The role of ALK gene and PI3k/Akt/NF - κ B signaling pathway in precancerous lesions of cervical cancer

The role of ALK gene and PI3k/Akt/NF - κ B signaling pathway in precancerous lesions of cervical cancer | 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 The role of ALK gene and PI3k/Akt/NF - κ B signaling pathway in precancerous lesions of cervical cancer Ding Qi, Yiming Sun, Wenxia Ai, Buwei Han, Mingge Liang, Mingshu Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal.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-4939442/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study aimed to unravel the molecular basis of cervical precancerous lesions leveraging bioinformatic tools to pinpoint crucial genes and signaling cascades. A multi-faceted approach was undertaken, commencing with GEO database mining for differential gene expression between CSILs and healthy cervical tissues. STRING 11.0 facilitated protein-protein interaction (PPI) analysis, generating a network visualized in Cytoscape 3.7.2. Gene Ontology (GO) and KEGG pathway enrichment via DAVID illuminated biological functions and pathways associated with identified differentially expressed genes (DEGs). GSEA further refined key genes and enriched modules. Concurrently, qRT-PCR validation on cervical biopsy samples from eligible patients corroborated bioinformatic findings. The analysis pinpointed 371 common DEGs across datasets, leading to the discovery of 102 biological processes, 33 cellular components, 15 molecular functions, 29 significant pathways, and 3 pivotal genes. Clinical assessment linked lesion severity to age, vaginal microbiota characteristics, and ALK gene/PI3K/AKT/NF-κB pathway activity. qRT-PCR verified heightened ALK and PI3K/AKT/NF-κB signaling in high-grade lesions, underscoring their roles in CSIL pathogenesis. The importance of this research lies in its potential to inform the development of targeted therapies and personalized treatment strategies for cervical precancerous lesions. By identifying the molecular drivers of the disease, researchers can design interventions that precisely target these pathways, improving patient outcomes and reducing the burden of cervical cancer. Cervical precancerous lesions ALK PI3k/Akt/NF-κB Prognosis and Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Cervical precancerous lesions, also known as cervical intraepithelial neoplasia (CIN), are closely related to invasive cervical cancer. According to pathological grading, CIN can be classified into CIN I, CIN II, and CIN III. Indeed, cervical precancerous lesions (CIN) represent a critical stage in the development of invasive cervical cancer, and their classification into low-grade (LSIL) and high-grade (HSIL) based on factors like p16 expression provides valuable insights into disease progression 1 . The transformation from normal cervical epithelium to precancerous lesions and ultimately to invasive cancer is a complex, multi-step process involving alterations in multiple genes and signaling pathways 2 . While high-risk human papillomavirus (HPV) infection is a well-established primary cause of cervical cancer 3 , it is important to note that not all HPV-infected individuals will develop the disease. This suggests that additional genetic and environmental factors contribute significantly to the progression of cervical precancerous lesions to invasive cancer 4 . In recent years, research on the relationship between genes and diseases has been of great significance in exploring the pathogenesis of diseases 5 . In related studies on cervical cancer, it has been pointed out that related genes and pathways play a role in the disease. For example, the PTEN gene, as an important tumor suppressor gene, is highly expressed in normal cervical tissue and crucial for maintaining the balance between cell proliferation and apoptosis 6 . However, in precancerous lesions and cervical cancer tissues, PTEN expression is often downregulated or absent 7 , leading to a weakened inhibitory effect on the PI3K/Akt pathway, which in turn promotes abnormal cell proliferation and tumorigenesis 8 . The TGF - β signaling pathway plays an important role in the occurrence and development of tumors, and the TGFBR2 gene is one of the core genes of this pathway. The loss or decrease of TGFBR2 expression is believed to promote the occurrence and progression of tumors 9 . The eEF1A2 gene is closely associated with the occurrence and development of various tumors, including ovarian cancer 10 , liver cancer 11 , and prostate cancer 12 . Although there is limited research on the direct role of the eEF1A2 gene in cervical cancer, previous studies have suggested that it may play a role in the development of cervical cancer by inhibiting cell apoptosis and promoting cancer cell expansion 13 . The transmission of information through signaling pathways from the outside to the inside of cells can have an impact on diseases. The cGAS STING signaling pathway plays a crucial role in the immune response triggered by viral infection. Studies have shown that abnormal activation of the cGAS STING signaling pathway may be associated with susceptibility to cervical precancerous lesions 14 . At present, relevant research has revealed that the molecular mechanisms underlying the induction of HSIL are multi-layered, involving multiple biological processes, and the layer-to-layer signaling pathways associated with HSIL are also diverse 15 . However, there are relatively few genetic reports on the specificity and specificity of HSIL. Therefore, this study aims to use bioinformatics methods and high-throughput sequencing data to screen genes and pathways closely related to HSIL, find more specific and sensitive indicators, and enrich genetic research on this disease. This research will help to develop more sensitive and specific early diagnostic markers, improve the early detection rate of cervical cancer, achieve early intervention and treatment, and reduce the incidence rate and mortality of cervical cancer. This study focuses on the role of genes and signaling pathways in cervical precancerous lesions, aiming to provide a new theoretical basis and potential targets for early diagnosis, prevention, and treatment of cervical cancer through in-depth molecular mechanism research. It has important scientific significance and clinical application value. 2. Results 2.1. Bioinformatics analysis results 2.1.1 Determination of Dataset A search was conducted in the GEO database using the terms “cervical intraepithelial neoplasia”, “cervical HSIL”, or “cervical high-grade intraepithelial lesion”, resulting in 588 eligible datasets. After inspection and comparison, the GSE63514 and GSE75132 datasets were finally selected for comparison after data correction. Both datasets were detected using high-throughput sequencing on the GPL570 platform, with a detectable nucleotide (nt) length of 600. The Affymetrix Human Genome U133 Plus 2.0 array was used to fully cover the U133 set of the human genome, as well as analyze 6500 additional genes and over 47000 transcripts. The sensitivity and accuracy of the detection were high 16 . The GSE63514 dataset contains a total of 128 sequencing samples, including tissue sample data from 24 normal patients, 14 cervical CIN I, 22 cervical CIN II, 40 cervical CIN III, and 28 cervical cancer patients. The GSE75132 dataset includes 41 samples, including 11 normal samples, 10 patients with persistent HPV16 infection, 4 cervical CIN II, 9 cervical CIN III, and 7 cervical cancer samples. 2.1.2 GEO 2R processing results The basic information analysis of the two datasets is as follows: In the GSE75132 dataset, the data was first adjusted for baseline alignment, then compared and analyzed, and volcano plots (Fig. 2-1A) and scatter plots (Fig. 2-1B) were plotted for this dataset. Finally, 2266 differentially expressed genes were identified, of which 951 genes were significantly upregulated and 1315 genes were significantly downregulated (Fig. 2-1C). In the GSE63514 dataset, there are a total of 2208 differentially expressed genes between normal tissues and high-grade lesion tissues (Fig. 2-1D, Fig. 2-1E), of which 1203 genes are significantly downregulated and 1005 genes are significantly up-regulated (Fig. 2-1F). Figure 2 − 1 Gene expression in two datasets A. GSE75132 Volcano Map, B. GSE75132 Scatter plot, C. GSE75132 heat map, D. GSE63514 Volcano Map, E. GSE63514 scatter plot, F. GSE63514 Heat Map 2.1.3 Screening of differentially expressed genes Upload differentially expressed genes from two datasets separately to the website( https://bioinfogp.cnb.csic.es/tools/venny/index.html)Dra w a Venny plot to obtain 371 differentially expressed genes, as shown in Fig. 2–2. After analyzing and calculating 371 differentially expressed genes, it was found that there were 221 downregulated genes and 150 upregulated genes. Figure 2–2 Venny plot 2.1.4 PPI network analysis results Download the results of STRING database analysis and use Cytoscape 3.9.1 software to construct a PPI interaction network (Fig. 2-3A) with 288 nodes and 1274 edges. The top 10 key genes were calculated, including vascular endothelial growth factor A (VEGFA), matrix metalloproteinase-9 (MMP9), hyaluronic acid receptor protein CD44 (CD44), cyclin D1 (CCND1), cyclin B1 (CCNB1), serum chemokine ligand 8 (CXCL8), estrogen receptor 1 (ESR1), and Toll-like receptor 2 (Toll-like receptor 2). Like receptor 2 (TLR2), CXC chemokine receptor 4 (CXCR4), and signal transducer and activator of transcription 1 (STAT1), the results are shown in Fig. 2-3B. The average degree value of the PPI network is 7.26 (Fig. 2-3C), and the correlation between genes is good (Fig. 2-3D). Figure 2–3 PPI Network and Cytoscape Analysis A. PPI network diagram of genes, B. The top 10 key genes in terms of degree ranking, C. Degree distribution map of differentially co-expressed genes, D. Degree correlation analysis of differentially co-expressed genes 2.1.5 GO analysis Through the analysis of differentially expressed genes, 102 key biological processes (BP), 33 cellular components (CC), and 15 molecular functions (MF) were identified. The top 10 items from each group were selected to draw GO analysis graphs, as shown in Fig. 2–4. After analysis, it was found that the biological processes involved in this disease include inflammatory response, cellular response to interferon - γ, immune response, signal transduction, positive regulation of protein phosphorylation, positive regulation of interleukin-6 production, apoptosis process, and positive regulation of I-kappaB kinase/NF-kapaB signaling. Cellular components are mainly related to cytoplasmic contents and the extracellular environment. Molecular functions involve protein binding, enzyme binding, as well as related factors and protein connections. Figure 2–4 Visual display of GO analysis 3.1.6 KEGG pathway screening Through comparative analysis, a total of 29 signaling pathways were found to be associated with cervical HSIL lesions in this experiment (Fig. 2–5). These include chemokine signaling pathway, Kaposi sarcoma-associated herpesvirus infection, proteoglycan in cancer, cancer pathway, the interaction of the viral protein with cytokines and cytokine receptors, natural killer cell-mediated cytotoxicity, human immunodeficiency virus type 1 infection, human cytomegalovirus infection, PI3K-AKT signaling pathway, Toll-like receptor signaling pathway, cytokine receptor interaction, Ras signaling pathway, nucleotide metabolism, leukocyte transendothelial migration, DNA replication, lipid and atherosclerosis. Figure 2–5 KEGG pathway visualization analysis 2.1.7 GSEA Key Gene Screening Through GSEA enrichment analysis, three genes related to this disease were predicted, namely Serine/threonine kinase 33 (STK33), Ribosomal protein S14 (RPS14), and Anaplastic lymphoma kinase (ALK). The expression levels of these three key genes showed an upward trend in this disease (Fig. 2-6A). The analysis of enriched gene modules showed that the three biological processes most closely related to this disease are the TNF - α signaling pathway mediated by NF - κ B, interferon - γ response, and immune response (Fig. 2-6B). Studies have shown that STK33 is expressed in various tissues, with the strongest hybridization signals observed in the testes, fetal lungs, and heart, followed by the pituitary gland, kidneys, pancreas, heart, thyroid gland, and uterus. It exhibits weak hybridization signals in the aorta, blood system, and digestive system, while no hybridization signals were detected