Multi-Omics Integrated Analysis of the Protective Effect of Tertiary Lymphoid Structures and Associated Key Regulatory Genes in Human Gallbladder Cancer

Multi-Omics Integrated Analysis of the Protective Effect of Tertiary Lymphoid Structures and Associated Key Regulatory Genes in Human Gallbladder 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 Multi-Omics Integrated Analysis of the Protective Effect of Tertiary Lymphoid Structures and Associated Key Regulatory Genes in Human Gallbladder Cancer Xiaolong Chen, Yimin Nong, Ming Zhang, Chengyou Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6564288/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 Tertiary lymphoid structures (TLS) are lymphoid structures found in non-lymphoid tissues, which are associated with immune cell activation and have been involved in pathogenesis of multiple chronic diseases including tumors. Growing evidence has revealed that TLS are associated with better prognosis and response to immunotherapy in most types of tumors. However, what are their associations with the prognosis and the exact roles of TLS-related genes in gallbladder cancer are still unclear. Here, we investigated the associations of TLS with the prognosis in 85 gallbladder cancer patients. Our results showed that the presence of intra-tumoral TLS was negatively associated with T stage ( P = 0.035) and vascular invasion ( P = 0.048) and predicted a higher rate of overall survival ( P = 0.004) and a decreased risk of early recurrence ( P = 0.002). Moreover, using integrative analysis of single cell RNA sequencing, bulk RNA sequencing, and machine learning, we screened 6 TLS-related genes that potentially involved in the progression of gallbladder cancer, including CTSG, FLNC, CCNB1, HSPB8, NR4A1 and MYLK. Futhermore, through evaluation of biofunctions and clinical significance, we found that CTSG, FLNC, CCNB1 and HSPB8 played an important role in immune infiltration, diagonosis and prognosis of gallbladder cancer. In conclusion, we demonstrated that TLS had a protective effect on human gallbladder cancer, and its related genes, CTSG, FLNC, CCNB1 and HSPB8, played an important role in diagonosis, prognosis, immune infiltration and metastasis of gallbladder cancer. Our findings provided a new insight for the research on clinical biomarkers of gallbladder cancer and development of its therapeutic targets. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Tertiary lymphoid structures gallbladder cancer multi-omics integrated analysis diagonosis and prognosis immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Gallbladder cancer (GBC) is a common malignant gastrointestinal tumor in China, with a constant growth and rejuvenation of incidence rate [ 1 ]. Gallbladder cancer lacks specific clinical manifestations, making early diagnosis extremely difficult, but it progresses rapidly. Most gallbladder cancers are found in the late stage, with a very poor prognosis. Surgery is the only means of curing early-stage gallbladder cancer, but due to the difficulty in early diagnosis, only 20% of patients can be eligible for radical resection [ 2 ]. Radical surgical resection is currently the only potentially curative treatment for gallbladder cancer [ 3 ]. Postoperative pathological characteristics and TNM staging of gallbladder cancer can help assess the prognosis and guide treatment strategies [ 4 ]. However, the recurrence rate after surgery is relatively high, and recurrent GBC patients displayed a quite low survival rate. It is urgent to explore biomarkers that can accurately predict the prognosis and the risk of postoperative recurrence in GBC patients. Tertiary lymphoid structures (TLS), also known as ectopic lymphoid aggregates, are lymphoid structures found in non-lymphoid tissues that can drive immune cell activation, and their formation is associated with chronic diseases, such as chronic inflammatory diseases, autoimmune diseases, and tumors [ 5 , 6 ]. As an important feature of anti-tumor immune response, numerous studies have shown that TLS are related to superior prognosis of patients with various tumors, including pancreatic cancer, melanoma, non-small cell lung cancer, hepatocellular carcinoma, and colorectal cancer [ 5 , 7 – 10 ]. Currently, there are few studies on TLS in gallbladder cancer, and their role in gallbladder cancer is still unclear. In this study, the association of TLS with the clinical features, prognosis and recurrence of GBC were investigated in 85 patients. We found that the presence of intra-tumoral TLS was negatively associated with T stage and vascular invasion and predicted a higher rate of overall survival and a decreased risk of early recurrence. Moreover, we screened potential key regulators of gallbladder cancer by integrative analyzing datasets from single cell RNA sequencing, bulk RNA sequencing, and machine learning from multiple databases, which provides a new insight for the research on prognosis of gallbladder cancer and development of therapeutic targets. Material and methods Patients and samples A total of two 210 patients were followed up in this study, who underwent their first HCC resection in the First Affiliated Hospital of Chongqing Medical University (Chongqing, China) from 2014 to 2022. 125 patients were excluded through the criteria (transarterial chemotherapy and embolization, immunotherapy and other anticancer treatments; and preoperative tumor rupture or distant metastasis). And 85 patients were ultimately enrolled. Their following clinical and biological features were recorded, including age, gender, T stage, N stage, TNM stage, tumor differentiation degree, vascular invasion and perineural invasion The pathologic tumor type was diagnosed by three pathologists after surgical excision. Recurrence within 2 years of surgical resection was considered early recurrence because such cases are considered metastasis of the original tumor after resection rather than a new tumor. This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Approval number: 2024-486-01). Informed consent was obtained from all participants and/or their legal guardians. Research involving human research participants performed in accordance with the Declaration of Helsinki. Pathological examination Formalin-fixed and paraffin-embedded samples were sectioned continuously at a thickness of 4 µm by Servicebio (Wuhan, China). Hematoxylin-eosin (HE) staining was used to verify the presence of TLSs, according to the detailed staining procedures described in a previous study [ 11 ]. At least one TLS was confirmed positive in each section. This process was performed by three individual pathologists. According to maturity, the TLSs were classified as: aggregates (tumor samples show only aggregates but no primary follicles) and primary follicles (tumor sample show at least one primary follicle or germinal center, with or without aggregates). Slides were scanned using a Nanozoomer scanner (Hamamatsu, Hirakuchi, Japan), and the surfaces of tissue areas were calculated using Nanozoomer Digital Pathology View software. Single cell RNA sequencing analysis for primary and metastatic focuses of GBC patients Single cell RNA sequencing data from primary and metastatic focuses of GBC patients were downloaded from the GEO database (dataset: GSE201425). The expression matrix was analyzed with Seurat (v 4.2.0) to filter out low-quality cells, with the following parameters: Min.Cells = 3, novelty score (log 10 GenesPerUMI: average number of genes corresponding to each UMI) > 0.8, UMI > 500, nGene (number of genes expressed greater than 0) > 200, mitoRatio (proportion of mitochondrial expressed genes) < 0.1. The R package Seurat (v 4.2.0) was used for low-quality cell filtering, standardization, data integration, PCA linear clustering, UMAP dimensionality reduction clustering, tSNE dimensionality reduction clustering, identification of marker genes and conservative marker genes [ 11 ]; Cell types were identified based on marker genes in the Cell Taxonomy database, and ClusterProfiler was used to perform GO and pathway enrichment analysis on marker genes and conserved marker genes [ 12 ]. Data normalization and scaling were performed using the SCTransform function, with mitochondrial gene counts regressed out to eliminate potential biases. Subsequently, SelectIntegrationFeatures, PrepSCTIntegration, and FindIntegrationAnchors functions were utilized to identify highly variable features across cells and to establish "anchors" for integrating individual datasets. This integration process culminated in the creation of an "unbatched" dataset through the IntegrateData function, effectively removing batch effects and enabling combined analysis of all cells. Principal Component Analysis (PCA) was conducted on this integrated dataset using the RunPCA function, which focused on the scaled and transformed data of identified variable genes. For visualization, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) were applied using the RunTSNE and RunUMAP functions with top 30 principal components (PCs), respectively. These techniques facilitated the exploration of the dataset's underlying structure by projecting the high-dimensional data into two dimensions. Cell clustering was performed using the FindNeighbors and FindClusters functions from Seurat, initially setting the resolution to 1.5 to identify major cell types. Following cell clustering, we primarily identified differentially expressed genes in specific clusters compared to the others using the FindConservedMarkers function with only.pos = TRUE and logfc. threshold > 0.1 as the thresholds, which facilitated the identification of characteristic markers for each cluster. Conserved marker genes were analyzed using clusterProfiler to enrich for over-represented biological processes and pathways, enhancing our understanding of the functional implications of gene expression patterns within each cluster. Based on the marker genes in the Cell Taxonomy databases, these clusters were manually annotated based on their marker profiles. To detect (DEGs, referred as scRNA-seq DEGs) across groups, we employed the FindMarkers with P 0.25 and minimum percentage (min.pct) > 0.1 as the thresholds. The differentially expressed genes were conducted to enrich for over-represented biological processes and pathways using clusterProfilers. A