
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12715-6
10.1016/j.heliyon.2024.e36684
e36684
Research Article
Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance
Lu Si-Yuan lusy39@mail2.sysu.edu.cn
1
Xu Qiong-Cong 1
Fang De-Liang 1
Shi Yin-Hao
Zhu Ying-Qin
Liu Zhi-De
Ma Ming-Jian
Ye Jing-Yuan
Yin Xiao Yu yinxy@mail.sysu.edu.cn
∗
Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
∗ Corresponding author. Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China. yinxy@mail.sysu.edu.cn
1 These authors contributed equally to this work.

22 8 2024
15 9 2024
22 8 2024
10 17 e3668412 6 2024
19 8 2024
20 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Increasing evidence indicates that the remodeling of immune microenvironment heterogeneity influences pancreatic cancer development, as well as sensitivity to chemotherapy and immunotherapy. However, a gap remains in the exploration of the immunosenescence microenvironment in pancreatic cancer. In this study, we identified two immunosenescence-associated isoforms (IMSP1 and IMSP2), with consequential differences in prognosis and immune cell infiltration. We constructed the MLIRS score, a hazard score system with robust prognostic performance (area under the curve, AUC = 0.91), based on multiple machine learning algorithms (101 cross-validation methods). Patients in the high MLIRS score group had worse prognosis (P < 0.0001) and lower abundance of immune cell infiltration. Conversely, the low MLIRS score group showed better sensitivity to chemotherapy and immunotherapy. Additionally, our MLIRS system outperformed 68 other published signatures. We identified the immunosenescence microenvironmental windsock GLUT1 with certain co-expression properties with immunosenescence markers. We further demonstrated its positive modulation ability of proliferation, migration, and gemcitabine resistance in pancreatic cancer cells. To conclude, our study focused on training of composite machine learning algorithms in multiple datasets to develop a robust machine learning modeling system based on immunosenescence and to identify an immunosenescence-related microenvironment windsock, providing direction and guidance for clinical prediction and application.

Graphical abstract

Image 1

Highlights

• Our composite machine learning system contains 101 composite machine learning methods

• Our prognostic scoring system was well validated in our SYSU dataset.

• We found that GLUT1was associated with gemcitabine resistance.
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pmc1 Introduction

Pancreatic cancer is highly aggressive, malignant, and lethal [1] and is reported to be the sixth leading cause of cancer-related deaths in China in recent years [2]. Early radical surgery is the only curative therapy; however, less than one-fifth of patients are eligible due to delayed diagnosis [3]. Chemotherapy is the first-line therapeutic strategy for advanced unresectable and recurrent metastatic pancreatic cancer; however, chemotherapy resistance remains an unresolved challenge [4,5]. While radiotherapy shows promise in treating nasopharyngeal cancer and other tumors, its role in pancreatic cancer therapy is primarily palliative [6]. Immune checkpoint inhibitors are an immunotherapeutic paradigm for several malignant tumors and have brought considerable benefits to prolonging the survival of patients [7]. However, the complexity of immune cells and the heterogeneity of stromal components in different tumors notably affect the expression of checkpoints, such as PDL1, LAG3, and BTLA, thus impacting the efficacy of immunotherapy [8]. For example, immunotherapy application represented by PD1 inhibitors is limited in pancreatic cancer due to the special “cold” tumor immune microenvironment [9]. Therefore, novel breakthroughs are crucially needed in the judgment and treatment of pancreatic cancer, particularly from the perspective of the immune microenvironment.

Immunosenescence refers to the change in the immune system that occurs with age, influenced by both intrinsic and extrinsic factors [10]. It is characterized by two major manifestations: a defective overall immune response and systemic chronic inflammation. The inflammatory microenvironment exacerbates immune senescence and immunosenescent cells appear elevated in several age-related diseases [11]. Moreover, in tumors with immune abnormalities, immune senescence usually promotes their progression and increases risk. Vatter et al. found that progenitor cells accumulate in the female breast with age, leading to an increased risk of tumorigenesis [12]. Additionally, some researchers have elucidated that the activation of the PKA-CREB and P38 pathways, along with aberrant glucose utilization, can induce DNA damage and T cell immunosenescence, ultimately leading to tumor development [13]. While these fundamental studies using cellular and animal models revealed the deep mechanisms linking immunosenescence and tumorigenesis, they lack the comprehensiveness that comes from integrated large-sample data and multidimensional analyses. Ideal clinical markers should demonstrate robust expression and prognostic properties across patients and within tumor tissues. Panel scoring of multiple genes derived from machine learning may serve as a valuable tool for this purpose.