in the nervous system tissues 17 . STK33 has been found to inhibit mitochondrial apoptosis and enhance tumor cell growth vitality by suppressing BAD activity in tumor-related diseases, suggesting that STK33 may be an effective target for combating oncogenic mutations 18 . Through a literature search, it was found that RPS14 can prevent the interaction between MDM2-p53, leading to a significant accumulation of p53. This accumulation can induce cell cycle arrest, DNA repair damage, aging, and apoptosis 19 . RPS14 is a 40S ribosomal subunit component that is associated with red blood cell differentiation and is related to various blood diseases such as myelodysplastic syndrome and 5q syndrome 20 . In a study on colon cancer, it was found that overexpression of RPS14 can activate the PI3K/AKT signaling pathway, enhancing the survival ability of tumor cells 21 . At present, changes in ALK gene recombination, fusion, mutation, amplification, and splicing have been found in various tumor diseases. In addition, ALK can mediate the NF - κ B signaling pathway, and regulate and promote the activation of NLRP3 inflammasome in macrophages. Therefore, ALK targeting may represent a new strategy for treating NLRP3 inflammasome-mediated diseases 22 , 23 . Figure 2–6 GSEA analysis results A. Key gene prediction, B. Prediction of Key Gene Enrichment Module 2.2 Data Analysis 2.2.1 Basic Patient Information Collect medical records of outpatient and inpatient patients. The original total number of collected patients was 77, but due to various reasons, a total of 29 patients were dropped or excluded. This study includes a total of 48 patients. Among them, 38 patients were included in the normal group, of which 16 were excluded due to incomplete information filling, 2 were excluded from previous cervical LEEP treatment, and 1 was excluded from seeking medical treatment at an external hospital. A total of 18 patients were excluded, 1 dropped out, and 19 patients were collected. 17 patients were included in the LSIL group, including 1 case excluded due to abnormal cervical glandular cells and 2 cases excluded due to incomplete data filling. A total of 3 patients were excluded, and 14 patients were collected. 22 patients were included in the HSIL group, of which 3 were excluded due to abnormal cervical glandular cells, 3 were excluded due to incomplete information filling, and 1 was excluded due to a controversial diagnosis from an external hospital (nonregular hospital) based on pathological results. A total of 7 patients were excluded, and 15 patients were collected. 2.2.2 Comparison of Basic Age Information of Patients in Different Groups The age distribution of patients in each group was compared, and the differences were statistically significant (P < 0.01). The age distribution of the control group is mostly concentrated between 40–50 years old, and this group of patients mostly undergo total hysterectomy due to conditions such as adenomyosis and multiple uterine fibroids. The prevalence of LSIL in the age group is mostly concentrated between 30–40 years old and 20–30 years old, with younger onset ages. The age incidence rate of the HSIL group is mostly concentrated between 40–50 years old and 30–40 years old (Table 2 − 1). This study found that the age of onset in the LSIL group was mostly concentrated between 30–40 years old, with a younger onset age, while in the HSIL group, the age of onset was mostly concentrated between 40–50 years old. Statistical calculations showed that age was positively correlated with the severity of the disease, which may be related to the prolonged duration of viral infection. The relationship between age and this disease mainly includes the following points: 1. There are differences in the severity of lesions between different age groups: Chang HK et al. found through research that the peak age range of LSIL onset is 25–29 years old, the peak age range of HSIL onset is 30–34 years old, and the peak age range of cervical cancer onset is 70–74 years old. There are differences in the severity of lesions between different age groups. The older the age, the more severe the disease 24 , while younger patients have higher disease regression and complete remission rates, and lower progression rates 25 . 2. The natural regression rate of SIL varies among different age groups: HSIL is a precancerous disease that may lead to cervical cancer if it progresses. Through meta-analysis, it was found that the regression rate varies among different degrees of lesions. For CIN1 patients with mild conditions, there is approximately a 40% spontaneous regression rate. The possibility of progression of advanced CIN (CIN2 or CIN3) lesions is relatively high, with a progression rate of 10.28% in CIN2 patients. However, in this process, age is negatively correlated with the rate of lesion regression, and the older the age, the lower the regression rate 26 . 3. Age is associated with abnormal HPV and TCT test results: The average interval between carcinogenic HPV infection and cervical cancer progression is 25–30 years. As age increases, the detection rate of HR-HPV positivity and abnormal TCT results is higher 27 . Generally speaking, the prevalence of HPV infection is highest among middle-aged and young women, especially those aged 25 to 35, who are prone to HPV virus infection due to frequent sexual activity. However, due to factors such as low education level, weakened immune function, and changes in hormone levels, the detection rate of SIL disease is higher in older age groups 28 . Table 2 − 1: Comparison of age distribution among different groups [ n (%)] Age n 20–30 >30–40 >40–50 Control 19 0 (0) 0 (0) 19 (100) LSIL 14 5 (35.71) 6 (42.86) 3 (21.43) HSIL 15 1 (6.67) 6 (40) 8 (53.33) χ ༒ 25.08 P 0.00** *P <0.05, **P <0.01. 3.2.3 Distribution of other factors in each group of patients Through the collection and organization of other basic information of each group of patients, we found that compared with the control group, there were no significant differences ( P > 0.05) in the number of pregnancies, smoking history, alcohol consumption history, vaginal microbiota diversity, and microbiota density between the LSIL group and HSIL group patients. However, there were differences in the age of first sexual intercourse and the number of sexual partners ( P < 0.05 and P < 0.01). Through statistical analysis (Table 2 – 2 ), we found that age, age of first sexual intercourse, and number of sexual partners are related to the occurrence of SIL, while no significant differences were observed in terms of parity, smoking history, alcohol consumption history, vaginal microbiota diversity, and microbiota density in this study. This study found that there were differences in the age of first sexual intercourse and the number of sexual partners among the three groups through a comparative analysis of basic information between the normal group and the lesion group. The lesion groups (LSIL group and HSIL group) showed characteristics such as premature first sexual intercourse and a large number of sexual partners. Table 2- 2 Distribution of other factors in each group [ n (%)] Group n Gravidity and parity history Smoke Insobriety Age of first sexual intercourse Number of sexual partners Diversity of vaginal microbiota Vaginal microbiota density ≤ 2 >2 No Yes No Yes ≤ 20 >20 <2 ≥ 2 +、++++ ++、+++ +、++++ ++、+++ Control 19 17 (89.47) 2 (10.53) 19 (100) 0 (0) 18 (94.74) 1 (5.26) 6 (31.58) 13 (68.42) 17 (89.47) 2 (10.53) 18 (94.74) 1 (5.26) 18 (94.74) 1 (5.26) LSIL 14 13 (92.86) 1 (7.14) 14 (100) 0 (0) 9 (64.29) 5 (35.71) 11 (78.57) 3 (21.43) 3 (21.43) 11 (78.57) 1 (7.14) 13 (92.86) 2 (14.29) 12 (85.71) HSIL 15 12 (80) 3 (20) 14 (93.33) 1 (6.67) 13 (86.67) 2 (13.33) 11 (73.33) 4 (26.67) 2 (13.33) 13 (86.67) 2 (13.33) 13 (86.67) 2 (13.33) 13 (86.67) χ ༒ 1.17 2.02 4.95 8.98 25.19 0.93 1.14 P 0.64 0.60 0.07 0.01 0.00 0.82 0.60 *Through comparative analysis, it was found that there were no differences among the three groups of patients in terms of parity, smoking, alcohol consumption, microbial diversity, and microbial density ( P > 0.05), but there were differences in age at first sexual intercourse and number of sexual partners(* P <0.05, ** P <0.01). 3.2.4 HPV and TCT test results By analyzing the HPV infection typing and TCT examination of patients in the LSIL and HSIL groups, it was found that compared with the LSIL group, the HSIL group had a higher detection frequency of high-risk types 16 and 18 infections ( P < 0.05), and the detection frequency of other high-risk types of infections in the LSIL group was also relatively higher. In the LSIL group, TCT detection is mostly NILM, while in the HSIL group, the vast majority of patients were found to have significant cytological abnormalities ( P < 0.01), as shown in Table 2 –3. In terms of detection methods and results, compared with LSIL, HSIL patients have a higher infection rate of HPV16 and 18 types and an abnormal rate of TCT test results. The detection rate of HR-HPV types 16 and 18 was higher in the LSIL group than in the HSIL group, except for patients. Table 2- 3 HPV and TCT results [ n (%)] Group n (%) HPV TCT High-risk HPV16 and 18 infections Other high-risk infections NILM ASC-US/LSIL ASC-H/HSIL SCC LSIL 14 7 (50) 7 (50) 13 (92.86) 1 (7.14) 0 (0) 0 (0) HSIL 15 14 (93.33) 1 (6.67) 2 (13.33) 6 (40) 7 (46.67) 0 (0) χ ༒ 4.81 19.00 P 0.03* 0.00** *P <0.05, **P <0.01. 2.2.5 Key Gene Expression Levels Through experimental analysis, we found that compared with the blank group, the target genes ALK and RPS14 were highly expressed in the HSIL group, and the difference was statistically significant ( P < 0.01); Compared with the LSIL group, the HSIL group showed high expression of the target gene RPS14 ( P < 0.05) and significant high expression of the target gene ALK ( P < 0.01). Compared with the blank group, key signaling pathway indicators PI3K, AKT, NF - κ B, and I κ B - α showed high expression in the HSIL group, and the difference was statistically significant ( P < 0.01); Compared with the LSIL group, key signaling pathway indicators PI3K, AKT, NF - κ B, and I κ B - α were significantly upregulated in the HSIL group ( P < 0.01). The specific results are shown in Fig. 2–7, and the numerical values are presented in Table 2 –4. This experiment directly observed the expression levels of PI3K and AKT in cervical lesions and normal cervical tissues through qRT PCR analysis. The expression level of NF - κ B is positively correlated with the severity of cervical lesions, and in the LSIL stage, the expression level of NF - κ B shows an upward trend. In the HSIL stage, the expression level significantly increases. The expression level of I κ B - α is low in normal cervical tissue and shows a slight decrease during the LSIL stage. However, in the HSIL stage, I κ B - α shows a high expression state. This indicates that the inflammatory response pathway may have been activated during the LSIL stage and is significantly activated during the HSIL stage. During the experiment, we found that the STK33 gene did not display Ct values, and no values were detected by changing primers or increasing sample concentration. Therefore, we did not display the results and speculated that this may be related to the low content of the STK33 gene in cervical tissue. Although the RPS14 gene can detect numerical values, the Ct value is too high, which may be related to the initial concentration of the template, indicating a low sample size in the template and indirectly reflecting that the RPS14 gene may have a low expression level in cervical tissue. Compared to others, the ALK gene has a more stable expression level in cervical tissue and is easier to detect. Table 2-4: mRNA expression levels of target genes in each group(±s, n = 3) Gene Control LSIL HSIL ALK 1.00 ± 0.43 2.47 ± 1.70 36.65 ± 22.68 **## RPS14 0.89 ± 0.36 1.23 ± 0.35 2.80 ± 2.40 **# PI3K 1.24 ± 0.56 2.01 ± 1.43 12.91 ± 9.40 **## AKT 1.09 ± 0.44 2.03 ± 1.18 23.99 ± 11.49 **## NF-κB 1.48 ± 0.84 24.19 ± 20.00 144.35 ± 68.51 **## IκB-α 1.01 ± 0.84 0.74 ± 0.44 19.99 ± 11.42 **## Note: Compared with the blank group, * P < 0.05, ** P < 0.01; Compared with LSIL, # P < 0.05, ## P < 0.01. 