volcano plot was created using ggplot2, and GO and KEGG enrichment analyses were performed on differentially expressed genes using clusterProfiler. Multi-omics integrated analysis and deep machine learning The GEO dataset (GSE138109) containing RNA-seq data from 20 paired human GBC normal and cancer tissues were downloaded and was re-analyzed with the R package limma. A |log 2 (fold change)| ≥ 2 and P < 0.05 were used as the thresholds to determine whether the RNA transcripts were upregulated or downregulated. Volcano plots and heatmaps showing DEGs (GEO-DEGs) were generated with R soft and related Bioconductor packages. Then, 7728 TLS-related genes were obtained from the online database GeneCards ( https://www.genecards.org/Search/Keyword?queryString= tertiary%20lymphoid%20structure). The scRNA-seq DEGs, GeneCards-TLS related genes, and GEO-DEGs were intersected to screen potential TLS-related regulators in GBC progression. The intersection was applied in deep machine learning by using SVM-RFE, RandomForest and Lasso for further screening. Ultimately, the output genes from machine learning were applied in single gene analysis in depth, including Immune infiltration, Clinical Prognosis, and multiple therapy responses. Statistical analysis The data were presented as the mean ± SD. Student’s t test and one-way ANOVA followed by Tukey’s post hoc test were used to verify the comparison among groups by using the SPSS 22.0 software. P < 0.05 was considered statistically significant. Results The presence of intra-tumoral TLS was associated with superior prognosis of GBC patients A total of 85 GBC patients were eventually enrolled in this study. Their following clinical and biological features were recorded, including age, gender, T stage, N stage, TNM stage, tumor differentiation degree, vascular invasion and perineural invasion The pathologic tumor type was diagnosed by three pathologists after surgical excision. Patients were female and older than 60 years in 75.3% and 51.8% of the cases, respectively (Table 1 ). The main risk factors were T stage, N status and TNM stage, differentiation, vascular invasion, perineural invasion. Pathological examination identified TLS in 30 tumors (35.3%) (Table 1 ). Among GBC with TLS (TLS + GBC), the maximum degree of TLS maturation was Agg, FL-I and FL-II in 10 (33.3%), 13(43.3%) and 7 (23.3%) cases, respectively (Fig. 1 ). Table 1 Clinical and biological features according to the presence of intra-tumoral TLS. Characteristics Overall, N = 85 TLS + GBC TLS − GBC p -value Age < 60 41(48.2%) 16(53.3%) 25(45.5%) 0.487 ≥ 60 44(51.8%) 14(46.7%) 30(54.5%) Gender Male 21(24.7%) 7(23.3%) 14(25.5%) 0.758 Female 64(75.3%) 23(76.7%) 41(74.5%) T stage 0.035 * T1/T2 59(69.4%) 25(73.5%) 34(66.7%) T3/T4 26(31.6%) 5(26.5%) 21(23.3%) N status N0 59(69.4%) 23(76.7%) 36(65.4%) 0.284 N1/N2 26(31.6%) 7(23.3%) 19(35.6%) TNM stage Ⅰ/Ⅱ 47(55.3%) 20(66.7%) 27(49.0%) 0.119 Ⅲ/Ⅳ 38(44.7%) 10(33.3%) 28(51.0%) Differentiation Well or moderately 50(58.8%) 18(60.0%) 32(58.2%) 0.871 Poorly or undifferentiated 35(41.2%) 12(40.0%) 23(41.8%) Vascular invasion Negative 78(92.9%) 30(100.0%) 48(88.2%) 0.048 * Positive 7(7.1%) 0(0.0%) 7(11.8%) Perineural invasion Negative 77(90.6%) 29(96.7%) 48(87.3%) 0.156 Positive 8(9.4%) 1(3.3%) 7(12.7%) Notes : TLS: Tertiary lymphoid structure; TNM: tumor-node-metastasis. Apart from an association with T stage (P = 0.035, chi-squared test), and vascular invasion (P = 0.048), TLS + GBC was not linked to any other clinical, or biological feature (Table 1 ). Interestingly, no differences were observed according to the status of age (P = 0.487), gender (P = 0.785), N status (P = 0.284), TNM stage (P = 0.119), differentiation (P = 0.871), and perineural invasion (P = 0.156) (Table 1 ). Altogether, these findings suggested that T stage and vascular invasion had impacts on the existence of intra-tumoral TLS. Features associated with an increased risk of overall survival in univariate analysis were T stage (hazard ratio [HR] = 2.334, P = 0.010), N stage (HR = 2.971, P = 0.001), TNM stage (HR = 2.799, P = 0.002) (Table 2 ). TLS (HR = 0.300; P = 0.004) was related to a decreased risk of overall survival (Table 2 and Fig. 2 ). The prognostic value of TLS was retained after multivariate analysis (HR = 0.320, P = 0.007) (Table 2 and Fig. 2 ). Features associated an increased risk of early recurrence in univariate analysis were gender (HR = 2.764, P = 0.003), T stage (HR = 3.277, P = 0.010) ,TNM stage (HR = 3.899, P = 0.000), and perineural invasion (HR = 3.506, P = 0.003) (Table 3 and Fig. 2 ).While feature associated an decreased risk of early recurrence in univariate analysis was TLS (HR = 0.264, P = 0.006) (Table 3 and Fig. 2 ). Moreover, the prognostic value of TLS was consistent after multivariate analysis (HR = 0.290, P = 0.006) (Table 3 and Fig. 2 ). Table 2 Univariate and multivariate analysis of GBC overall survival Variables Univariate analysis Multivariate analysis HR (CI 95%) p- value HR (CI 95%) p- value Age (< 60, ≥ 60) 1.815(0.946–3.482) 0.073 Gender (female, male) 2.132(1.101–4.129) 0.025* T stage (T1/T2, T3/T4) 2.334(1.229–4.433) 0.010* N status (N0, N1/N2) 2.971(1.564–5.645) 0.001** 3.010(1.578–5.740) 0.001** TNM stage (Ⅰ-Ⅱ, Ⅲ-Ⅳ) 2.799(1.443–5.429) 0.002** Differentiation (well or moderately, poorly) 1.363(0.718–2.586) 0.344 Vascular invasion (+, -) 2.364(0.919–6.079) 0.074 Perineural invasion (+, -) 2.144(0.834–5.512) 0.113 TLS (+, -) 0.320(0.141–0.728) 0.007** 0.317(0.139–0.721) 0.006** Notes : TLS: Tertiary lymphoid structure; HR: Hazard ratio. Table 3 Univariate and multivariate analysis of early GBC recurrence Variables Univariate analysis Multivariate analysis HR (CI 95%) p- value HR (CI 95%) p- value Age (< 60, ≥ 60) 1.372(0.711–2.650) 0.073 Gender (female, male) 2.764(1.410–5.346) 0.025* 2.007(0.999–4.028) 0.05 T stage (T1/T2, T3/T4) 3.277(1.696–6.330) 0.010* 2.188(1.088–4.402) 0.028* N status (N0, N1/N2) 3.263(1.689–6.303) 0.001** 2.108(1.031–4.311) 0.041* TNM stage (Ⅰ-Ⅱ, Ⅲ-Ⅳ) 3.899(1.913–7.950) 0.002** Differentiation (well or moderately, poorly) 1.795(0.933–3.454) 0.344 Vascular invasion (+, -) 2.960(1.139–7.695) 0.026* Perineural invasion (+, -) 3.506(1.526–8.502) 0.003** TLS (+, -) 0.264(0.110–0.636) 0.003** 0.290(0.119–0.705) 0.006** Notes : TLS: Tertiary lymphoid structure; HR: Hazard ratio. Several subgroups of T, B and macrophages were reduced in GBC metastatic focus Single cell RNA sequencing data from primary and metastatic focuses of GBC patients (GSE201425) were analyzed to compare the differences in heterogeneity and immune cell composition between primary and metastatic focuses of GBC patients. Our results of tSNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) showed that all cells could be divided into 33 cell clusters (0–32, Fig. 3 A and Supplementary Fig. 1A). Group comparison indicated that, compared with primary GBC focuses, metastatic focuses displayed a relatively smaller cell population in a few cell clusters, especially clusters 2, 3, 8, 9, 12, 16, 21 and 23 (Fig. 3 B and Supplementary Fig. 1B). Cell cycle stage comparison revealed that more cells were in the G1 or S phases than the G2 phase, suggesting the vigorous growth and rapid cell division in GBC focuses (Fig. 3 C and Supplementary Fig. 1C). Moreover, based on the conserved markers, these 33 clusters could be attributed to 13 cell types, including B cells, cancer associated fibroblasts (CAFs), Dendritic cells (DCs), Endothelial cells (ECs), Epithelial cells, Macrophages, Mast cells, Monocytes, Neutrophils, Natural killer (NK) cells, Plasma cells, Plasmacytoid DCs and T cells (Fig. 3 D and Supplementary Fig. 1D). As expected, clusters 3, 8, 12, 16 and 23, which belonged to of CAFs and epithelial cells and resident cells of the gallbladder tissue, were distinctly reduced in the metastatic focus (Fig. 3 B and Supplementary Fig. 1B). Moreover, as important cell components of TLS, clusters 2, 9 and 21, respectively belonging to T cells, macrophages and plasma cells, were also markedly reduced in the metastatic focus (Fig. 3 B and Supplementary Fig. 1B), suggesting a reduced tendency in cell components of TLS formation in metastatic GBC focus. Furthermore, conserved marker analysis revealed that the clusters 2, 9 and 21 could be defined as NELL2 + ZNF683 + CD8 + T cells (Fig. 4 A), GAL3ST4 + CD163 + macrophages (Fig. 4 B), and IGHG1 + IGHG4 + plasma cells (Fig. 4 C). GO and KEGG analyses suggested that they were significantly associated with T cell activation, protein binding and cell phagocytosis, and enriched in corresponding pathways (Supplementary Fig. 2). Multi-omics integrated analysis screened 6 candidate TLS-related genes in GBC Then, GEO dataset (GSE138109) containing RNA-seq data from 20 paired human GBC normal and cancer tissues were analyzed with the R package limma. Our results showed that there were 322 downregulated and 91 upregulated DEGs (Fig. 5 A). GO analysis indicated that these 413 DEGs were significantly associated with extracellular matrix metabolism, blood production and coagulation, biofilm fusion and disintegration, and multiple protein binding responses (Fig. 5 B), which are key biological processes in tumor invasion and metastasis. KEGG pathway annotation also indicated that these DEGs were enriched in multiple pathways related to immune responses and cell-cell interaction, such as cytoskeleton, neutrophil trap, ECM-receptor interaction and focal adhesion (Fig. 5 C), which are also key biological processes in tumor cell movement and adhesion Subsequently, scRNA-seq DEGs, GeneCards-TLS related genes, and GEO-DEGs were intersected to screen potential TLS-related regulators in GBC progression. 