In our current study, we first constructed immunosenescence microenvironmental heterogeneity typing in pancreatic cancer. We incorporated immunosenescence-related biomarkers into multiple datasets and used multiple machine-learning models to screen the most robust signature and explore the prognostic and therapeutic profiles. We sought to identify ideal biomarkers with robust prognostic and applicable potential for clinical applications.

2 Materials and methods

2.1 Data source acquisition and processing

We acquired and processed a total of 12 datasets used to construct and validate our scoring system. These included nine sequencing datasets for pancreatic cancer (TCGA-PAAD, GSE28735, GSE57495, GSE62452, GSE79668, GSE85916, PACA-AU-seq, PACA-CA-array, and EMTAB6134) and four datasets for immunotherapy and targeted therapies (IMvigor210 and GSE78220). After obtaining ethical approval from our center, we further collected tissues from 58 patients with pancreatic cancer and performed q-RT-PCR to test the applicability of the hazard model in the Sun Yat-Sen University (SYSU) cohort. We used the “Sva” package to fuse nine pancreatic cancer datasets to obtain a meta-cohort to expand the sample size and increase stability. Further details on materials and methods are provided in the supplementary materials. Fig. 1 illustrates our analysis workflow.Fig. 1 Workflow of our research.

Fig. 1

2.2 Immunosenescence transcriptome phenotypes

We obtained 190 immunosenescence-associated regulatory genes from the GeneCard database and the National Center for Biotechnology Information (NCBI) gene database. To further screen these genes with prognostic significance for pancreatic cancer, we performed univariate COX regression analysis on these genes in our fusion data set and selected genes with P < 0.01 as our immunosenescence-related prognostic genes in our next analysis. Based on these immunosenescence features, we used the consensus clustering algorithm for unsupervised clustering and typing of patients with pancreatic cancer. The immunosenescence biomarkers included “PRR11,” “IKZF2,” “IL6,” “SIRT1,” “SESN1,” “LIMS1,” “CX3CR1,” “JAK2,” “LDHA,” “SOD1,” “SLC16A3,” “BCL2,” “PRKDC,” “ENG,” “MET,” “PPARGC1A,” “TLR5,” “CRP,” “TLR6,” “FAM83A,” “CCR7,” “IL18,” “ELN,” “ITPR2,” “PRDM1,” “IL15RA,” “KLRG1,” “MAFF,” “BCL2A1,” “MYO9B,” “IGFBP7,” “FNDC3B,” “TGFBI,” “KCTD12,” “TGFB2,” and “GLUT1.” The neatness of the matrix typing, the size of the delta area, and the area under the CDF curve are all key factors in determining the optimal K-value (number of typing).

2.3 Machine learning-derived immunosenescence-related score (MLIRS)

To obtain an optimal hazard scoring system, we trained these immunosenescence biomarker-based matrices using a total of 101 combined machine learning algorithms (based on 10-fold cross-validation) across 10 basal categories (survival support vector machine (survival-SVM), CoxBoost, random survival forest (RSF), Lasso, stepwise Cox, partial least squares regression for Cox (plsRcox), Ridge, supervised principal components (SuperPC), elastic network (Enet), and generalized boosted regression modeling (GBM)) and validated the robustness in 10 validation datasets. We derived the C-index value of each machine learning algorithm in each dataset and identified the algorithm with the largest mean C-index as the optimal hazard scoring algorithm. Further, we calculated the MLIRS with the following formula:MLIRS=exprgene1*coffgene1+exprgene2*coffgene2+exprgene3*coffgene3……exprgenen*coffgenen

2.4 Construction of gemcitabine-resistant cell lines

We established stable gemcitabine-resistant BXPC-3 (BR) and CFPAC-1 (CR) cell lines from Cellcook (Guangzhou, China), following the establishment process referred to in our previous article [14].