2.2.6 Correlation between the severity of cervical lesions and age, vaginal microbiota diversity, microbiota density, expression of key genes and signaling pathway targets By analyzing the correlation between age, microbiota diversity, microbiota density, key genes, and signaling pathway indicators in patients with LSIL and HSIL groups (Table 2 – 5 ), it was found that the degree of cervical lesions was correlated with age, vaginal microenvironment, ALK, RPS14, and activation of key genes and signaling pathways of PI3K/AKT/NF - κ B ( P < 0.05). Table 2- 5: HSIL correlation analysis Spearman Sig. Degree of cervical pathology Age -0.362 0.01 Vaginal secretion flora diversity 0.325 0.03 Vaginal secretion flora density 0.319 0.03 ALK 0.758 0.00 RPS14 0.523 0.00 PI3K 0.619 0.00 AKT 0.798 0.00 NF-κB 0.918 0.00 IκB-α 0.650 0.00 2.2.7 ROC curve analysis between the severity of cervical lesions and key points Through the analysis of the expression of key genes ALK, RPS14, and PI3K/AKT/NF - κ B signaling pathway targets between the Control group and HSIL group, it was found that ALK, RPS14, PI3K, AKT, NF - κ B, and I κ B - α have high diagnostic value for this disease (Fig. 2–8). Through comparison, it was found that the ALK gene has a higher diagnostic value for this disease than the RPS14 gene (Table 2 –6). Table 2-6 ROC Results Summary Test result variable AUC Standard Error Sig. 95%CI (L) 95%CI (U) ALK 0.98 0.19 0.00** 0.95 1.00 RPS14 0.83 0.70 0.00** 0.69 0.97 PI3K 0.91 0.57 0.00** 0.79 1.00 AKT 1.00 0.00 0.00** 1.00 1.00 NF-κB 1.00 0.00 0.00** 1.00 1.00 IκB-α 0.99 0.01 0.00** 0.97 1.00 * P <0.05, ** P <0.01. 3. Discussion ALK (anaplastic lymphoma kinase) is a receptor tyrosine kinase encoded by the ALK gene on the short arm of chromosome 2. This gene plays an important role in the signaling pathway of cell proliferation, maintaining normal cellular homeostasis by regulating cell growth and division 29 . However, when the ALK gene undergoes abnormalities such as copy number increase or rearrangement, its regulatory mechanism may lose control, leading to abnormal cell proliferation and ultimately potentially triggering the development of tumors 30 . The abnormal expression of the ALK gene has gradually attracted attention in the early-stage lesions of cervical cancer. Although there is relatively little direct evidence for ALK gene rearrangement in cervical cancer, its high frequency in other types of tumors, especially lung cancer, suggests its potential role in tumorigenesis 31 . It is worth noting that an increase in ALK gene copy number has been found in various gynecological cancers, including ovarian cancer, cervical cancer, and endometrial cancer. Among them, the proportion of ALK gene copy number increase in cervical cancer is 22.22% 32 . This discovery suggests that abnormalities in the ALK gene may be closely related to the progression of precancerous lesions in cervical cancer. Meanwhile, this study found through cervical biopsy of patients with different degrees of cervical lesions that the copy number of the ALK gene showed an increasing trend in the HSIL stage. The abnormal expression of the ALK gene may affect the progression of cervical precancerous lesions through multiple mechanisms. Firstly, amplification of the ALK gene may lead to overexpression of its encoded ALK protein, thereby activating downstream signaling pathways such as MAPK/ERK and PI3K/Akt, which play critical roles in cell proliferation, survival, and migration 33 . Secondly, abnormalities in the ALK gene may also lead to chromosomal instability and genome rearrangement, further exacerbating abnormal cell proliferation and tumor formation. The PI3K/Akt/NF - κ B signaling pathway is an important intracellular signaling pathway involved in regulating various physiological and pathological processes such as cell survival, proliferation, metabolism, angiogenesis, and inflammatory response 34 . This pathway is centered around phosphatidylinositol 3-kinase (PI3K), which phosphorylates PIP2 to generate PIP3, thereby activating Akt (protein kinase B). The activation of Akt further regulates its downstream effector molecules, including mTOR, NF - κ B, etc., thereby regulating cell growth, differentiation, and apoptosis 35 . This study found that the PI3K/Akt/NF - κ B signaling pathway is abnormally activated in cervical precancerous lesions. This activation process may be triggered by multiple factors, including HPV infection, stimulation of growth factors, and cytokines. HPV infection is the main cause of cervical cancer, and its encoded E6 and E7 proteins can bind to p53 and Rb proteins, respectively, leading to uncontrolled cell cycle regulation and inhibition of cell apoptosis 36 . This process may indirectly activate the PI3K/Akt/NF - κ B signaling pathway, promoting abnormal cell proliferation and tumor formation. Although there is relatively little research on the direct interaction between the ALK gene and the PI3K/Akt/NF - κ B signaling pathway in cervical precancerous lesions, based on this study, it can be inferred that abnormalities in the ALK gene may promote the progression of cervical precancerous lesions by activating the PI3K/Akt/NF - κ B signaling pathway. ALK protein, as a receptor tyrosine kinase, may initiate a cascade reaction of the PI3K/Akt/NF - κ B signaling pathway by phosphorylating downstream signaling molecules such as Akt upon activation. This interaction may make the role of the ALK gene in cervical precancerous lesions more complex and diverse. Given the important roles of the ALK gene and PI3K/Akt/NF - κ B signaling pathway in cervical precancerous lesions, therapeutic strategies targeting these targets may have potential clinical application value. For example, developing inhibitors targeting ALK genes or targeted drugs targeting key molecules in the PI3K/Akt/NF - κ B signaling pathway may help block abnormal proliferation and survival of tumor cells, thereby achieving the goal of treating precancerous lesions of cervical cancer. In addition, further in-depth research on the specific interaction mechanism between the ALK gene and PI3K/Akt/NF - κ B signaling pathway in cervical precancerous lesions will help us to have a more comprehensive understanding of the pathogenesis of cervical cancer and provide a scientific basis for developing more effective treatment strategies. 4. Conclusion In summary, the combination of bioinformatics analysis and human cervical tissue sample testing has also confirmed the activation status of key genes ALK and PI3K/AKT/NF - κ B signaling pathways in this disease. This discovery suggests that ALK genes may play an important role in cervical precancerous lesions. At the same time, amplification of the ALK gene may lead to an increase in the expression level of its encoded ALK protein, which in turn promotes abnormal cell proliferation and survival by activating downstream signaling pathways such as PI3K/Akt/NF - κ B and MAPK/ERK. These abnormal activations are key steps in tumor development and progression. Therefore, abnormalities in the ALK gene may be an important factor in the progression of cervical precancerous lesions. 5. Materials and Methods 5.1 Patient Collection This experiment adopts a case-control study method for research. Select SIL patients who were admitted to the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine from March 2023 to February 2024 and met the relevant diagnostic criteria. According to the pathological diagnosis of the patients, they were divided into the LSIL group and the HSIL group. At the same time, patients who underwent cervical resection for other benign diseases and underwent cervical HPV testing combined with a liquid-based thin-layer cytology test (TCT) without any abnormalities were collected as the control group. This experimental study was approved by the Ethics Committee of the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine (approval number: HZYLLKY202300701). We promise that all research will be conducted in accordance with the specified regulations. All subjects were informed and consented. 5.2 Data collection When selecting a dataset from the GEO database, the following criteria must be met: (1) the data contains tissue samples of HSIL or SIL, (2) Contains technical and platform information for research purposes, (3) Simultaneously including normal cervical tissue as a control study object. 5.3 Identify differentially expressed genes Using the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). The GEO2R online processing tool in the dataset identifies differentially expressed genes (DEGs) and processes the data using the limma R software package. Genes with P 2.0, LogFC1 are selected as differentially expressed genes (DEGs), and the differentially expressed genes shared in the dataset are extracted using Venny plots from the selected DEGs. 5.4 Construction of protein-protein interaction network (PPI) Upload the target to the Uniprot database ( https://www.uniprot.org ). Obtain the converted gene name. Then upload the converted gene information to the STRING database for analysis, with the screening criteria being "Homo Sapiens" and the remaining criteria being based on default standards. At the same time, Cytoscape 3.9.1 software was used for PPI visualization processing, and the CytoHubba plugin was used to identify the central node genes in the PPI network. Then, the central node genes were selected as candidate DEGs for further analysis. 5.5 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis Using the DAVID online website to identify and analyze differentially expressed genes, the identification criteria are based on "Homo Sapiens" and "official gene symbol", with P < 0.05 considered a significant key criterion. Additionally, the online drawing software "Microbioinformatics" is utilized( http://www.bioinformatics.com ) to Visualize the analysis results of DAVID. 5.6 Gene Set Enrichment Analysis (GSEA) predicts key genes Upload the gene symbol to the "Weishengxin" website, select "Hallmark gene sets" for the gene set, and complete the prediction and screening of key genes and targets. At the same time, genes will be enriched and analyzed by the module to identify the most relevant gene enrichment modules for this disease and to clarify the key biological processes that induce this disease. 5.7 qRT-PCR Take 70mg of cervical tissue and add 1mL RNAkeyTM Reagent (Beijing Saiwen Innovation Biotechnology Co., Ltd., China) for total RNA extraction. After measuring RNA purity and RNA concentration in each group, perform reverse transcription experiments. The qRT-PCR experiment was conducted using the SYBR Premix ExTaq II kit (TaKaRa Biotechnology, Japan) on the ABILIFE QuantStudio 12K detection system (Foster City, California, USA). The total reaction volume is 20 µ L per well, with 3 sub-wells per sample, and the average value is taken for calculation. The primer design sequences for each indicator are shown in Table 5 − 1. Referring to GAPDH, the relative mRNA expression of each group was detected using the comparative Ct (2 − Δ Δ CT ) method. The melting curves of each amp were determined to verify their specificity. Table 5 − 1 Primer sequence design Primer name Sequence: 5′- 3′ STK33 F: GAAAAGTTTCTCCCGGTGCAG R: TTTATCTGGCTCCCCATCGC RPS14 F:AGCTTGTGAAAAATGGCACCTC R:TTCATCCCACCAGTCACAC ALK F:CCAGACTAACATGACTCTGCC R: AGCCTCCCTGGATCTCCATA PIK3CA F:GGACCCGATGCGGTTAGAG R:ATCAAGTGGATGCCCCACAG AKT1 F:GGACAAGGACGGGCACATTA R: CGACCGCACATCATCTCGTA NF-κB F:AATGGGCTACACCGAAGCAA R:CTGTCGCAGACACTGTCACT IκB-α F:AAGTGATCCGCCAGGTGAAG R:CTGCTCACAGGCAAGGTGTA GAPDH F:CTCGCTCCTGGAAGATGGTG R:GCAAAGTAGAAAAGGGCAAC 5.8 Statistical analyses For the cell studies, the data represent three independent experiments, and all data displays are shown as mean ± standard deviation (SD) unless otherwise stated. Statistical analyses were performed using SPSS 25.0 software, with one-way ANOVA selected if normal distribution was met, and rank-sum test if not, and Graphpad Prism 9.5.1 software for graphical presentation. P < 0.05 indicates that the difference is statistically significant, and P < 0.01 indicates that the difference is statistically significant. Declarations Acknowledgements We acknowledge Dr. Zhicheng Wang, who helped improve the scientific quality of this study. Author contributions Data curation: Yiming Sun, Wenxia Ai Formal analysis: Ding Qi, Buwei Han Funding acquistion: Li Liu, Yonggang Xia Wring draft: Ding Qi Writing - editing: Ding Qi, Mingge Liang This study has obtained informed consent from all participants. The human tissue research involved in this study has been approved by the Ethics Committee of the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine (approval number: HZYLLKY202300701). Data availability: The datasets used and analysed during the current study available from the corresponding author on reasonable request No conflict of interest between authors. References Duggan MA. A review of the natural history of cervical intraepithelial neoplasia. Gan To Kagaku Ryoho. Suppl 1:176-93 (2002). Wu B, Xi S. Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer. BMC Cancer. 21 (1):733 (2021). Liu M, Yan X, Zhang M, Li X, Li S, Jing M. Influence of Human Papillomavirus Infection on the Natural History of Cervical Intraepithelial Neoplasia 1: A Meta-Analysis. Biomed Res Int. 2017;2017:8971059. Koeneman MM, Kruitwagen RF, Nijman HW, Slangen BF, Van Gorp T, Kruse AJ. Natural history of high-grade cervical intraepithelial neoplasia: a review of prognostic biomarkers. Expert Rev Mol Diagn. 15 (4):527-46 (2015). van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 19 (4):575-592 (2018). Nero C, Ciccarone F, Pietragalla A, Scambia G. PTEN and Gynecological Cancers. Cancers (Basel). 11 (10):1458 (2019). Yang WT, Chen M, Xu R, Zheng PS. PRDM4 inhibits cell proliferation and tumorigenesis by inactivating the PI3K/AKT signaling pathway through targeting of PTEN in cervical carcinoma. Oncogene. 40 (18):3318-3330 (2021). Lee MS, Jeong MH, Lee HW, Han HJ, Ko A, Hewitt SM, Kim JH, Chun KH, Chung JY, Lee C, Cho H, Song J. PI3K/AKT activation induces PTEN ubiquitination and destabilization accelerating tumourigenesis. Nat Commun. 6 :7769 (2015). Yuan J, Yi K, Yang L. TGFBR2 Regulates Hedgehog Pathway and Cervical Cancer Cell Proliferation and Migration by Mediating SMAD4. J Proteome Res. 19 (8):3377-3385 (2020). Pinke DE, Kalloger SE, Francetic T, Huntsman DG, Lee JM. The prognostic significance of elongation factor eEF1A2 in ovarian cancer. Gynecol Oncol. 108 (3):561-8 (2008). Longerich T. EEF1A2 inhibiert über eine PI3K/AKT/mTOR-abhängige Stabilisierung von MDM4 die p53-Funktion im Leberzellkarzinom [EEF1A2 inhibits the p53 function in hepatocellular carcinoma via PI3K/AKT/mTOR-dependent stabilization of MDM4]. Pathologe. 35 Suppl 2:177-84 (2014). Sun Y, Du C, Wang B, Zhang Y, Liu X, Ren G. Up-regulation of eEF1A2 promotes proliferation and inhibits apoptosis in prostate cancer. Biochem Biophys Res Commun. 450 (1):1-6 (2014). Zheng W, Jin F, Wang F, Wang L, Fu S, Pan Z, Long H. Analysis of eEF1A2 gene expression and copy number in cervical carcinoma. Medicine (Baltimore). 102 (2):e32559 (2023). Xiao D, Huang W, Ou M, Guo C, Ye X, Liu Y, Wang M, Zhang B, Zhang N, Huang S, Zang J, Zhou Z, Wen Z, Zeng C, Wu C, Huang C, Wei X, Yang G, Jing C. Interaction between susceptibility loci in cGAS-STING pathway, MHC gene and HPV infection on the risk of cervical precancerous lesions in Chinese population. Oncotarget. 7 (51):84228-84238 (2016). Kurmyshkina O, Kovchur P, Schegoleva L, Volkova T. Markers of Angiogenesis, Lymphangiogenesis, and Epithelial-Mesenchymal Transition (Plasticity) in CIN and Early Invasive Carcinoma of the Cervix: Exploring Putative Molecular Mechanisms Involved in Early Tumor Invasion. Int J Mol Sci. 21 (18):6515 (2020). Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 447 (7145):661-78 (2007). Mujica AO, Hankeln T, Schmidt ER. A novel serine/threonine kinase gene, STK33, on human chromosome 11p15.3. 280 (1-2):175-81 (2001). Scholl C, Fröhling S, Dunn IF, et al. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. 137 (5):821-34 (2009). Hurtado R, Ramirez A, Nabipur L, et al. The Key Role of the RPS14 Gene in Neoplasms and Solid Tumors. J Assoc Genet Technol, 49 (3):121-126 (2023). Pellagatti A, Boultwood J. Recent Advances in the 5q- Syndrome. Mediterr J Hematol Infect Dis. 7 (1):e2015037 (2015). Tian B, Zhou J, Chen G, et al. Downregulation of ZNF280A inhibits the proliferation and tumorigenicity of colorectal cancer cells by promoting the ubiquitination and degradation of RPS14. Front Oncol. 12 :906281 (2022). Wang L, Lui VWY. Emerging Roles of ALK in Immunity and Insights for Immunotherapy. Cancers (Basel). 12 (2):426 (2020). Holla VR, Elamin YY, Bailey AM, et al. ALK: a tyrosine kinase target for cancer therapy. Cold Spring Harb Mol Case Stud. 3 (1):a001115 (2017). Chang HK, Seo SS, Myong JP, et al. Incidence and costs of cervical intraepithelial neoplasia in the Korean population. J Gynecol Oncol. 30 (3):e37 (2019). Bekos C, Schwameis R, Heinze G, et al. Influence of age on histologic outcome of cervical intraepithelial neoplasia during observational management: results from a large cohort, systematic review, meta-analysis. Sci Rep. 8 (1):6383 (2018). Zhang J, Lu CX. Spontaneous Regression of Cervical Intraepithelial Neoplasia 2: A Meta-analysis. Gynecol Obstet Invest, 84 (6):562-567 (2019). Yang D, Zhang J, Cui X, et al. Risk Factors Associated With Human Papillomavirus Infection, Cervical Cancer, and Precancerous Lesions in Large-Scale Population Screening. Front Microbiol. 13:914516 (2022). Ding YQ, Yu J, Wang RQ, et al. Clinical and epidemiological features of high-risk human papillomavirus infection in patients with cervical intraepithelial lesions. BMC Womens Health. 23 (1):468 (2023). Gromowsky MJ, D'Angelo CR, Lunning MA, Armitage JO. ALK-positive anaplastic large cell lymphoma in adults. Fac Rev. 12 :21 (2023). Cao Z, Gao Q, Fu M, Ni N, Pei Y, Ou WB. Anaplastic lymphoma kinase fusions: Roles in cancer and therapeutic perspectives. Oncol Lett. 17 (2):2020-2030 (2019). Nagano T, Tachihara M, Nishimura Y. Molecular Mechanisms and Targeted Therapies Including Immunotherapy for Non-Small Cell Lung Cancer. Curr Cancer Drug Targets. 19 (8):595-630 (2019). Croce S, Devouassoux-Shisheboran M, Pautier P, Ray-Coquard I, Treilleux I, Neuville A, Arnould L, Just PA, Belda MALF, Averous G, Leroux A, Mery E, Loussouarn D, Weinberg N, Le Guellec S, Mishellany F, Morice P, Guyon F, Genestie C. Uterine sarcomas and rare uterine mesenchymal tumors with malignant potential. Diagnostic guidelines of the French Sarcoma Group and the Rare Gynecological Tumors Group. Gynecol Oncol. 167 (2):373-389 (2022). Lambertz I, Kumps C, Claeys S, Lindner S, Beckers A, Janssens E, Carter DR, Cazes A, Cheung BB, De Mariano M, De Bondt A, De Brouwer S, Delattre O, Gibbons J, Janoueix-Lerosey I, Laureys G, Liang C, Marchall GM, Porcu M, Takita J, Trujillo DC, Van Den Wyngaert I, Van Roy N, Van Goethem A, Van Maerken T, Zabrocki P, Cools J, Schulte JH, Vialard J, Speleman F, De Preter K. Upregulation of MAPK Negative Feedback Regulators and RET in Mutant ALK Neuroblastoma: Implications for Targeted Treatment. Clin Cancer Res. 21 (14):3327-39 (2015). Chen L, Pei H, Lu SJ, Liu ZJ, Yan L, Zhao XM, Hu B, Lu HG. SPOP suppresses osteosarcoma invasion via the PI3K/AKT/NF-κB signaling pathway. Eur Rev Med Pharmacol Sci. 22 (3):609-615 (2018). Zhao W, Qiu Y, Kong D. Class I phosphatidylinositol 3-kinase inhibitors for cancer therapy. Acta Pharm Sin B. 7 (1):27-37 (2017). Gupta S, Kumar P, Das BC. HPV: Molecular pathways and targets. Curr Probl Cancer. 42 (2):161-174 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4939442","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":357548548,"identity":"7768183d-45fd-49a4-923f-76850c38f950","order_by":0,"name":"Ding Qi","email":"","orcid":"","institution":"The Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Qi","suffix":""},{"id":357548549,"identity":"ed808465-bba9-416a-b9e8-0f67af611b7a","order_by":1,"name":"Yiming Sun","email":"","orcid":"","institution":"Heilongjiang Provincial Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Sun","suffix":""},{"id":357548550,"identity":"adc48bd2-8c31-4adb-aa32-52e8cb182775","order_by":2,"name":"Wenxia Ai","email":"","orcid":"","institution":"Heilongjiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenxia","middleName":"","lastName":"Ai","suffix":""},{"id":357548551,"identity":"07f2ad10-3bac-4554-b81f-612cf2ad0609","order_by":3,"name":"Buwei Han","email":"","orcid":"","institution":"Heilongjiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Buwei","middleName":"","lastName":"Han","suffix":""},{"id":357548552,"identity":"9a229f4f-a2fa-455b-888c-2d931b1a3f1b","order_by":4,"name":"Mingge Liang","email":"","orcid":"","institution":"Heilongjiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingge","middleName":"","lastName":"Liang","suffix":""},{"id":357548553,"identity":"af9383c0-a675-4fcd-963f-5688d8dd131a","order_by":5,"name":"Mingshu Zhang","email":"","orcid":"","institution":"Heilongjiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingshu","middleName":"","lastName":"Zhang","suffix":""},{"id":357548554,"identity":"8ef5aa85-8d11-4365-81fa-28acb8fd32eb","order_by":6,"name":"Yonggang Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACNmbGxocfDGzk2NibDxCnhY+9+bCxREGaMT/PsQTitMjxHEsT4PlwOHHmjBwDIh0mkWPGIGHAzLjhQM7HG28Y7OR0G4jQ8qDAgI3Z4MDZzZZzGJKNzQ4Q1mJuIGHAw2ZwsHebNA/DgcRtRGgxk+AxAKLDPM+I1AL0PlC9gYRkGw8bkVrAgWyQYMDPw2ZsOceACL/IN4Oi8s//+jb5xw9vvKmwkyOoBQUAXUiKcogWUnWMglEwCkbBiAAAWFg9vFoMZpoAAAAASUVORK5CYII=","orcid":"","institution":"Heilongjiang University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yonggang","middleName":"","lastName":"Xia","suffix":""},{"id":357548555,"identity":"91589d70-541a-430e-85fa-d85af7342a7f","order_by":7,"name":"Li Liu","email":"","orcid":"","institution":"Heilongjiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-08-19 14:45:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4939442/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4939442/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67099708,"identity":"827f31b0-f05b-4bb9-b0b8-e30830890f3a","added_by":"auto","created_at":"2024-10-21 08:04:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558426,"visible":true,"origin":"","legend":"\u003cp\u003eFig.2-1 Gene expression in two datasets\u003c/p\u003e\n\u003cp\u003eA. GSE75132 Volcano Map, B. GSE75132 Scatter plot, C. GSE75132 heat map, D.GSE63514 Volcano Map, E. GSE63514 scatter plot, F. GSE63514 Heat Map\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/674569aa35c8725cb800fccc.png"},{"id":67099706,"identity":"29bea6f6-b55a-48c6-95e5-09183f51ae43","added_by":"auto","created_at":"2024-10-21 08:04:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71005,"visible":true,"origin":"","legend":"\u003cp\u003eFig.2-2 Venny plot\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/9becce18ccc74cdf55e08825.png"},{"id":67099707,"identity":"4c48d964-c0ff-4388-b2db-269ff42b95dd","added_by":"auto","created_at":"2024-10-21 08:04:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":445339,"visible":true,"origin":"","legend":"\u003cp\u003eFig.2-3 PPI Network and Cytoscape Analysis\u003c/p\u003e\n\u003cp\u003eA. PPI network diagram of genes, B. The top 10 key genes in terms of degree ranking, C. Degree distribution map of differentially co-expressed genes, D. Degree correlation analysis of differentially co-expressed genes\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/48c3f85ba69f07170c766d4b.png"},{"id":67099714,"identity":"836e0ead-89c8-46fd-b6b3-043c930448db","added_by":"auto","created_at":"2024-10-21 08:04:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":200597,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 2-4 Visual display of GO analysis\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/95d7b640b9b393e1c7f2ae26.png"},{"id":67099710,"identity":"b6059ac7-676a-4612-8b91-9d7a6319c160","added_by":"auto","created_at":"2024-10-21 08:04:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":201723,"visible":true,"origin":"","legend":"\u003cp\u003eFig.2-5 KEGG pathway visualization analysis\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/ce5fb4fc768088da15989830.png"},{"id":67099712,"identity":"807d82da-a9bf-4ba0-97ff-94939be1a7a6","added_by":"auto","created_at":"2024-10-21 08:04:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":145946,"visible":true,"origin":"","legend":"\u003cp\u003eFig.2-6 GSEA analysis results\u003c/p\u003e\n\u003cp\u003eA. Key gene prediction, B. Prediction of Key Gene Enrichment Module\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/b6b27a972e16587f6d7134bf.png"},{"id":67101213,"identity":"9379a481-8886-417f-84d9-0b25d2e3b556","added_by":"auto","created_at":"2024-10-21 08:12:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191707,"visible":true,"origin":"","legend":"\u003cp\u003eFig.2-7 mRNA expression levels of target genes in each group\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/bf8eb0876596fc0af3c703de.png"},{"id":67102018,"identity":"f162b95c-e094-4577-9547-734ce197884e","added_by":"auto","created_at":"2024-10-21 08:20:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2093157,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 2-8 ROC curve