165 intersection genes were screened and applied in the following deep machine learning (Fig. 6 ). Our results showed that we screened 6 TLS-related genes, including Cathepsin G (CTSG), filamin C (FLNC), cyclin B1 (CCNB1), heat shock protein family B member 8 (HSPB8), nuclear receptor subfamily 4,group A-1 (NR4A1) and myosin light polypeptide kinase (MYLK), which might be involved in the progression of GBC (Figs. 6 and 7 ). CTSG, FLNC, CCNB1 and HSPB8 played important roles in diagnosis and prognosis in GBC Then, their associations with the immune infiltration, diagnosis and prognosis of gallbladder cancer were evaluated by single gene bioinformatic analysis in depth. Boxplots revealed that CTSG, FLNC, CCNB1 (down), HSPB8, NR4A1 and MYLK showed distinct expression levels in Normal and GBC tissues (Fig. 8 A). Immune infiltration assay showed that CTSG, FLNC, CCNB1, HSPB8 and NR4A1 were significantly associated with most key cell types in tumor immune (Fig. 8 B and C), the interaction s of which are relatively strong (Fig. 8 D). Protein-protein interaction network indicated that they participated in networks related to cell cycle, extracellular matrix metabolism, oxidative stress and cell adhesion and multiple kinases associated with cell survival and invasion (Fig. 9 ). Gene Set Enrichment Analysis (GSEA) also showed that they were enriched in pathways associated with cell cycle and multiple kinases associated with cell survival and invasion (Fig. 10 A and Supplementary Fig. 3). Finally, their values in diagnosis and prognosis of gallbladder cancer were assessed. Our results indicated CTSG, FLNC, CCNB1, HSPB8, NR4A1 and MYLK all displayed high diagnostic values (Fig. 10 B), while CTSG, FLNC, CCNB1, and HSPB8 were significantly related to the survival rates of GBC patients (Fig. 10 C). Discussion The presence of TLSs has been associated with clinical benefit in patients with multiple types of cancer, whereas, the significance of TLSs in gallbladder cancer patients was still largely unknown. In this study, we found that the presence of intra-tumoral TLS was negatively associated with T stage and vascular invasion and predicted a higher rate of overall survival and a decreased risk of early recurrence in 85 GBC patients. Moreover, multi-omics integrated analysis screened CTSG, FLNC, CCNB1, and HSPB8 as potential key regulators in immune infiltration, diagonosis and prognosis of GBC. TLSs, also known as ectopic lymphoid structures, form at the site of inflammation in which lymphoid tissue undergoes extra-nodal seeding, under sustained chronic pathological conditions. Its formation process is similar to the formation of secondary lymphoid organs during embryonic development, under the interaction between hematopoietic cells and non lymphoid stromal cells, as well as the induction of molecular components such as cytokines, chemokines, adhesion molecules, and survival factors [ 13 ]. TLSs are mainly composed of T cells and B cells: the central region of the TLS is mainly composed of CD20 + B cells, and various mature CD3 + T cells surround the periphery of B cells to form a lymphoid like structure [ 14 ]. In addition to the main T cells and B cells, there are also other subgroups of dendritic cells and macrophages in TLS, such as CD83 + mature dendritic cells associated with lysosomal membrane proteins mainly localized in the T cell region, CD68 + macrophages that are used to clear apoptotic cells, and IgG-producing plasma cells that sustained B cell maturation and antibody production[ 15 ]. In this study, our single-cell RNA sequencing analysis indicated that compared with primary GBC focuses, metastatic focuses displayed a relatively smaller cell population in a few cell clusters, especially clusters 2 (NELL2 + ZNF683 + CD8+), 9 (GAL3ST4 + CD163 + ), and 21 (IGHG1 + IGHG4 + ), which respectively belonged to T cells, macrophages and plasma cells. These findings suggested a reduced tendency in cell components of TLS formation in metastatic GBC focus. Moreover, GO and KEGG analyses suggested that they were significantly associated with T cell activation, protein binding and cell phagocytosis, and enriched in corresponding pathways. CTSG gene encodes the cathepsin G protein, a member of the serine protease family. CTSG is mainly expressed in neutrophils, monocytes, myeloid DCs, and plasmacytoid DCs (pDCs), which are common cellular components of TLSs [ 16 , 17 ]. Upon activation, CTSG was expressed and inhibited malignant behaviors by autocrine or paracrine means in human cancers, such as breast, oral squamous cell, and colon cancer [ 18 – 20 ]. However, there has been no report revealing the exact role of CTSG in GBC progression. In this study, CTSG was screened as a biomarker in TLS formation in GBC by integrative analysis of single-cell RNA sequencing, bulk RNA sequencing, TCGA data and TLS-related genes from Genecards combined with machine learning. Boxplots revealed that it showed a distinctly decreased expression level in GBC tissues compared with the normal, immune infiltration assay showed that it was significantly associated with most key cell types in tumor immune, protein-protein interaction network and GSEA assays indicated that it participated in networks related to extracellular matrix metabolism, cell adhesion and multiple signalings associated with cell survival and invasion, and it was positively related to the survival rates of GBC patients, suggesting it a tumor suppressor role in GBC. FLNC is a member of large dimeric actin-binding protein family FLNs, which dominate the remodeling of actin cytoskeleton and also serve as scaffolds for signaling proteins, such as tyrosine kinases, GTPases, or phosphatases. FLNC was reported as a biomarker and therapeutic target in some types of cancer, including gastric cancer, rectal cancer, lung cancer and glioblastoma[ 21 – 24 ]. FLNC, directly bound and degraded by TRIM54, a proto-oncogene in gastric cancer (GC), efficiently inhibited GC progression [ 21 ], while, in glioblastoma, low-frequency repetitive transcranial magnetic stimulation inhibited ERK/JNK/p38 and PI3K/AKT/mTOR pathways and induced apoptosis by suppressing the expression of FLNC [ 24 ]. However, there were rare reports revealing the exact role of FLNC in GBC progression. In this study, FLNC was screened as a biomarker in TLS formation in GBC by multi-omics integrated analysis combined with machine learning. Boxplots revealed that it was significantly downregulated in GBC tissues, immune infiltration assay showed that it was significantly associated with most key cell types in tumor immune, protein-protein interaction network and GSEA assays indicated that it participated in multiple signalings associated with cell survival and invasion, and it was negatively related to the survival rates of GBC patients. As an important member of cyclins, CCNB1 plays an pivotal role in mitosis initiation, which confers it a promoting role in cell survival, invasion and drug resistance in many tumors, including gallbladder cancer[ 25 , 26 ]. In this study, CCNB1 was screened as a biomarker in TLS formation in GBC by multi-omics integrated analysis combined with machine learning. It was significantly upregulated in GBC tissues, significantly associated with most key cell types in tumor immune, and involved in multiple kinases associated with cell cycle, cell stress and invasion, and it was negatively related to the survival rates of GBC patients, indicating that CCNB1 acts as proto-oncogene and therapeutic target in GBC. Consistent with our results, many studies have indicated that CCNB1 was upregulated in GBC and inhibition of CCNB1 was abled to suppress GBC progression [ 27 – 29 ]. HSPB8 is a chaperone molecule involved in autophagy that promotes selective degradation of proteins to counteract cell stress in normal and tumor cells [ 30 , 31 ]. Accumulating evidence has indicated that HSPB8 is associated with chromosome segregation and G0/G1cell cycle arrest and dendritic cell maturation and cytokine production, which confers it an important role in regulating the tumorigenesis, tumor progression and drug resistance [ 32 – 34 ]. However, there were rare reports revealing the exact role of FLNC in GBC progression. In this study, HSPB8 was screened as a biomarker in TLS formation in GBC by multi-omics integrated analysis combined with machine learning. It was significantly downregulated in GBC tissues, significantly associated with most key cell types in tumor immune, and involved in multiple molecular chaperones associated with cell survival, invasion and drug resistance, and it was negatively related to the survival rates of GBC patients. There were some limitations in our current study. First, due to the limited amount of data on gallbladder cancer in the public databases, the sample size applied in our multi-omics analysis is not large, limits our in-depth analysis of some clinical indicators. Second, due to limited time, funding and laboratory conditions, we did not conduct cellular and animal experiments to validate the role of the screened candidate genes in TLS formation and gallbladder progression. Our next plan is to investigate the role of CTSG in TLS formation and gallbladder progression using cellular and animal models. In conclusion, the presence of intra-tumoral TLS was negatively associated with T stage and vascular invasion and predicted a higher rate of overall survival and a decreased risk of early recurrence in GBC patients. TLS-related genes CTSG, FLNC, CCNB1, and HSPB8 are potential key regulators in immune infiltration, diagonosis and prognosis of GBC. Declarations Acknowledgements Not applicable. Author contributions Xiaolong Chen: Investigation, bioinformatics analysis, and Writing-original draft; Yimin Nong: figure/table preparation, Data curation and Writing-Review&Editing; Ming Zhang: Software, Resources and Writing-Review&Editing; Chengyou Du: Conceptualization, Validation, Supervision and Writing-Review&Editing. Data Availability The datasets generated in the current study are available from the corresponding author upon reasonable request or downloaded from the following web links: GSE138109 (https://www.ncbi.nlm.nih.gov/gds/?term=GSE138109), GSE201425 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201425), Genecards-TLS (https://www.genecards.org/Search/Keyword?queryString=Tertiary%20lymphoid%20structure) and SEER (https://seer.cancer.gov/). Conflict of interest The authors declare that they have no competing interests. Ethical approval This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Approval number: 2024-486-01). Informed consent was obtained from all participants and/or their legal guardians. Research involving human research participants performed in accordance with the Declaration of Helsinki. Consent to participate All the subjects enrolled in this study were fully informed and gave informed consent. 