e \u003cp\u003eOverall, our comprehensive study showed that KNL1 can stimulate the incidence and progression of LUAD. These findings broaden immunotherapy's scope and offer suggestions for future possibilities in research and clinical treatment approaches. Even while research using the TCGA database, which is accessible to the public, is valuable, additional experimental investigations are necessary to follow up and obtain a better understanding of the role KNL1 plays in LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003econcise and informative title\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eBioinformatics study of KNL1 in LUAD\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement for all authors\u003c/strong\u003e:The authors have no potential conflicts of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYiRan Dong:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Validation, Conceptualization. \u003cstrong\u003eTing Wu:\u003c/strong\u003e Visualization, Methodology. \u003cstrong\u003eJaYang\u003c/strong\u003e\u003cstrong\u003eChen\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Visualization, Methodology. \u003cstrong\u003eLiang Mo:\u003c/strong\u003e Visualization, Validation, Supervision, Funding acquisition, Conceptualization. \u003cstrong\u003eYong You:\u003c/strong\u003e Visualization, Validation, Supervision, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors are grateful for the financial support provided by Hunan Provincial Natural Science Foundation (2021JJ30618, 2022JJ50158) and Hunan Provincial Clinical Medical Technology Innovation Guidance Program (2020SK51825).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations section\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWe used primary data from 424 TCGA-LUAD samples, but we did not conduct additional experiments for validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge our utilization of R software and Cytoscape software. The findings partly rely on data obtained from TCGA, CancerSea, String, and GDSC2 databases. We express gratitude to the platforms and the researchers who contributed their data.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e \u003cp\u003eThe data generated in this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022[J]. Cancer J Clin. 2022;72(1):7\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21708\u003c/span\u003e\u003cspan address=\"10.3322/caac.21708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTravis WD, Brambilla E, Nicholson AG, et al. 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Immunity. 2021;54(12):2701\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2021.11.015\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2021.11.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"TCGA, lung adenocarcinoma, KNL1, tumour microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4379762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4379762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: Kinetochore scaffold 1 (KNL1) plays a crucial role in cell cycle regulation and is implicated in lung adenocarcinoma (LUAD) progression, especially in the tumor microenvironment and immunotherapy. Our study aims to investigate KNL1's potential as a therapeutic target for LUAD.\nMethods: We conducted pan-cancer analysis of KNL1 expression using the CancerSea database and performed survival analysis. Functional studies included GO, KEGG, and GESA analyses, as well as protein interaction network construction. Immune infiltration analysis was conducted using six algorithms from the \"IOBR\" R package. Therapeutic effects of immune checkpoint inhibitors were predicted using the TIDE and TCIA databases, and drug responses were forecasted using the \"Oncopredict\" R package.\nResults: KNL1 was significantly expressed across 22 malignancies, including LUAD, and correlated with worse prognosis. Immune infiltration analysis revealed associations between KNL1 expression and various immune cell types. Higher KNL1 expression was associated with increased susceptibility to CTLA4 inhibitors. Drug prediction suggested potential treatments for LUAD patients with high KNL1 expression.\nConclusion: Our findings suggest KNL1 as a potential therapeutic target for LUAD, particularly in immunotherapy, making it a valuable biomarker for treatment strategies in this cancer type.","manuscriptTitle":"KNL1 is a lung cancer prognostic biomarker associated with the immune microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 19:58:55","doi":"10.21203/rs.3.rs-4379762/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":"817b192c-3828-458a-a24b-c91713700d24","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-19T07:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 19:58:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4379762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4379762","identity":"rs-4379762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}