analysis\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/fe7bc78127288f9ab90a4db4.png"},{"id":81032342,"identity":"da1337c1-fd4f-41cf-b6e9-554a491867bd","added_by":"auto","created_at":"2025-04-21 11:31:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5276702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4939442/v1/46f7e902-f380-4865-b3f9-8ab2db1341dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of ALK gene and PI3k/Akt/NF - κ B signaling pathway in precancerous lesions of cervical cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCervical precancerous lesions, also known as cervical intraepithelial neoplasia (CIN), are closely related to invasive cervical cancer. According to pathological grading, CIN can be classified into CIN I, CIN II, and CIN III. Indeed, cervical precancerous lesions (CIN) represent a critical stage in the development of invasive cervical cancer, and their classification into low-grade (LSIL) and high-grade (HSIL) based on factors like p16 expression provides valuable insights into disease progression\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The transformation from normal cervical epithelium to precancerous lesions and ultimately to invasive cancer is a complex, multi-step process involving alterations in multiple genes and signaling pathways\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While high-risk human papillomavirus (HPV) infection is a well-established primary cause of cervical cancer\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, it is important to note that not all HPV-infected individuals will develop the disease. This suggests that additional genetic and environmental factors contribute significantly to the progression of cervical precancerous lesions to invasive cancer\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, research on the relationship between genes and diseases has been of great significance in exploring the pathogenesis of diseases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In related studies on cervical cancer, it has been pointed out that related genes and pathways play a role in the disease. For example, the PTEN gene, as an important tumor suppressor gene, is highly expressed in normal cervical tissue and crucial for maintaining the balance between cell proliferation and apoptosis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, in precancerous lesions and cervical cancer tissues, PTEN expression is often downregulated or absent\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, leading to a weakened inhibitory effect on the PI3K/Akt pathway, which in turn promotes abnormal cell proliferation and tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The TGF - β signaling pathway plays an important role in the occurrence and development of tumors, and the TGFBR2 gene is one of the core genes of this pathway. The loss or decrease of TGFBR2 expression is believed to promote the occurrence and progression of tumors\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The eEF1A2 gene is closely associated with the occurrence and development of various tumors, including ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, liver cancer\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and prostate cancer\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Although there is limited research on the direct role of the eEF1A2 gene in cervical cancer, previous studies have suggested that it may play a role in the development of cervical cancer by inhibiting cell apoptosis and promoting cancer cell expansion\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The transmission of information through signaling pathways from the outside to the inside of cells can have an impact on diseases. The cGAS STING signaling pathway plays a crucial role in the immune response triggered by viral infection. Studies have shown that abnormal activation of the cGAS STING signaling pathway may be associated with susceptibility to cervical precancerous lesions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. At present, relevant research has revealed that the molecular mechanisms underlying the induction of HSIL are multi-layered, involving multiple biological processes, and the layer-to-layer signaling pathways associated with HSIL are also diverse\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, there are relatively few genetic reports on the specificity and specificity of HSIL. Therefore, this study aims to use bioinformatics methods and high-throughput sequencing data to screen genes and pathways closely related to HSIL, find more specific and sensitive indicators, and enrich genetic research on this disease. This research will help to develop more sensitive and specific early diagnostic markers, improve the early detection rate of cervical cancer, achieve early intervention and treatment, and reduce the incidence rate and mortality of cervical cancer.\u003c/p\u003e \u003cp\u003eThis study focuses on the role of genes and signaling pathways in cervical precancerous lesions, aiming to provide a new theoretical basis and potential targets for early diagnosis, prevention, and treatment of cervical cancer through in-depth molecular mechanism research. It has important scientific significance and clinical application value.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Bioinformatics analysis results\u003c/h2\u003e\n \u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.1.1 Determination of Dataset\u003c/h2\u003e\n \u003cp\u003eA search was conducted in the GEO database using the terms \u0026ldquo;cervical intraepithelial neoplasia\u0026rdquo;, \u0026ldquo;cervical HSIL\u0026rdquo;, or \u0026ldquo;cervical high-grade intraepithelial lesion\u0026rdquo;, resulting in 588 eligible datasets. After inspection and comparison, the GSE63514 and GSE75132 datasets were finally selected for comparison after data correction. Both datasets were detected using high-throughput sequencing on the GPL570 platform, with a detectable nucleotide (nt) length of 600. The Affymetrix Human Genome U133 Plus 2.0 array was used to fully cover the U133 set of the human genome, as well as analyze 6500 additional genes and over 47000 transcripts. The sensitivity and accuracy of the detection were high\u003csup\u003e\u003cspan\u003e16\u003c/span\u003e\u003c/sup\u003e. The GSE63514 dataset contains a total of 128 sequencing samples, including tissue sample data from 24 normal patients, 14 cervical CIN I, 22 cervical CIN II, 40 cervical CIN III, and 28 cervical cancer patients. The GSE75132 dataset includes 41 samples, including 11 normal samples, 10 patients with persistent HPV16 infection, 4 cervical CIN II, 9 cervical CIN III, and 7 cervical cancer samples.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.1.2 GEO 2R processing results\u003c/h2\u003e\n \u003cp\u003eThe basic information analysis of the two datasets is as follows: In the GSE75132 dataset, the data was first adjusted for baseline alignment, then compared and analyzed, and volcano plots (Fig.\u0026nbsp;2-1A) and scatter plots (Fig.\u0026nbsp;2-1B) were plotted for this dataset. Finally, 2266 differentially expressed genes were identified, of which 951 genes were significantly upregulated and 1315 genes were significantly downregulated (Fig.\u0026nbsp;2-1C). In the GSE63514 dataset, there are a total of 2208 differentially expressed genes between normal tissues and high-grade lesion tissues (Fig.\u0026nbsp;2-1D, Fig.\u0026nbsp;2-1E), of which 1203 genes are significantly downregulated and 1005 genes are significantly up-regulated (Fig.\u0026nbsp;2-1F).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure\u0026nbsp;2\u0026thinsp;\u0026minus;\u0026thinsp;1 Gene expression in two datasets\u003c/p\u003e\n \u003cp\u003eA. GSE75132 Volcano Map, B. GSE75132 Scatter plot, C. GSE75132 heat map, D. GSE63514 Volcano Map, E. GSE63514 scatter plot, F. GSE63514 Heat Map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.1.3 Screening of differentially expressed genes\u003c/h2\u003e\n \u003cp\u003eUpload differentially expressed genes from two datasets separately to the website(\u003cspan\u003e\u003cspan\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/index.html)Dra\u003c/span\u003e\u003c/span\u003ew a Venny plot to obtain 371 differentially expressed genes, as shown in Fig.\u0026nbsp;2\u0026ndash;2. After analyzing and calculating 371 differentially expressed genes, it was found that there were 221 downregulated genes and 150 upregulated genes.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure\u0026nbsp;2\u0026ndash;2 Venny plot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.1.4 PPI network analysis results\u003c/h2\u003e\n \u003cp\u003eDownload the results of STRING database analysis and use Cytoscape 3.9.1 software to construct a PPI interaction network (Fig.\u0026nbsp;2-3A) with 288 nodes and 1274 edges. The top 10 key genes were calculated, including vascular endothelial growth factor A (VEGFA), matrix metalloproteinase-9 (MMP9), hyaluronic acid receptor protein CD44 (CD44), cyclin D1 (CCND1), cyclin B1 (CCNB1), serum chemokine ligand 8 (CXCL8), estrogen receptor 1 (ESR1), and Toll-like receptor 2 (Toll-like receptor 2). Like receptor 2 (TLR2), CXC chemokine receptor 4 (CXCR4), and signal transducer and activator of transcription 1 (STAT1), the results are shown in Fig.\u0026nbsp;2-3B. The average degree value of the PPI network is 7.26 (Fig.\u0026nbsp;2-3C), and the correlation between genes is good (Fig.\u0026nbsp;2-3D).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure\u0026nbsp;2\u0026ndash;3 PPI Network and Cytoscape Analysis\u003c/p\u003e\n \u003cp\u003eA. PPI network diagram of genes, B. The top 10 key genes in terms of degree ranking, C. Degree distribution map of differentially co-expressed genes, D. Degree correlation analysis of differentially co-expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.1.5 GO analysis\u003c/h2\u003e\n \u003cp\u003eThrough the analysis of differentially expressed genes, 102 key biological processes (BP), 33 cellular components (CC), and 15 molecular functions (MF) were identified. The top 10 items from each group were selected to draw GO analysis graphs, as shown in Fig.\u0026nbsp;2\u0026ndash;4. After analysis, it was found that the biological processes involved in this disease include inflammatory response, cellular response to interferon - \u0026gamma;, immune response, signal transduction, positive regulation of protein phosphorylation, positive regulation of interleukin-6 production, apoptosis process, and positive regulation of I-kappaB kinase/NF-kapaB signaling. Cellular components are mainly related to cytoplasmic contents and the extracellular environment. Molecular functions involve protein binding, enzyme binding, as well as related factors and protein connections.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure\u0026nbsp;2\u0026ndash;4 Visual display of GO analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1.6 KEGG pathway screening\u003c/h2\u003e\n \u003cp\u003eThrough comparative analysis, a total of 29 signaling pathways were found to be associated with cervical HSIL lesions in this experiment (Fig.\u0026nbsp;2\u0026ndash;5). These include chemokine signaling pathway, Kaposi sarcoma-associated herpesvirus infection, proteoglycan in cancer, cancer pathway, the interaction of the viral protein with cytokines and cytokine receptors, natural killer cell-mediated cytotoxicity, human immunodeficiency virus type 1 infection, human cytomegalovirus infection, PI3K-AKT signaling pathway, Toll-like receptor signaling pathway, cytokine receptor interaction, Ras signaling pathway, nucleotide metabolism, leukocyte transendothelial migration, DNA replication, lipid and atherosclerosis.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure\u0026nbsp;2\u0026ndash;5 KEGG pathway visualization analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.1.7 GSEA Key Gene Screening\u003c/h2\u003e\n \u003cp\u003eThrough GSEA enrichment analysis, three genes related to this disease were predicted, namely Serine/threonine kinase 33 (STK33), Ribosomal protein S14 (RPS14), and Anaplastic lymphoma kinase (ALK). The expression levels of these three key genes showed an upward trend in this disease (Fig.\u0026nbsp;2-6A). The analysis of enriched gene modules showed that the three biological processes most closely related to this disease are the TNF - \u0026alpha; signaling pathway mediated by NF - \u0026kappa; B, interferon - \u0026gamma; response, and immune response (Fig.\u0026nbsp;2-6B).