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Kuo, Repression of CTSG, ELANE and PRTN3-mediated histone H3 proteolytic cleavage promotes monocyte-to-macrophage differentiation, Nat Immunol, 22 (2021) 711-722. T.J. Wilson, K.C. Nannuru, M. Futakuchi, R.K. Singh, Cathepsin G-mediated enhanced TGF-beta signaling promotes angiogenesis via upregulation of VEGF and MCP-1, Cancer Lett, 288 (2010) 162-169. G.Z. Huang, Q.Q. Wu, Z.N. Zheng, T.R. Shao, F. Li, X.Y. Lu, H.Y. Ye, G.X. Chen, Y.X. Song, W.S. Zeng, Y.L. Ai, X.Z. Lv, Bioinformatics Analyses Indicate That Cathepsin G (CTSG) is a Potential Immune-Related Biomarker in Oral Squamous Cell Carcinoma (OSCC), Onco Targets Ther, 14 (2021) 1275-1289. B. Shao, M.G. Wahrenbrock, L. Yao, T. David, S.R. Coughlin, L. Xia, A. Varki, R.P. McEver, Carcinoma mucins trigger reciprocal activation of platelets and neutrophils in a murine model of Trousseau syndrome, Blood, 118 (2011) 4015-4023. H. Cao, Y. Li, L. Chen, Z. Lu, T. You, X. Wang, B. Ji, Tripartite motif-containing 54 promotes gastric cancer progression by upregulating K63-linked ubiquitination of filamin C, Asia Pac J Clin Oncol, 18 (2022) 669-677. J. Yang, Z. Cao, C. Yu, W. Cui, J. Zhou, Identification of a mitophagy-related gene signature for predicting overall survival and response to immunotherapy in rectal cancer, BMC Cancer, 25 (2025) 15. Y. Zhang, F. Chen, Y. Cao, H. Zhang, L. Zhao, Y. Xu, Identifying diagnostic markers and establishing prognostic model for lung cancer based on lung cancer-derived exosomal genes, Cancer Biomark, 42 (2025) 18758592251317400. S. Jo, S.H. Im, S.H. Kim, D. Baek, J.K. Shim, S.G. Kang, J.H. Chang, G.R. Notario, D.W. Lee, A. Baek, S.R. Cho, Tumor suppressive effect of low-frequency repetitive transcranial magnetic stimulation on glioblastoma progression, Neurotherapeutics, (2025) e00569. B. Xie, S. Wang, N. Jiang, J.J. Li, Cyclin B1/CDK1-regulated mitochondrial bioenergetics in cell cycle progression and tumor resistance, Cancer Lett, 443 (2019) 56-66. Y.J. Shu, R.F. Bao, X.S. Wu, H. Weng, Q. Ding, Y. Cao, M.L. Li, J.S. Mu, W.G. Wu, Q.C. Ding, T.Y. Liu, L. Jiang, Y.P. Hu, Z.J. Tan, P. Wang, Y.B. Liu, Baicalin induces apoptosis of gallbladder carcinoma cells in vitro via a mitochondrial-mediated pathway and suppresses tumor growth in vivo, Anticancer Agents Med Chem, 14 (2014) 1136-1145. C.S. Huang, Q.C. Xu, C. Dai, L. Wang, Y.C. Tien, F. Li, Q. Su, X.T. Huang, J. Wu, W. Zhao, X.Y. Yin, Nanomaterial-Facilitated Cyclin-Dependent Kinase 7 Inhibition Suppresses Gallbladder Cancer Progression via Targeting Transcriptional Addiction, ACS Nano, 15 (2021) 14744-14755. M. Wencong, W. Jinghan, Y. Yong, A. Jianyang, L. Bin, C. Qingbao, L. Chen, J. Xiaoqing, FOXK1 Promotes Proliferation and Metastasis of Gallbladder Cancer by Activating AKT/mTOR Signaling Pathway, Front Oncol, 10 (2020) 545. J. Brägelmann, C. Barahona Ponce, K. Marcelain, S. Roessler, B. Goeppert, I. Gallegos, A. Colombo, V. Sanhueza, E. Morales, M.T. Rivera, G. de Toro, A. Ortega, B. Müller, F. Gabler, D. Scherer, M. Waldenberger, E. Reischl, F. Boekstegers, V. Garate-Calderon, S.U. Umu, T.B. Rounge, O. Popanda, J. Lorenzo Bermejo, Epigenome-Wide Analysis of Methylation Changes in the Sequence of Gallstone Disease, Dysplasia, and Gallbladder Cancer, Hepatology, 73 (2021) 2293-2310. R. Cristofani, M. Piccolella, V. Crippa, B. Tedesco, M. Montagnani Marelli, A. Poletti, R.M. Moretti, The Role of HSPB8, a Component of the Chaperone-Assisted Selective Autophagy Machinery, in Cancer, Cells, 10 (2021). F. Li, H. Xiao, Z. Hu, F. Zhou, B. Yang, Exploring the multifaceted roles of heat shock protein B8 (HSPB8) in diseases, Eur J Cell Biol, 97 (2018) 216-229. A.A. Varlet, M. Fuchs, C. Luthold, H. Lambert, J. Landry, J.N. Lavoie, Fine-tuning of actin dynamics by the HSPB8-BAG3 chaperone complex facilitates cytokinesis and contributes to its impact on cell division, Cell Stress Chaperones, 22 (2017) 553-567. L. Aurelian, J.M. Laing, K.S. Lee, H11/HspB8 and Its Herpes Simplex Virus Type 2 Homologue ICP10PK Share Functions That Regulate Cell Life/Death Decisions and Human Disease, Autoimmune Dis, 2012 (2012) 395329. J.J. Shi, S.M. Chen, C.L. Guo, Y.X. Li, J. Ding, L.H. Meng, The mTOR inhibitor AZD8055 overcomes tamoxifen resistance in breast cancer cells by down-regulating HSPB8, Acta Pharmacol Sin, 39 (2018) 1338-1346. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx 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. 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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-6564288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466133519,"identity":"cc84e007-6927-4d04-ac05-7247691fc627","order_by":0,"name":"Xiaolong Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Chen","suffix":""},{"id":466133520,"identity":"bf0230be-bed5-4e7d-93da-4ed90b7bbbd0","order_by":1,"name":"Yimin Nong","email":"","orcid":"","institution":"Shenzhen Baoan Women's and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Nong","suffix":""},{"id":466133521,"identity":"a83dfb94-eff7-4c2c-9fef-9a024e2c0c59","order_by":2,"name":"Ming Zhang","email":"","orcid":"","institution":"The First People's Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhang","suffix":""},{"id":466133522,"identity":"8ab0fe17-6754-4941-ae9e-33bf2020b48a","order_by":3,"name":"Chengyou Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYJCCDzwgkr2x8eEHInUwzgBr4TncbCxBmhaJ9DYBHmLUm0sfPtjwpuawnMHNh20MEgx2croNBLRY9qUlNs45dtjY4HZi24MChmRjswMEtBic4TF/zNtwOHHD7cR2AwmGA4nbiNBi2AzWcvNgmwQPaVpuMBKpxbKHDeSXdGPJM4nAQDYgwi/mPMygELOW4zt+/OHDDxV2coS9D6GaUbnEaKkjQukoGAWjYBSMWAAAVG5GAptYZQcAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chengyou","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-04-30 10:53:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6564288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6564288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84214673,"identity":"866eac3d-0140-4627-99d5-527313863e93","added_by":"auto","created_at":"2025-06-09 10:32:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11793017,"visible":true,"origin":"","legend":"\u003cp\u003eHistological appearance of intra-tumoral TLS. (A) Aggregates are vague, ill-defined clusters of lymphocytes. (B) Primary follicles consist of dense, round or oval shaped clusters of lymphocytes. (C)Secondary follicles are centered by a germinal center.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/3143031edf3153b052e06f29.png"},{"id":84214666,"identity":"f83ead8d-849c-4c1e-8996-968777c5ff27","added_by":"auto","created_at":"2025-06-09 10:32:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":465251,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of tertiary lymphoid structures on tumor overall survival both early recurrences. (A) Patients with intra-tumor TLSs display a lower risk of overall survival in GBC. (B) TLSs have an impact on the risk of early tumor recurrence. GBC, gallbladder carcinoma; TLS, tertiary lymphoid structure. Cox proportional hazard regression.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/6eecf2a176aafc3cba4384e7.png"},{"id":84216743,"identity":"be910936-e045-44d5-bc26-695ed33c81f7","added_by":"auto","created_at":"2025-06-09 10:48:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6848400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle cell RNA sequencing analysis for the heterogeneity and distinct subgroups between primary and metastatic focuses of GBC patients. \u003c/strong\u003etSNE dimensionality reduction clustering analysis in (A) overall samples, (B) different groups, (C) different cell cycle phases, and (D) cell types in different groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/c548c31715b4b7c58a924e1f.png"},{"id":84216171,"identity":"d9c171f1-b506-4155-a1f2-c4374d118e74","added_by":"auto","created_at":"2025-06-09 10:40:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1766220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConserved marker genes of clusters 2 (A), 9 (B) and 21 (C).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/19c312f6864531143f4504be.png"},{"id":84216175,"identity":"36b3e794-8db3-4006-a3e6-51708e562338","added_by":"auto","created_at":"2025-06-09 10:40:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1552096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhole transcriptome analysis of paired normal and tumor tissues from 20 GBC patients. \u003c/strong\u003eA. Volcano plots for the differentially expressed genes (DEGs) between normal and tumor GBC tissues. B. GO analysis for the biological processes, cell components and molecular functions of the DEGs. C.KEGG analysis for the signaling pathways that the DEGs participated in.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/d295f7fdb059ba4c4eb9ad27.png"},{"id":84214678,"identity":"509d0469-b963-45d7-893b-e0edd303250d","added_by":"auto","created_at":"2025-06-09 10:32:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1326608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flow chart of multi-omics integrated analysis and deep machine learning.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/2d0b647fc88cc5d0256e4beb.png"},{"id":84214683,"identity":"2efde63f-9bce-4b9c-b170-2f56fe2d673d","added_by":"auto","created_at":"2025-06-09 10:32:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2240005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeep machine learningwas used to screen key regulators based on the 165 intersected DEGs. \u003c/strong\u003eOutputs from (A) SVM-RFE, (A) RandomForest and (C) Lasso machine learning.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/2198c0ac422d019c81715343.png"},{"id":84214685,"identity":"98b11a0f-2fa1-4c92-b329-1f3f7b28b8c7","added_by":"auto","created_at":"2025-06-09 10:32:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2462583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration of the 6 screened key regulators in GBC patients. \u003c/strong\u003eA. Boxplots for the expression of the 6 screened key regulators in GBC patients. B. Boxplots for the expression of the6 screened key regulators in different immune cells. C. The association of the expression levels of the 6 screened key regulators and main regulatory cell types in tumor immune. D. The interaction strength among main regulatory cell types in tumor immune.