2.5 Gene silencing of GLUT1

We applied the Ribo-designed (GeneAdv Co. Ltd, Suzhou, China ) siRNA sequence to knock down GLUT1. The small interfering RNA sequences of GLUT1 that we applied were siRNA1 and siRNA2. The sequences were as follows: siRNA1: 5′- GCATGTGCTTCCAGTATGT-3′ and siRNA2: 5′- CAAAGTTCCTGAGACTAAA-3′.

2.6 qRT–PCR

ABScript III RT Master Mix was used for reverse transcription (RK20428, ABclonal, China). Using the QuantStudio 6 Flex Real-Time PCR machine, the expression of a candidate gene was assessed (Applied Biosystems, Life Technologies, USA).

2.7 Flow cytometry

Pancreatic cancer cells were cultured in 6-well plates with three repeated holes. PE Annexin V Apoptosis Detection Kit (BD, cat: 559763) was used to stain PANC-1 cells, and FACSCalibur flow cytometer was used for counting.

2.8 Transwell migration assay

PANC-1 and MiaPaCa-2 cells (10 × 104 cells each) were fostered in the upper chamber. The number of migrating cells was counted in a random area and an average of five fields per chamber was evaluated.

2.9 EDU assay

PANC-1 and MiaPaCa-2 cells (3 × 104 cells each) were cultivated in 96-well plates with three repeating holes (BeyoClickTM EdU Kit with Alexa Fluor 594, Shanghai, China) to measure the capacity for proliferation. Fixation, membrane breaking, and staining were performed according to the manufacturer's instructions.

2.10 Immunohistochemical staining

The First Affiliated Hospital of Sun Yat-Sen University provided tissue sections. The ethics committee approved each step of the process. The Service bio (Wuhan, China) business used IHC staining to identify protein expression using antibodies against GLUT1 (Proteintech, 1:100), CD8 (Abcam, 1:100), FOXP3 (Proteintech, 1:100), PDL1 (Proteintech, 1:100), Ki67(Proteintech, 1:100), and IL6 (Proteintech, 1:100).

2.11 Correlation with drug sensitivity

For the purpose of predicting chemotherapy drug sensitivity, three public pharmacogenomics databases: the Cancer Genome Project (CGP, ftp:/ftp.sanger.ac.uk/pub4/cancerrxgene/releases), Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/), and Cancer Therapeutics Response Portal (CTRP, https://portals.broadinstitute.org/) were used for prediction, the R packages “oncopredict” were utilized to predict IC50 (all sample in the fusion data was used).

2.12 Statistical analysis

The data were processed using R-4.1.2 and Excel software. Plotting was mainly performed using R-4.1.2 and Graphid-8.0. “Consensusclusterprofile” was mainly used for unsupervised clustering analysis. “PROC” packages were used for ROC curve analysis. Our data variables were compared using the t- and Wilcox tests.

3 Results

Identification of immunosenescence-related microenvironment heterogeneity phenotypes through consensus clustering analysis.