\u003c/p\u003e\n \u003cp\u003eStudies have shown that STK33 is expressed in various tissues, with the strongest hybridization signals observed in the testes, fetal lungs, and heart, followed by the pituitary gland, kidneys, pancreas, heart, thyroid gland, and uterus. It exhibits weak hybridization signals in the aorta, blood system, and digestive system, while no hybridization signals were detected in the nervous system tissues\u003csup\u003e\u003cspan\u003e17\u003c/span\u003e\u003c/sup\u003e. STK33 has been found to inhibit mitochondrial apoptosis and enhance tumor cell growth vitality by suppressing BAD activity in tumor-related diseases, suggesting that STK33 may be an effective target for combating oncogenic mutations\u003csup\u003e\u003cspan\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThrough a literature search, it was found that RPS14 can prevent the interaction between MDM2-p53, leading to a significant accumulation of p53. This accumulation can induce cell cycle arrest, DNA repair damage, aging, and apoptosis\u003csup\u003e\u003cspan\u003e19\u003c/span\u003e\u003c/sup\u003e. RPS14 is a 40S ribosomal subunit component that is associated with red blood cell differentiation and is related to various blood diseases such as myelodysplastic syndrome and 5q syndrome\u003csup\u003e\u003cspan\u003e20\u003c/span\u003e\u003c/sup\u003e. In a study on colon cancer, it was found that overexpression of RPS14 can activate the PI3K/AKT signaling pathway, enhancing the survival ability of tumor cells\u003csup\u003e\u003cspan\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eAt present, changes in ALK gene recombination, fusion, mutation, amplification, and splicing have been found in various tumor diseases. In addition, ALK can mediate the NF - \u0026kappa; B signaling pathway, and regulate and promote the activation of NLRP3 inflammasome in macrophages. Therefore, ALK targeting may represent a new strategy for treating NLRP3 inflammasome-mediated diseases\u003csup\u003e\u003cspan\u003e22\u003c/span\u003e, \u003cspan\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFigure\u0026nbsp;2\u0026ndash;6 GSEA analysis results\u003c/p\u003e\n \u003cp\u003eA. Key gene prediction, B. Prediction of Key Gene Enrichment Module\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.2 Data Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2.2.1 Basic Patient Information\u003c/h2\u003e\n \u003cp\u003eCollect medical records of outpatient and inpatient patients. The original total number of collected patients was 77, but due to various reasons, a total of 29 patients were dropped or excluded. This study includes a total of 48 patients. Among them, 38 patients were included in the normal group, of which 16 were excluded due to incomplete information filling, 2 were excluded from previous cervical LEEP treatment, and 1 was excluded from seeking medical treatment at an external hospital. A total of 18 patients were excluded, 1 dropped out, and 19 patients were collected. 17 patients were included in the LSIL group, including 1 case excluded due to abnormal cervical glandular cells and 2 cases excluded due to incomplete data filling. A total of 3 patients were excluded, and 14 patients were collected. 22 patients were included in the HSIL group, of which 3 were excluded due to abnormal cervical glandular cells, 3 were excluded due to incomplete information filling, and 1 was excluded due to a controversial diagnosis from an external hospital (nonregular hospital) based on pathological results. A total of 7 patients were excluded, and 15 patients were collected.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e2.2.2 Comparison of Basic Age Information of Patients in Different Groups\u003c/h2\u003e\n \u003cp\u003eThe age distribution of patients in each group was compared, and the differences were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The age distribution of the control group is mostly concentrated between 40\u0026ndash;50 years old, and this group of patients mostly undergo total hysterectomy due to conditions such as adenomyosis and multiple uterine fibroids. The prevalence of LSIL in the age group is mostly concentrated between 30\u0026ndash;40 years old and 20\u0026ndash;30 years old, with younger onset ages. The age incidence rate of the HSIL group is mostly concentrated between 40\u0026ndash;50 years old and 30\u0026ndash;40 years old (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1).\u003c/p\u003e\n \u003cp\u003eThis study found that the age of onset in the LSIL group was mostly concentrated between 30\u0026ndash;40 years old, with a younger onset age, while in the HSIL group, the age of onset was mostly concentrated between 40\u0026ndash;50 years old. Statistical calculations showed that age was positively correlated with the severity of the disease, which may be related to the prolonged duration of viral infection.\u003c/p\u003e\n \u003cp\u003eThe relationship between age and this disease mainly includes the following points: 1. There are differences in the severity of lesions between different age groups: Chang HK et al. found through research that the peak age range of LSIL onset is 25\u0026ndash;29 years old, the peak age range of HSIL onset is 30\u0026ndash;34 years old, and the peak age range of cervical cancer onset is 70\u0026ndash;74 years old. There are differences in the severity of lesions between different age groups. The older the age, the more severe the disease\u003csup\u003e\u003cspan\u003e24\u003c/span\u003e\u003c/sup\u003e, while younger patients have higher disease regression and complete remission rates, and lower progression rates\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/sup\u003e. 2. The natural regression rate of SIL varies among different age groups: HSIL is a precancerous disease that may lead to cervical cancer if it progresses. Through meta-analysis, it was found that the regression rate varies among different degrees of lesions. For CIN1 patients with mild conditions, there is approximately a 40% spontaneous regression rate. The possibility of progression of advanced CIN (CIN2 or CIN3) lesions is relatively high, with a progression rate of 10.28% in CIN2 patients. However, in this process, age is negatively correlated with the rate of lesion regression, and the older the age, the lower the regression rate\u003csup\u003e\u003cspan\u003e26\u003c/span\u003e\u003c/sup\u003e. 3. Age is associated with abnormal HPV and TCT test results: The average interval between carcinogenic HPV infection and cervical cancer progression is 25\u0026ndash;30 years. As age increases, the detection rate of HR-HPV positivity and abnormal TCT results is higher\u003csup\u003e\u003cspan\u003e27\u003c/span\u003e\u003c/sup\u003e. Generally speaking, the prevalence of HPV infection is highest among middle-aged and young women, especially those aged 25 to 35, who are prone to HPV virus infection due to frequent sexual activity. However, due to factors such as low education level, weakened immune function, and changes in hormone levels, the detection rate of SIL disease is higher in older age groups\u003csup\u003e\u003cspan\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1: Comparison of age distribution among different groups [\u003cem\u003en\u003c/em\u003e (%)]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;30\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026gt;30\u0026ndash;40\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026gt;40\u0026ndash;50\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (35.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (42.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (53.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e༒\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e25.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.2.3 Distribution of other factors in each group of patients\u003c/h2\u003e\n \u003cp\u003eThrough the collection and organization of other basic information of each group of patients, we found that compared with the control group, there were no significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the number of pregnancies, smoking history, alcohol consumption history, vaginal microbiota diversity, and microbiota density between the LSIL group and HSIL group patients. However, there were differences in the age of first sexual intercourse and the number of sexual partners (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Through statistical analysis (Table\u0026nbsp;\u0026lt;link rid=\u0026quot;tb6\u0026quot;\u0026gt;\u003cspan\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e\u0026ndash;\u003cspan\u003e2\u003c/span\u003e), we found that age, age of first sexual intercourse, and number of sexual partners are related to the occurrence of SIL, while no significant differences were observed in terms of parity, smoking history, alcohol consumption history, vaginal microbiota diversity, and microbiota density in this study. This study found that there were differences in the age of first sexual intercourse and the number of sexual partners among the three groups through a comparative analysis of basic information between the normal group and the lesion group. The lesion groups (LSIL group and HSIL group) showed characteristics such as premature first sexual intercourse and a large number of sexual partners.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2-\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e2 Distribution of other factors in each group [\u003cem\u003en\u003c/em\u003e (%)]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGravidity and parity history\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInsobriety\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge of first sexual intercourse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNumber of sexual partners\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiversity of vaginal microbiota\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVaginal microbiota density\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026gt;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;20\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026gt;20\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lt;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e+、++++\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e++、+++\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e+、++++\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e++、+++\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (89.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (94.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (68.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (89.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (94.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (94.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (92.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (64.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (35.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (78.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (78.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (92.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (85.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (93.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (73.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (26.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e༒\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*Through comparative analysis, it was found that there were no differences among the three groups of patients in terms of parity, smoking, alcohol consumption, microbial diversity, and microbial density (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but there were differences in age at first sexual intercourse and number of sexual partners(*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.2.4 HPV and TCT test results\u003c/h2\u003e\n \u003cp\u003eBy analyzing the HPV infection typing and TCT examination of patients in the LSIL and HSIL groups, it was found that compared with the LSIL group, the HSIL group had a higher detection frequency of high-risk types 16 and 18 infections (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the detection frequency of other high-risk types of infections in the LSIL group was also relatively higher. In the LSIL group, TCT detection is mostly NILM, while in the HSIL group, the vast majority of patients were found to have significant cytological abnormalities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as shown in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u0026ndash;3. In terms of detection methods and results, compared with LSIL, HSIL patients have a higher infection rate of HPV16 and 18 types and an abnormal rate of TCT test results. The detection rate of HR-HPV types 16 and 18 was higher in the LSIL group than in the HSIL group, except for patients.