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/9c7371fdc5679eb615d56ead.png"},{"id":84214699,"identity":"abf9cac0-5c5c-4d98-a831-16748c4b071f","added_by":"auto","created_at":"2025-06-09 10:32:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2353238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network of the 6 candidate genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/f7f77cc1d37b90ed7b8b7b33.png"},{"id":84216745,"identity":"15abd424-71c3-461f-b4a9-0c6d87bd9ba5","added_by":"auto","created_at":"2025-06-09 10:48:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":7077424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of the diagnostic and prognostic values of the 6 candidate genes in GBC patients based on the SEER (Surveillance, Epidemiology, and End Results) database. \u003c/strong\u003eA. Gene Set Enrichment Analysis (GSEA) for the 6 candidate genes. B.\u003cstrong\u003e \u003c/strong\u003eEvaluation of diagnostic values of the 6 candidate genes in GBC. C.\u003cstrong\u003e \u003c/strong\u003eEvaluation of prognostic values of the 6 candidate genes in GBC.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/3a5a0f34f11c8d6e27212764.png"},{"id":106728537,"identity":"e807dfce-3165-4a20-80b1-aebd2f288cb9","added_by":"auto","created_at":"2026-04-12 18:43:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36622617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/00fa7508-8868-42ba-930a-407b54f7544f.pdf"},{"id":84214668,"identity":"406330d4-b74e-4450-8a5a-d3197b211652","added_by":"auto","created_at":"2025-06-09 10:32:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1985297,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6564288/v1/2740b1935d97d4ff500b2c6f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Omics Integrated Analysis of the Protective Effect of Tertiary Lymphoid Structures and Associated Key Regulatory Genes in Human Gallbladder Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGallbladder cancer (GBC) is a common malignant gastrointestinal tumor in China, with a constant growth and rejuvenation of incidence rate [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Gallbladder cancer lacks specific clinical manifestations, making early diagnosis extremely difficult, but it progresses rapidly. Most gallbladder cancers are found in the late stage, with a very poor prognosis. Surgery is the only means of curing early-stage gallbladder cancer, but due to the difficulty in early diagnosis, only 20% of patients can be eligible for radical resection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Radical surgical resection is currently the only potentially curative treatment for gallbladder cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Postoperative pathological characteristics and TNM staging of gallbladder cancer can help assess the prognosis and guide treatment strategies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the recurrence rate after surgery is relatively high, and recurrent GBC patients displayed a quite low survival rate. It is urgent to explore biomarkers that can accurately predict the prognosis and the risk of postoperative recurrence in GBC patients.\u003c/p\u003e \u003cp\u003eTertiary lymphoid structures (TLS), also known as ectopic lymphoid aggregates, are lymphoid structures found in non-lymphoid tissues that can drive immune cell activation, and their formation is associated with chronic diseases, such as chronic inflammatory diseases, autoimmune diseases, and tumors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As an important feature of anti-tumor immune response, numerous studies have shown that TLS are related to superior prognosis of patients with various tumors, including pancreatic cancer, melanoma, non-small cell lung cancer, hepatocellular carcinoma, and colorectal cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Currently, there are few studies on TLS in gallbladder cancer, and their role in gallbladder cancer is still unclear.\u003c/p\u003e \u003cp\u003eIn this study, the association of TLS with the clinical features, prognosis and recurrence of GBC were investigated in 85 patients. We found that the presence of intra-tumoral TLS was negatively associated with T stage and vascular invasion and predicted a higher rate of overall survival and a decreased risk of early recurrence. Moreover, we screened potential key regulators of gallbladder cancer by integrative analyzing datasets from single cell RNA sequencing, bulk RNA sequencing, and machine learning from multiple databases, which provides a new insight for the research on prognosis of gallbladder cancer and development of therapeutic targets.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and samples\u003c/h2\u003e \u003cp\u003e A total of two 210 patients were followed up in this study, who underwent their first HCC resection in the First Affiliated Hospital of Chongqing Medical University (Chongqing, China) from 2014 to 2022. 125 patients were excluded through the criteria (transarterial chemotherapy and embolization, immunotherapy and other anticancer treatments; and preoperative tumor rupture or distant metastasis). And 85 patients were ultimately enrolled. Their following clinical and biological features were recorded, including age, gender, T stage, N stage, TNM stage, tumor differentiation degree, vascular invasion and perineural invasion The pathologic tumor type was diagnosed by three pathologists after surgical excision. Recurrence within 2 years of surgical resection was considered early recurrence because such cases are considered metastasis of the original tumor after resection rather than a new tumor. This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Approval number: 2024-486-01). Informed consent was obtained from all participants and/or their legal guardians. Research involving human research participants performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePathological examination\u003c/h3\u003e\n\u003cp\u003eFormalin-fixed and paraffin-embedded samples were sectioned continuously at a thickness of 4 \u0026micro;m by Servicebio (Wuhan, China). Hematoxylin-eosin (HE) staining was used to verify the presence of TLSs, according to the detailed staining procedures described in a previous study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. At least one TLS was confirmed positive in each section. This process was performed by three individual pathologists. According to maturity, the TLSs were classified as: aggregates (tumor samples show only aggregates but no primary follicles) and primary follicles (tumor sample show at least one primary follicle or germinal center, with or without aggregates). Slides were scanned using a Nanozoomer scanner (Hamamatsu, Hirakuchi, Japan), and the surfaces of tissue areas were calculated using Nanozoomer Digital Pathology View software.\u003c/p\u003e\n\u003ch3\u003eSingle cell RNA sequencing analysis for primary and metastatic focuses of GBC patients\u003c/h3\u003e\n\u003cp\u003eSingle cell RNA sequencing data from primary and metastatic focuses of GBC patients were downloaded from the GEO database (dataset: GSE201425). The expression matrix was analyzed with Seurat (v 4.2.0) to filter out low-quality cells, with the following parameters: Min.Cells\u0026thinsp;=\u0026thinsp;3, novelty score (log\u003csub\u003e10\u003c/sub\u003eGenesPerUMI: average number of genes corresponding to each UMI)\u0026thinsp;\u0026gt;\u0026thinsp;0.8, UMI\u0026thinsp;\u0026gt;\u0026thinsp;500, nGene (number of genes expressed greater than 0)\u0026thinsp;\u0026gt;\u0026thinsp;200, mitoRatio (proportion of mitochondrial expressed genes)\u0026thinsp;\u0026lt;\u0026thinsp;0.1. The R package Seurat (v 4.2.0) was used for low-quality cell filtering, standardization, data integration, PCA linear clustering, UMAP dimensionality reduction clustering, tSNE dimensionality reduction clustering, identification of marker genes and conservative marker genes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; Cell types were identified based on marker genes in the Cell Taxonomy database, and ClusterProfiler was used to perform GO and pathway enrichment analysis on marker genes and conserved marker genes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Data normalization and scaling were performed using the SCTransform function, with mitochondrial gene counts regressed out to eliminate potential biases. Subsequently, SelectIntegrationFeatures, PrepSCTIntegration, and FindIntegrationAnchors functions were utilized to identify highly variable features across cells and to establish \"anchors\" for integrating individual datasets. This integration process culminated in the creation of an \"unbatched\" dataset through the IntegrateData function, effectively removing batch effects and enabling combined analysis of all cells.\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) was conducted on this integrated dataset using the RunPCA function, which focused on the scaled and transformed data of identified variable genes. For visualization, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) were applied using the RunTSNE and RunUMAP functions with top 30 principal components (PCs), respectively. These techniques facilitated the exploration of the dataset's underlying structure by projecting the high-dimensional data into two dimensions.\u003c/p\u003e \u003cp\u003eCell clustering was performed using the FindNeighbors and FindClusters functions from Seurat, initially setting the resolution to 1.5 to identify major cell types. Following cell clustering, we primarily identified differentially expressed genes in specific clusters compared to the others using the FindConservedMarkers function with only.pos\u0026thinsp;=\u0026thinsp;TRUE and logfc. threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.1 as the thresholds, which facilitated the identification of characteristic markers for each cluster. Conserved marker genes were analyzed using clusterProfiler to enrich for over-represented biological processes and pathways, enhancing our understanding of the functional implications of gene expression patterns within each cluster. Based on the marker genes in the Cell Taxonomy databases, these clusters were manually annotated based on their marker profiles. To detect (DEGs, referred as scRNA-seq DEGs) across groups, we employed the FindMarkers with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Log\u003csub\u003e2\u003c/sub\u003efold-change\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and minimum percentage (min.pct)\u0026thinsp;\u0026gt;\u0026thinsp;0.1 as the thresholds. The differentially expressed genes were conducted to enrich for over-represented biological processes and pathways using clusterProfilers. A volcano plot was created using ggplot2, and GO and KEGG enrichment analyses were performed on differentially expressed genes using clusterProfiler.