Previous literature has illustrated the link between immunosenescence, the tumor microenvironment, and tumor progression (Fig. 2A). In the immunosenescence microenvironment, immunosuppressive cells (such as Tregs and MDSCs) are upregulated in both number and function, indirectly promoting tumor progression. We further identified 36 immunosenescence-regulating molecules from the Genecard, NCBI database, and previous literature using univariate Cox regression analysis (Fig. 2B). Fig. 2C shows the univariate regression results of these immunosenescence molecules in pancreatic cancer. To enhance the reliability of our analyses, we fused nine public datasets through the “Combat” algorithm to exclude batch effects. These datasets demonstrated high consistency after fusion (Figs. S1A and S1B). We further used the consensus clustering algorithm to classify the expression matrix containing 36 immunosenescent molecules. The CDF values and delta areas suggested that the best results were achieved when patients were divided into two immunosenescence phenotypes (IMSP) (Fig. 2D and E). The PCA plot and heatmap demonstrate the expression distribution of the two phenotypes (Fig. 2F and G). Overall, IMSP2 was significantly more immunologically active than IMSP1, which exhibited lower expression of immune checkpoints (Fig. S2A). The immune score, stromal score, and estimate score were significantly higher for IMSP2 than for IMSP1 (Fig. 2H). Additionally, IMSP1 exhibited higher age than IMSP2 in the TCGA and PACA-AU-seq database, indicating immunosenescence trends (Fig. 2I and J). To ensure the robustness of our transcriptomic immunosenescence typing, we used seven immune infiltration algorithms (MCPcounter, TIMER, quantizeq, Xcell, EPIC, Cibersort, and ssGSEA) for validation and to explore inflammatory microenvironmental differences between the two immunosenescence phenotypes. The results suggested that immunopromoting immune cells, including CD8+ T cells, cytotoxic lymphocytes, and NK cells, were highly expressed in IMSP2 (Fig. S2B); immunosuppressive fibroblasts and M2 macrophages showed the opposite expression trend. Additionally, most inflammatory factors, including chemokines and interleukin receptors, were more highly expressed in IMSP2 (Fig. S2C). Survival analysis indicated that patients in IMSP1 had significantly worse overall survival times compared to those in IMSP2, suggesting that IMSP1 has optimal prognostic competence for our immunosenescent subtypes (Fig. 2K).Fig. 2 Identification of the transcriptome immunosenescence microenvironment phenotypes through consensus clustering analysis (A) Crosstalk between immunosenescence and tumor cells in different tumor immune microenvironments. (B) Process of identification of molecules associated with immunosenescence. (C) Univariate Cox analysis showing the prognostic value of immunosenescence molecules in pancreatic cancer (*P < 0.05, **P < 0.01, ***P < 0.001). (D, E) Consensus Clustering algorithm identifying two phenotypes based on delta area and CDF fitted curve. (F) PCA plot showing the relative distribution of the two phenotypes. (G) A thermogram exhibiting the expression mode of these immunosenescence molecules in different phenotypes. (H) The immune microenvironment score between two immunosenescence phenotypes. (I, J) The age difference between two immunosenescence phenotypes. (K) The survival curve between two immunosenescence phenotypes.

Fig. 2

3.1 Comprehensive construction of the machine-learning immunosenescence-related scoring (MLIRS) system

To obtain the most robust immunosenescence-related scoring system, we incorporated the aforementioned 36 immunosenescence-related genes into 101 complex machine learning algorithms in 10 datasets, calculated the C-index value of each training method in each dataset and used the machine learning method with the largest average as the final method to be incorporated into the scoring system. The final result suggested that the training method of stepcox (forward) combined with GBM had the highest average C-index, and we thus used it as the optimal method to screen genes. Finally, 13 immunosenescence-related markers were incorporated into the Cox model to construct the MLIRS system (Fig. 3A). The calculation formula of MLIRS was: 0.5584*exp(MYO9B)+ 0.2910*exp(BCL2A1)+0.3887 *exp(IGFBP7)+ 0.6343*exp(LDHA)+0.6599*exp(FNDC3B) - 0.9129*exp(LIMS1)+ 0.2154*exp(FAM83A) −0.5020*exp(SLC16A3)+ 0.3768*exp(TGFBI) −0.5582*exp(KCTD12) −0.3814*exp(TGFB2) +0.3906*exp(GLUT1)+ 0.9461*exp(MET).Fig. 3 Comprehensive construction of the consensus machine learning immunosenescence-related scoring (MLIRS) system (A) The C-index of 101 machine learning models in nine validation datasets (only showing the first 30 models). (B) KM-curve identifying the prognosis value of MLIRS in various datasets. (C) Sankey diagram showing the relationship between MLIRS and classical subtypes. (D) Correlation analysis between MLIRS and chemotherapeutic drug IC50. (E, F) The age difference between the two MLIRS groups. (G) The link between MLIRS and immunosenescence subtypes.