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2-\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e3 HPV and TCT results [\u003cem\u003en\u003c/em\u003e (%)]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHPV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTCT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh-risk HPV16 and 18 infections\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOther high-risk infections\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNILM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASC-US/LSIL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASC-H/HSIL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (92.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (93.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (46.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e༒\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e2.2.5 Key Gene Expression Levels\u003c/h2\u003e\n \u003cp\u003eThrough experimental analysis, we found that compared with the blank group, the target genes ALK and RPS14 were highly expressed in the HSIL group, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); Compared with the LSIL group, the HSIL group showed high expression of the target gene RPS14 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and significant high expression of the target gene ALK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Compared with the blank group, key signaling pathway indicators PI3K, AKT, NF - \u0026kappa; B, and I \u0026kappa; B - \u0026alpha; showed high expression in the HSIL group, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); Compared with the LSIL group, key signaling pathway indicators PI3K, AKT, NF - \u0026kappa; B, and I \u0026kappa; B - \u0026alpha; were significantly upregulated in the HSIL group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The specific results are shown in Fig.\u0026nbsp;2\u0026ndash;7, and the numerical values are presented in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u0026ndash;4. This experiment directly observed the expression levels of PI3K and AKT in cervical lesions and normal cervical tissues through qRT PCR analysis. The expression level of NF - \u0026kappa; B is positively correlated with the severity of cervical lesions, and in the LSIL stage, the expression level of NF - \u0026kappa; B shows an upward trend. In the HSIL stage, the expression level significantly increases. The expression level of I \u0026kappa; B - \u0026alpha; is low in normal cervical tissue and shows a slight decrease during the LSIL stage. However, in the HSIL stage, I \u0026kappa; B - \u0026alpha; shows a high expression state. This indicates that the inflammatory response pathway may have been activated during the LSIL stage and is significantly activated during the HSIL stage.\u003c/p\u003e\n \u003cp\u003eDuring the experiment, we found that the STK33 gene did not display Ct values, and no values were detected by changing primers or increasing sample concentration. Therefore, we did not display the results and speculated that this may be related to the low content of the STK33 gene in cervical tissue. Although the RPS14 gene can detect numerical values, the Ct value is too high, which may be related to the initial concentration of the template, indicating a low sample size in the template and indirectly reflecting that the RPS14 gene may have a low expression level in cervical tissue. Compared to others, the ALK gene has a more stable expression level in cervical tissue and is easier to detect.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003cdiv\u003eTable 2-4: mRNA expression levels of target genes in each group(\u0026plusmn;s, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3)\u003c/div\u003e\n \u003ctable id=\"Tabg\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLSIL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHSIL\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.65\u0026thinsp;\u0026plusmn;\u0026thinsp;22.68\u003csup\u003e**##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRPS14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003csup\u003e**#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI3K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.40\u003csup\u003e**##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;11.49\u003csup\u003e**##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNF-\u0026kappa;B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.35\u0026thinsp;\u0026plusmn;\u0026thinsp;68.51\u003csup\u003e**##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u0026kappa;B-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.99\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003csup\u003e**##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: Compared with the blank group,\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Compared with LSIL, \u003csup\u003e\u003cem\u003e#\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e2.2.6 Correlation between the severity of cervical lesions and age, vaginal microbiota diversity, microbiota density, expression of key genes and signaling pathway targets\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBy analyzing the correlation between age, microbiota diversity, microbiota density, key genes, and signaling pathway indicators in patients with LSIL and HSIL groups (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u0026ndash;\u003cspan\u003e5\u003c/span\u003e), it was found that the degree of cervical lesions was correlated with age, vaginal microenvironment, ALK, RPS14, and activation of key genes and signaling pathways of PI3K/AKT/NF - \u0026kappa; B (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2-\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e5: HSIL correlation analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpearman\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree of cervical pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaginal secretion flora diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaginal secretion flora density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRPS14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI3K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNF-\u0026kappa;B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u0026kappa;B-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e2.2.7 ROC curve analysis between the severity of cervical lesions and key points\u003c/h2\u003e\n \u003cp\u003eThrough the analysis of the expression of key genes ALK, RPS14, and PI3K/AKT/NF - \u0026kappa; B signaling pathway targets between the Control group and HSIL group, it was found that ALK, RPS14, PI3K, AKT, NF - \u0026kappa; B, and I \u0026kappa; B - \u0026alpha; have high diagnostic value for this disease (Fig.\u0026nbsp;2\u0026ndash;8). Through comparison, it was found that the ALK gene has a higher diagnostic value for this disease than the RPS14 gene (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e\u0026ndash;6).\u003c/p\u003e\n \u003cdiv\u003e \u0026nbsp; \u003c/div\u003e\n \u003cdiv\u003e \u0026nbsp; \u003c/div\u003e\n \u003cdiv\u003eTable 2-6 ROC Results Summary\u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabh\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest result variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI (L)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI (U)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRPS14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI3K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNF-\u0026kappa;B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u0026kappa;B-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eALK (anaplastic lymphoma kinase) is a receptor tyrosine kinase encoded by the ALK gene on the short arm of chromosome 2. This gene plays an important role in the signaling pathway of cell proliferation, maintaining normal cellular homeostasis by regulating cell growth and division\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, when the ALK gene undergoes abnormalities such as copy number increase or rearrangement, its regulatory mechanism may lose control, leading to abnormal cell proliferation and ultimately potentially triggering the development of tumors\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The abnormal expression of the ALK gene has gradually attracted attention in the early-stage lesions of cervical cancer. Although there is relatively little direct evidence for ALK gene rearrangement in cervical cancer, its high frequency in other types of tumors, especially lung cancer, suggests its potential role in tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. It is worth noting that an increase in ALK gene copy number has been found in various gynecological cancers, including ovarian cancer, cervical cancer, and endometrial cancer. Among them, the proportion of ALK gene copy number increase in cervical cancer is 22.22%\u003csup\u003e32\u003c/sup\u003e. This discovery suggests that abnormalities in the ALK gene may be closely related to the progression of precancerous lesions in cervical cancer. Meanwhile, this study found through cervical biopsy of patients with different degrees of cervical lesions that the copy number of the ALK gene showed an increasing trend in the HSIL stage. The abnormal expression of the ALK gene may affect the progression of cervical precancerous lesions through multiple mechanisms. Firstly, amplification of the ALK gene may lead to overexpression of its encoded ALK protein, thereby activating downstream signaling pathways such as MAPK/ERK and PI3K/Akt, which play critical roles in cell proliferation, survival, and migration\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Secondly, abnormalities in the ALK gene may also lead to chromosomal instability and genome rearrangement, further exacerbating abnormal cell proliferation and tumor formation.\u003c/p\u003e \u003cp\u003eThe PI3K/Akt/NF - κ B signaling pathway is an important intracellular signaling pathway involved in regulating various physiological and pathological processes such as cell survival, proliferation, metabolism, angiogenesis, and inflammatory response\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This pathway is centered around phosphatidylinositol 3-kinase (PI3K), which phosphorylates PIP2 to generate PIP3, thereby activating Akt (protein kinase B). The activation of Akt further regulates its downstream effector molecules, including mTOR, NF - κ B, etc., thereby regulating cell growth, differentiation, and apoptosis\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This study found that the PI3K/Akt/NF - κ B signaling pathway is abnormally activated in cervical precancerous lesions. This activation process may be triggered by multiple factors, including HPV infection, stimulation of growth factors, and cytokines. HPV infection is the main cause of cervical cancer, and its encoded E6 and E7 proteins can bind to p53 and Rb proteins, respectively, leading to uncontrolled cell cycle regulation and inhibition of cell apoptosis\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This process may indirectly activate the PI3K/Akt/NF - κ B signaling pathway, promoting abnormal cell proliferation and tumor formation.\u003c/p\u003e \u003cp\u003eAlthough there is relatively little research on the direct interaction between the ALK gene and the PI3K/Akt/NF - κ B signaling pathway in cervical precancerous lesions, based on this study, it can be inferred that abnormalities in the ALK gene may promote the progression of cervical precancerous lesions by activating the PI3K/Akt/NF - κ B signaling pathway. ALK protein, as a receptor tyrosine kinase, may initiate a cascade reaction of the PI3K/Akt/NF - κ B signaling pathway by phosphorylating downstream signaling molecules such as Akt upon activation. This interaction may make the role of the ALK gene in cervical precancerous lesions more complex and diverse.