\u003c/p\u003e\n\u003ch3\u003eMulti-omics integrated analysis and deep machine learning\u003c/h3\u003e\n\u003cp\u003eThe GEO dataset (GSE138109) containing RNA-seq data from 20 paired human GBC normal and cancer tissues were downloaded and was re-analyzed with the R package limma. A |log\u003csub\u003e2\u003c/sub\u003e (fold change)| \u0026ge; 2 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used as the thresholds to determine whether the RNA transcripts were upregulated or downregulated. Volcano plots and heatmaps showing DEGs (GEO-DEGs) were generated with R soft and related Bioconductor packages. Then, 7728 TLS-related genes were obtained from the online database GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/Search/Keyword?queryString=\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/Search/Keyword?queryString=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e tertiary%20lymphoid%20structure). The scRNA-seq DEGs, GeneCards-TLS related genes, and GEO-DEGs were intersected to screen potential TLS-related regulators in GBC progression. The intersection was applied in deep machine learning by using SVM-RFE, RandomForest and Lasso for further screening. Ultimately, the output genes from machine learning were applied in single gene analysis in depth, including Immune infiltration, Clinical Prognosis, and multiple therapy responses.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Student\u0026rsquo;s t test and one-way ANOVA followed by Tukey\u0026rsquo;s post hoc test were used to verify the comparison among groups by using the SPSS 22.0 software. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe presence of intra-tumoral TLS was associated with superior prognosis of GBC patients\u003c/h2\u003e \u003cp\u003eA total of 85 GBC patients were eventually enrolled in this study. Their following clinical and biological features were recorded, including age, gender, T stage, N stage, TNM stage, tumor differentiation degree, vascular invasion and perineural invasion The pathologic tumor type was diagnosed by three pathologists after surgical excision. Patients were female and older than 60 years in 75.3% and 51.8% of the cases, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main risk factors were T stage, N status and TNM stage, differentiation, vascular invasion, perineural invasion. Pathological examination identified TLS in 30 tumors (35.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among GBC with TLS (TLS\u003csup\u003e+\u003c/sup\u003e GBC), the maximum degree of TLS maturation was Agg, FL-I and FL-II in 10 (33.3%), 13(43.3%) and 7 (23.3%) cases, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and biological features according to the presence of intra-tumoral TLS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;85\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTLS\u003csup\u003e+\u003c/sup\u003e GBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLS\u003csup\u003e\u0026minus;\u003c/sup\u003e GBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41(48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(53.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44(51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30(54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21(24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14(25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64(75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41(74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1/T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59(69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25(73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3/T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59(69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36(65.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1/N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19(35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ/Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27(49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ/Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38(44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28(51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell or moderately\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32(58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly or undifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35(41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(41.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78(92.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30(100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48(88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77(90.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48(87.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: TLS: Tertiary lymphoid structure; TNM: tumor-node-metastasis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eApart from an association with T stage (P\u0026thinsp;=\u0026thinsp;0.035, chi-squared test), and vascular invasion (P\u0026thinsp;=\u0026thinsp;0.048), TLS\u003csup\u003e+\u003c/sup\u003e GBC was not linked to any other clinical, or biological feature (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interestingly, no differences were observed according to the status of age (P\u0026thinsp;=\u0026thinsp;0.487), gender (P\u0026thinsp;=\u0026thinsp;0.785), N status (P\u0026thinsp;=\u0026thinsp;0.284), TNM stage (P\u0026thinsp;=\u0026thinsp;0.119), differentiation (P\u0026thinsp;=\u0026thinsp;0.871), and perineural invasion (P\u0026thinsp;=\u0026thinsp;0.156) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Altogether, these findings suggested that T stage and vascular invasion had impacts on the existence of intra-tumoral TLS.\u003c/p\u003e \u003cp\u003eFeatures associated with an increased risk of overall survival in univariate analysis were T stage (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;2.334, P\u0026thinsp;=\u0026thinsp;0.010), N stage (HR\u0026thinsp;=\u0026thinsp;2.971, P\u0026thinsp;=\u0026thinsp;0.001), TNM stage (HR\u0026thinsp;=\u0026thinsp;2.799, P\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). TLS (HR\u0026thinsp;=\u0026thinsp;0.300; P\u0026thinsp;=\u0026thinsp;0.004) was related to a decreased risk of overall survival (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The prognostic value of TLS was retained after multivariate analysis (HR\u0026thinsp;=\u0026thinsp;0.320, P\u0026thinsp;=\u0026thinsp;0.007) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Features associated an increased risk of early recurrence in univariate analysis were gender (HR\u0026thinsp;=\u0026thinsp;2.764, P\u0026thinsp;=\u0026thinsp;0.003), T stage (HR\u0026thinsp;=\u0026thinsp;3.277, P\u0026thinsp;=\u0026thinsp;0.010) ,TNM stage (HR\u0026thinsp;=\u0026thinsp;3.899, P\u0026thinsp;=\u0026thinsp;0.000), and perineural invasion (HR\u0026thinsp;=\u0026thinsp;3.506, P\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).While feature associated an decreased risk of early recurrence in univariate analysis was TLS (HR\u0026thinsp;=\u0026thinsp;0.264, P\u0026thinsp;=\u0026thinsp;0.006) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, the prognostic value of TLS was consistent after multivariate analysis (HR\u0026thinsp;=\u0026thinsp;0.290, P\u0026thinsp;=\u0026thinsp;0.006) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of GBC overall survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026lt;\u0026thinsp;60, \u0026ge;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.815(0.946\u0026ndash;3.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female, male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.132(1.101\u0026ndash;4.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage (T1/T2, T3/T4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.334(1.229\u0026ndash;4.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN status (N0, N1/N2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.971(1.564\u0026ndash;5.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.010(1.578\u0026ndash;5.740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (Ⅰ-Ⅱ, Ⅲ-Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.799(1.443\u0026ndash;5.429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation (well or moderately, poorly)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.363(0.718\u0026ndash;2.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion (+, -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.364(0.919\u0026ndash;6.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion (+, -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.144(0.834\u0026ndash;5.512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLS (+, -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.320(0.141\u0026ndash;0.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.317(0.139\u0026ndash;0.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: TLS: Tertiary lymphoid structure; HR: Hazard ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of early GBC recurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026lt;\u0026thinsp;60, \u0026ge;\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.372(0.711\u0026ndash;2.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female, male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.764(1.410\u0026ndash;5.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.007(0.999\u0026ndash;4.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage (T1/T2, T3/T4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.277(1.696\u0026ndash;6.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.188(1.088\u0026ndash;4.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN status (N0, N1/N2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.263(1.689\u0026ndash;6.303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.108(1.031\u0026ndash;4.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage (Ⅰ-Ⅱ, Ⅲ-Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.899(1.913\u0026ndash;7.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation (well or moderately, poorly)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.795(0.933\u0026ndash;3.454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular invasion (+, -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.960(1.139\u0026ndash;7.695)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerineural invasion (+, -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.506(1.526\u0026ndash;8.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLS (+, -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.264(0.110\u0026ndash;0.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.290(0.119\u0026ndash;0.705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: TLS: Tertiary lymphoid structure; HR: Hazard ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSeveral subgroups of T, B and macrophages were reduced in GBC metastatic focus\u003c/h3\u003e\n\u003cp\u003eSingle cell RNA sequencing data from primary and metastatic focuses of GBC patients (GSE201425) were analyzed to compare the differences in heterogeneity and immune cell composition between primary and metastatic focuses of GBC patients. Our results of tSNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) showed that all cells could be divided into 33 cell clusters (0\u0026ndash;32, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;1A). Group comparison indicated that, compared with primary GBC focuses, metastatic focuses displayed a relatively smaller cell population in a few cell clusters, especially clusters 2, 3, 8, 9, 12, 16, 21 and 23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;1B). Cell cycle stage comparison revealed that more cells were in the G1 or S phases than the G2 phase, suggesting the vigorous growth and rapid cell division in GBC focuses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and Supplementary Fig.\u0026nbsp;1C). Moreover, based on the conserved markers, these 33 clusters could be attributed to 13 cell types, including B cells, cancer associated fibroblasts (CAFs), Dendritic cells (DCs), Endothelial cells (ECs), Epithelial cells, Macrophages, Mast cells, Monocytes, Neutrophils, Natural killer (NK) cells, Plasma cells, Plasmacytoid DCs and T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Supplementary Fig.\u0026nbsp;1D). As expected, clusters 3, 8, 12, 16 and 23, which belonged to of CAFs and epithelial cells and resident cells of the gallbladder tissue, were distinctly reduced in the metastatic focus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;1B). Moreover, as important cell components of TLS, clusters 2, 9 and 21, respectively belonging to T cells, macrophages and plasma cells, were also markedly reduced in the metastatic focus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;1B), suggesting a reduced tendency in cell components of TLS formation in metastatic GBC focus. Furthermore, conserved marker analysis revealed that the clusters 2, 9 and 21 could be defined as NELL2\u003csup\u003e+\u003c/sup\u003eZNF683\u003csup\u003e+\u003c/sup\u003e CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), GAL3ST4\u003csup\u003e+\u003c/sup\u003eCD163\u003csup\u003e+\u003c/sup\u003e macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and IGHG1\u003csup\u003e+\u003c/sup\u003eIGHG4\u003csup\u003e+\u003c/sup\u003e plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). GO and KEGG analyses suggested that they were significantly associated with T cell activation, protein binding and cell phagocytosis, and enriched in corresponding pathways (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMulti-omics integrated analysis screened 6 candidate TLS-related genes in GBC\u003c/h2\u003e \u003cp\u003eThen, GEO dataset (GSE138109) containing RNA-seq data from 20 paired human GBC normal and cancer tissues were analyzed with the R package limma. Our results showed that there were 322 downregulated and 91 upregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). GO analysis indicated that these 413 DEGs were significantly associated with extracellular matrix metabolism, blood production and coagulation, biofilm fusion and disintegration, and multiple protein binding responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), which are key biological processes in tumor invasion and metastasis. KEGG pathway annotation also indicated that these DEGs were enriched in multiple pathways related to immune responses and cell-cell interaction, such as cytoskeleton, neutrophil trap, ECM-receptor interaction and focal adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), which are also key biological processes in tumor cell movement and adhesion\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, scRNA-seq DEGs, GeneCards-TLS related genes, and GEO-DEGs were intersected to screen potential TLS-related regulators in GBC progression. 165 intersection genes were screened and applied in the following deep machine learning (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Our results showed that we screened 6 TLS-related genes, including Cathepsin G (CTSG), filamin C (FLNC), cyclin B1 (CCNB1), heat shock protein family B member 8 (HSPB8), nuclear receptor subfamily 4,group A-1 (NR4A1) and myosin light polypeptide kinase (MYLK), which might be involved in the progression of GBC (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCTSG, FLNC, CCNB1 and HSPB8 played important roles in diagnosis and prognosis in GBC\u003c/h2\u003e \u003cp\u003eThen, their associations with the immune infiltration, diagnosis and prognosis of gallbladder cancer were evaluated by single gene bioinformatic analysis in depth. Boxplots revealed that CTSG, FLNC, CCNB1 (down), HSPB8, NR4A1 and MYLK showed distinct expression levels in Normal and GBC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Immune infiltration assay showed that CTSG, FLNC, CCNB1, HSPB8 and NR4A1 were significantly associated with most key cell types in tumor immune (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and C), the interaction s of which are relatively strong (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Protein-protein interaction network indicated that they participated in networks related to cell cycle, extracellular matrix metabolism, oxidative stress and cell adhesion and multiple kinases associated with cell survival and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Gene Set Enrichment Analysis (GSEA) also showed that they were enriched in pathways associated with cell cycle and multiple kinases associated with cell survival and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;3). Finally, their values in diagnosis and prognosis of gallbladder cancer were assessed. Our results indicated CTSG, FLNC, CCNB1, HSPB8, NR4A1 and MYLK all displayed high diagnostic values (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB), while CTSG, FLNC, CCNB1, and HSPB8 were significantly related to the survival rates of GBC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe presence of TLSs has been associated with clinical benefit in patients with multiple types of cancer, whereas, the significance of TLSs in gallbladder cancer patients was still largely unknown. In this study, we found that the presence of intra-tumoral TLS was negatively associated with T stage and vascular invasion and predicted a higher rate of overall survival and a decreased risk of early recurrence in 85 GBC patients. Moreover, multi-omics integrated analysis screened CTSG, FLNC, CCNB1, and HSPB8 as potential key regulators in immune infiltration, diagonosis and prognosis of GBC.\u003c/p\u003e \u003cp\u003eTLSs, also known as ectopic lymphoid structures, form at the site of inflammation in which lymphoid tissue undergoes extra-nodal seeding, under sustained chronic pathological conditions. Its formation process is similar to the formation of secondary lymphoid organs during embryonic development, under the interaction between hematopoietic cells and non lymphoid stromal cells, as well as the induction of molecular components such as cytokines, chemokines, adhesion molecules, and survival factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. TLSs are mainly composed of T cells and B cells: the central region of the TLS is mainly composed of CD20\u0026thinsp;+\u0026thinsp;B cells, and various mature CD3\u0026thinsp;+\u0026thinsp;T cells surround the periphery of B cells to form a lymphoid like structure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition to the main T cells and B cells, there are also other subgroups of dendritic cells and macrophages in TLS, such as CD83\u0026thinsp;+\u0026thinsp;mature dendritic cells associated with lysosomal membrane proteins mainly localized in the T cell region, CD68\u0026thinsp;+\u0026thinsp;macrophages that are used to clear apoptotic cells, and IgG-producing plasma cells that sustained B cell maturation and antibody production[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this study, our single-cell RNA sequencing analysis indicated that compared with primary GBC focuses, metastatic focuses displayed a relatively smaller cell population in a few cell clusters, especially clusters 2 (NELL2\u003csup\u003e+\u003c/sup\u003eZNF683\u003csup\u003e+\u003c/sup\u003e CD8+), 9 (GAL3ST4\u003csup\u003e+\u003c/sup\u003eCD163\u003csup\u003e+\u003c/sup\u003e), and 21 (IGHG1\u003csup\u003e+\u003c/sup\u003eIGHG4\u003csup\u003e+\u003c/sup\u003e), which respectively belonged to T cells, macrophages and plasma cells. These findings suggested a reduced tendency in cell components of TLS formation in metastatic GBC focus. Moreover, GO and KEGG analyses suggested that they were significantly associated with T cell activation, protein binding and cell phagocytosis, and enriched in corresponding pathways.