\u003c/p\u003e \u003cp\u003eGiven the important roles of the ALK gene and PI3K/Akt/NF - κ B signaling pathway in cervical precancerous lesions, therapeutic strategies targeting these targets may have potential clinical application value. For example, developing inhibitors targeting ALK genes or targeted drugs targeting key molecules in the PI3K/Akt/NF - κ B signaling pathway may help block abnormal proliferation and survival of tumor cells, thereby achieving the goal of treating precancerous lesions of cervical cancer. In addition, further in-depth research on the specific interaction mechanism between the ALK gene and PI3K/Akt/NF - κ B signaling pathway in cervical precancerous lesions will help us to have a more comprehensive understanding of the pathogenesis of cervical cancer and provide a scientific basis for developing more effective treatment strategies.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn summary, the combination of bioinformatics analysis and human cervical tissue sample testing has also confirmed the activation status of key genes ALK and PI3K/AKT/NF - κ B signaling pathways in this disease. This discovery suggests that ALK genes may play an important role in cervical precancerous lesions. At the same time, amplification of the ALK gene may lead to an increase in the expression level of its encoded ALK protein, which in turn promotes abnormal cell proliferation and survival by activating downstream signaling pathways such as PI3K/Akt/NF - κ B and MAPK/ERK. These abnormal activations are key steps in tumor development and progression. Therefore, abnormalities in the ALK gene may be an important factor in the progression of cervical precancerous lesions.\u003c/p\u003e"},{"header":"5. Materials and Methods","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Patient Collection\u003c/h2\u003e \u003cp\u003eThis experiment adopts a case-control study method for research. Select SIL patients who were admitted to the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine from March 2023 to February 2024 and met the relevant diagnostic criteria. According to the pathological diagnosis of the patients, they were divided into the LSIL group and the HSIL group. At the same time, patients who underwent cervical resection for other benign diseases and underwent cervical HPV testing combined with a liquid-based thin-layer cytology test (TCT) without any abnormalities were collected as the control group. This experimental study was approved by the Ethics Committee of the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine (approval number: HZYLLKY202300701). We promise that all research will be conducted in accordance with the specified regulations. All subjects were informed and consented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Data collection\u003c/h2\u003e \u003cp\u003eWhen selecting a dataset from the GEO database, the following criteria must be met: (1) the data contains tissue samples of HSIL or SIL, (2) Contains technical and platform information for research purposes, (3) Simultaneously including normal cervical tissue as a control study object.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Identify differentially expressed genes\u003c/h2\u003e \u003cp\u003eUsing the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GEO2R online processing tool in the dataset identifies differentially expressed genes (DEGs) and processes the data using the limma R software package. Genes with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Log Fold change (LogFC)\u0026thinsp;\u0026gt;\u0026thinsp;2.0, LogFC\u0026lt;-2.0, or |Log2FC|\u0026gt;1 are selected as differentially expressed genes (DEGs), and the differentially expressed genes shared in the dataset are extracted using Venny plots from the selected DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Construction of protein-protein interaction network (PPI)\u003c/h2\u003e \u003cp\u003eUpload the target to the Uniprot database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Obtain the converted gene name. Then upload the converted gene information to the STRING database for analysis, with the screening criteria being \"Homo Sapiens\" and the remaining criteria being based on default standards. At the same time, Cytoscape 3.9.1 software was used for PPI visualization processing, and the CytoHubba plugin was used to identify the central node genes in the PPI network. Then, the central node genes were selected as candidate DEGs for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis\u003c/h2\u003e \u003cp\u003eUsing the DAVID online website to identify and analyze differentially expressed genes, the identification criteria are based on \"Homo Sapiens\" and \"official gene symbol\", with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered a significant key criterion. Additionally, the online drawing software \"Microbioinformatics\" is utilized(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to Visualize the analysis results of DAVID.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Gene Set Enrichment Analysis (GSEA) predicts key genes\u003c/h2\u003e \u003cp\u003eUpload the gene symbol to the \"Weishengxin\" website, select \"Hallmark gene sets\" for the gene set, and complete the prediction and screening of key genes and targets. At the same time, genes will be enriched and analyzed by the module to identify the most relevant gene enrichment modules for this disease and to clarify the key biological processes that induce this disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.7 qRT-PCR\u003c/h2\u003e \u003cp\u003eTake 70mg of cervical tissue and add 1mL RNAkeyTM Reagent (Beijing Saiwen Innovation Biotechnology Co., Ltd., China) for total RNA extraction. After measuring RNA purity and RNA concentration in each group, perform reverse transcription experiments. The qRT-PCR experiment was conducted using the SYBR Premix ExTaq II kit (TaKaRa Biotechnology, Japan) on the ABILIFE QuantStudio 12K detection system (Foster City, California, USA). The total reaction volume is 20 \u0026micro; L per well, with 3 sub-wells per sample, and the average value is taken for calculation. The primer design sequences for each indicator are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1. Referring to GAPDH, the relative mRNA expression of each group was detected using the comparative Ct (2\u003csup\u003e\u0026minus; Δ Δ CT\u003c/sup\u003e) method. The melting curves of each amp were determined to verify their specificity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 Primer sequence design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence: 5\u0026prime;- 3\u0026prime;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSTK33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GAAAAGTTTCTCCCGGTGCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: TTTATCTGGCTCCCCATCGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRPS14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:AGCTTGTGAAAAATGGCACCTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR:TTCATCCCACCAGTCACAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:CCAGACTAACATGACTCTGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: AGCCTCCCTGGATCTCCATA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:GGACCCGATGCGGTTAGAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR:ATCAAGTGGATGCCCCACAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAKT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:GGACAAGGACGGGCACATTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: CGACCGCACATCATCTCGTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNF-κB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:AATGGGCTACACCGAAGCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR:CTGTCGCAGACACTGTCACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIκB-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:AAGTGATCCGCCAGGTGAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR:CTGCTCACAGGCAAGGTGTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:CTCGCTCCTGGAAGATGGTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR:GCAAAGTAGAAAAGGGCAAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.8 Statistical analyses\u003c/h2\u003e \u003cp\u003eFor the cell studies, the data represent three independent experiments, and all data displays are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) unless otherwise stated. Statistical analyses were performed using SPSS 25.0 software, with one-way ANOVA selected if normal distribution was met, and rank-sum test if not, and Graphpad Prism 9.5.1 software for graphical presentation. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates that the difference is statistically significant, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 indicates that the difference is statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Dr. Zhicheng Wang, who helped improve the scientific quality of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData curation: Yiming Sun, Wenxia Ai\u003c/p\u003e\n\u003cp\u003eFormal analysis: Ding Qi, Buwei Han\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding acquistion: Li Liu, Yonggang Xia\u003c/p\u003e\n\u003cp\u003eWring draft: Ding Qi\u003c/p\u003e\n\u003cp\u003eWriting - editing: Ding Qi, Mingge Liang\u003c/p\u003e\n\u003cp\u003eThis study has obtained informed consent from all participants. The human tissue research involved in this study has been approved by the Ethics Committee of the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine \u0026nbsp;(approval number: HZYLLKY202300701).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability: The datasets used and analysed during the current study available from the corresponding author on reasonable request\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo conflict of interest between authors.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDuggan MA. A review of the natural history of cervical intraepithelial neoplasia. Gan To Kagaku Ryoho. Suppl 1:176-93 (2002). \u003c/li\u003e\n\u003cli\u003eWu B, Xi S. Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer. 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Curr Probl Cancer. \u003cstrong\u003e42\u003c/strong\u003e(2):161-174 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cervical precancerous lesions, ALK, PI3k/Akt/NF-κB, Prognosis and Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-4939442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4939442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aimed to unravel the molecular basis of cervical precancerous lesions leveraging bioinformatic tools to pinpoint crucial genes and signaling cascades. A multi-faceted approach was undertaken, commencing with GEO database mining for differential gene expression between CSILs and healthy cervical tissues. STRING 11.0 facilitated protein-protein interaction (PPI) analysis, generating a network visualized in Cytoscape 3.7.2. Gene Ontology (GO) and KEGG pathway enrichment via DAVID illuminated biological functions and pathways associated with identified differentially expressed genes (DEGs). GSEA further refined key genes and enriched modules. Concurrently, qRT-PCR validation on cervical biopsy samples from eligible patients corroborated bioinformatic findings. The analysis pinpointed 371 common DEGs across datasets, leading to the discovery of 102 biological processes, 33 cellular components, 15 molecular functions, 29 significant pathways, and 3 pivotal genes. Clinical assessment linked lesion severity to age, vaginal microbiota characteristics, and ALK gene/PI3K/AKT/NF-κB pathway activity. qRT-PCR verified heightened ALK and PI3K/AKT/NF-κB signaling in high-grade lesions, underscoring their roles in CSIL pathogenesis. The importance of this research lies in its potential to inform the development of targeted therapies and personalized treatment strategies for cervical precancerous lesions. By identifying the molecular drivers of the disease, researchers can design interventions that precisely target these pathways, improving patient outcomes and reducing the burden of cervical cancer.\u003c/p\u003e","manuscriptTitle":"The role of ALK gene and PI3k/Akt/NF - κ B signaling pathway in precancerous lesions of cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 08:04:28","doi":"10.21203/rs.3.rs-4939442/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":"8d52346f-eb31-4bf8-9e4c-7b94aac0a086","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T11:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-21 08:04:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4939442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4939442","identity":"rs-4939442","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}