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCTSG\u003c/em\u003e gene encodes the cathepsin G protein, a member of the serine protease family. CTSG is mainly expressed in neutrophils, monocytes, myeloid DCs, and plasmacytoid DCs (pDCs), which are common cellular components of TLSs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Upon activation, CTSG was expressed and inhibited malignant behaviors by autocrine or paracrine means in human cancers, such as breast, oral squamous cell, and colon cancer [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, there has been no report revealing the exact role of CTSG in GBC progression. In this study, CTSG was screened as a biomarker in TLS formation in GBC by integrative analysis of single-cell RNA sequencing, bulk RNA sequencing, TCGA data and TLS-related genes from Genecards combined with machine learning. Boxplots revealed that it showed a distinctly decreased expression level in GBC tissues compared with the normal, immune infiltration assay showed that it was significantly associated with most key cell types in tumor immune, protein-protein interaction network and GSEA assays indicated that it participated in networks related to extracellular matrix metabolism, cell adhesion and multiple signalings associated with cell survival and invasion, and it was positively related to the survival rates of GBC patients, suggesting it a tumor suppressor role in GBC.\u003c/p\u003e \u003cp\u003eFLNC is a member of large dimeric actin-binding protein family FLNs, which dominate the remodeling of actin cytoskeleton and also serve as scaffolds for signaling proteins, such as tyrosine kinases, GTPases, or phosphatases. FLNC was reported as a biomarker and therapeutic target in some types of cancer, including gastric cancer, rectal cancer, lung cancer and glioblastoma[\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. FLNC, directly bound and degraded by TRIM54, a proto-oncogene in gastric cancer (GC), efficiently inhibited GC progression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while, in glioblastoma, low-frequency repetitive transcranial magnetic stimulation inhibited ERK/JNK/p38 and PI3K/AKT/mTOR pathways and induced apoptosis by suppressing the expression of FLNC [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, there were rare reports revealing the exact role of FLNC in GBC progression. In this study, FLNC was screened as a biomarker in TLS formation in GBC by multi-omics integrated analysis combined with machine learning. Boxplots revealed that it was significantly downregulated in GBC tissues, immune infiltration assay showed that it was significantly associated with most key cell types in tumor immune, protein-protein interaction network and GSEA assays indicated that it participated in multiple signalings associated with cell survival and invasion, and it was negatively related to the survival rates of GBC patients.\u003c/p\u003e \u003cp\u003eAs an important member of cyclins, CCNB1 plays an pivotal role in mitosis initiation, which confers it a promoting role in cell survival, invasion and drug resistance in many tumors, including gallbladder cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, CCNB1 was screened as a biomarker in TLS formation in GBC by multi-omics integrated analysis combined with machine learning. It was significantly upregulated in GBC tissues, significantly associated with most key cell types in tumor immune, and involved in multiple kinases associated with cell cycle, cell stress and invasion, and it was negatively related to the survival rates of GBC patients, indicating that CCNB1 acts as proto-oncogene and therapeutic target in GBC. Consistent with our results, many studies have indicated that CCNB1 was upregulated in GBC and inhibition of CCNB1 was abled to suppress GBC progression [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHSPB8 is a chaperone molecule involved in autophagy that promotes selective degradation of proteins to counteract cell stress in normal and tumor cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Accumulating evidence has indicated that HSPB8 is associated with chromosome segregation and G0/G1cell cycle arrest and dendritic cell maturation and cytokine production, which confers it an important role in regulating the tumorigenesis, tumor progression and drug resistance [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, there were rare reports revealing the exact role of FLNC in GBC progression. In this study, HSPB8 was screened as a biomarker in TLS formation in GBC by multi-omics integrated analysis combined with machine learning. It was significantly downregulated in GBC tissues, significantly associated with most key cell types in tumor immune, and involved in multiple molecular chaperones associated with cell survival, invasion and drug resistance, and it was negatively related to the survival rates of GBC patients.\u003c/p\u003e \u003cp\u003eThere were some limitations in our current study. First, due to the limited amount of data on gallbladder cancer in the public databases, the sample size applied in our multi-omics analysis is not large, limits our in-depth analysis of some clinical indicators. Second, due to limited time, funding and laboratory conditions, we did not conduct cellular and animal experiments to validate the role of the screened candidate genes in TLS formation and gallbladder progression. Our next plan is to investigate the role of CTSG in TLS formation and gallbladder progression using cellular and animal models.\u003c/p\u003e \u003cp\u003eIn conclusion, the presence of intra-tumoral TLS was negatively associated with T stage and vascular invasion and predicted a higher rate of overall survival and a decreased risk of early recurrence in GBC patients. TLS-related genes CTSG, FLNC, CCNB1, and HSPB8 are potential key regulators in immune infiltration, diagonosis and prognosis of GBC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaolong Chen: Investigation, bioinformatics analysis, and Writing-original draft; Yimin Nong: figure/table preparation, Data curation and\u0026nbsp;Writing-Review\u0026amp;Editing; Ming Zhang:\u0026nbsp;Software, Resources and Writing-Review\u0026amp;Editing; Chengyou Du:\u0026nbsp;Conceptualization, Validation, Supervision and Writing-Review\u0026amp;Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated in the current study are available from the corresponding author upon reasonable request or downloaded from the following web links: GSE138109 (https://www.ncbi.nlm.nih.gov/gds/?term=GSE138109), GSE201425 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201425), Genecards-TLS (https://www.genecards.org/Search/Keyword?queryString=Tertiary%20lymphoid%20structure) and SEER (https://seer.cancer.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Approval number: 2024-486-01). Informed consent was obtained from all participants and/or their legal guardians. Research involving human research participants performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the subjects enrolled in this study were fully informed and gave informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors consent to publish this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ.C. Roa, P. Garc\u0026iacute;a, V.K. Kapoor, S.K. Maithel, M. Javle, J. Koshiol, Gallbladder cancer, Nat Rev Dis Primers, 8 (2022) 69.\u003c/li\u003e\n\u003cli\u003eC.F. Feo, G.C. Ginesu, A. Fancellu, T. Perra, C. Ninniri, G. 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Ding, L.H. Meng, The mTOR inhibitor AZD8055 overcomes tamoxifen resistance in breast cancer cells by down-regulating HSPB8, Acta Pharmacol Sin, 39 (2018) 1338-1346.\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":"Tertiary lymphoid structures, gallbladder cancer, multi-omics integrated analysis, diagonosis and prognosis, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-6564288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6564288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTertiary lymphoid structures (TLS) are lymphoid structures found in non-lymphoid tissues, which are associated with immune cell activation and have been involved in pathogenesis of multiple chronic diseases including tumors. Growing evidence has revealed that TLS are associated with better prognosis and response to immunotherapy in most types of tumors. However, what are their associations with the prognosis and the exact roles of TLS-related genes in gallbladder cancer are still unclear. Here, we investigated the associations of TLS with the prognosis in 85 gallbladder cancer patients. Our results showed that the presence of intra-tumoral TLS was negatively associated with T stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and vascular invasion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) and predicted a higher rate of overall survival (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and a decreased risk of early recurrence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Moreover, using integrative analysis of single cell RNA sequencing, bulk RNA sequencing, and machine learning, we screened 6 TLS-related genes that potentially involved in the progression of gallbladder cancer, including CTSG, FLNC, CCNB1, HSPB8, NR4A1 and MYLK. Futhermore, through evaluation of biofunctions and clinical significance, we found that CTSG, FLNC, CCNB1 and HSPB8 played an important role in immune infiltration, diagonosis and prognosis of gallbladder cancer. In conclusion, we demonstrated that TLS had a protective effect on human gallbladder cancer, and its related genes, CTSG, FLNC, CCNB1 and HSPB8, played an important role in diagonosis, prognosis, immune infiltration and metastasis of gallbladder cancer. Our findings provided a new insight for the research on clinical biomarkers of gallbladder cancer and development of its therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Multi-Omics Integrated Analysis of the Protective Effect of Tertiary Lymphoid Structures and Associated Key Regulatory Genes in Human Gallbladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 10:32:48","doi":"10.21203/rs.3.rs-6564288/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":"3b72f821-574a-4a50-a0d9-ac2d1e807f7c","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49486221,"name":"Biological sciences/Cancer"},{"id":49486222,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":49486223,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-04-11T13:25:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 10:32:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6564288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6564288","identity":"rs-6564288","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}