Fig. 3

We categorized patients with pancreatic cancer into high- and low-risk groups according to the MLIRS system in the different datasets according to the best cutoff value. The results indicated that the low-risk group in each dataset had a more ideal prognosis than the high-risk group (Fig. 3B). We collected information on the classical subtype of pancreatic cancer and compared our MLIRS with it and found that our MLIRS system is independent of these subtypes (Fig. 3C). We also explored the relationship between the MLIRS system and chemotherapeutic drug sensitivity by analyzing the IC50 values of 28 common chemotherapeutic drugs. The results suggested a positive correlation between MLIRS and the IC50 of most drugs, which illustrated the lower chemotherapeutic sensitivity that patients with a high MLIRS may exhibit (Fig. 3D). Additionally, MLIRS high groups exhibited a higher age than MLIRS low groups in the TCGA and PACA-AU-seq database, indicating immunosenescence trends (Fig. 3E and F). We further explored the link between MLIRS and immunosenescence subtypes and found that found that IMSP2 exhibited lower MLIRS scores, which is consistent with our previous findings (Fig. 3G)

3.2 Evaluation of the performance of our MLIRS system and comparison with other prognostic signatures

Subsequently, we evaluated the stability of the MLIRS system using its AUC values for overall survival in different datasets. The AUC values for the first two years were close to 0.7 and that for the 1-, 2-, and 3-year survival in TCGA-PAAD were all over 0.9 (Fig. S3A). We also assessed the C-index values in the different datasets and found that the mean C-index for the MLIRS system was over 0.66, with a C-index of 0.85 for TCGA-PAAD (Fig. S3B). We compared our MLIRS system with seven datasets containing comprehensive clinicopathologic information. Our MLIRS system had a higher C-index than the other clinicopathologic features in each dataset (Fig. S3C). Furthermore, in multivariate Cox analysis, our MLIRS system demonstrated superior prognostic ability compared to other clinicopathologic features (Figs. S4A–G).

We collected 68 published mRNA signatures as well as lncRNA signatures in pancreatic cancers and compared our MLIRS system with them; miRNA signatures were excluded due to the lack of corresponding data in the validation set. Only our MLIRS system maintained robust prognostic performance across all datasets and exhibited the characteristics of a hazard signature, demonstrating superior prognostic performance compared to other systems (Fig. 4A).Fig. 4 Comparison of our MLIRS with other prognostic signatures (A) Univariate Cox analysis of MLIRS and 68 published signatures across all datasets. (B) C-index comparison of MLIRS and 68 published signatures across all datasets (only showing the first 30 signatures).

Fig. 4

Subsequently, we compared the C-index of the MLIRS system with other prognostic features across nine datasets and the meta-cohort to further compare the robustness of prognostic performance. Overall, our MLIRS system ranked first in four datasets, namely, TCGA-PAAD, GSE28735, GSE85916, and meta-cohort. In addition, the C-index of the MLIRS system ranked second in three datasets, namely, GSE62452, EMTAB6134, and PACA-AU-array, and fifth (GSE57495), eighth (GSE79668), and fourth (PACA-AU-seq) in the other datasets (Fig. 4B). To summarize, our MLIRS system performed the best overall, with some prognostic signatures excelling in only one or two datasets (e.g., Liu X), though underperforming in others, which may be due to the simplicity of the algorithms and the datasets used for constructing the model, which is remedied by our MLIRS system.

3.3 Variation of oncogenic signal and metabolic reprogramming in different MLIRS groups

We further analyzed alterations in signaling pathways between the MLIRS high- and low-scoring groups. Fig. S5A illustrates the proportion of immunosenescence-related genes activated or inhibited in different signaling pathways in patients with pancreatic cancer. These genes were more activated in the epithelial-mesenchymal transition and the cell cycle, while they were more inhibited in DNA damage repair and the hormone AR pathway. The results in Fig. S5B suggest that these genes may promote cell cycle activation and ER hormone activity in pancreatic cancer, while inhibiting the RTK pathway and DNA damage repair. We analyzed the differentially expressed genes in the high- and low-risk groups and performed enrichment analyses to explore the signaling pathways altered (Fig. S5C). GO analysis revealed differences in “regulation of T-cell activation,” “glucose metabolic process,” “response to fatty acid,” “cellular response to drug,” and “positive regulation of cell cycle process” between the high- and low-scoring groups (Fig. S5D). KEGG analysis suggested that some oncogenic pathways, such as the “PI3K-Akt signaling pathway” and “Wnt signaling pathway,” were altered between the high- and low-scoring groups (Fig. S5E). Some metabolic and immune-related pathways, such as “glycolysis/gluconeogenesis” and “Th17 cell differentiation,” were reprogrammed.

Due to potential alterations in the metabolic microenvironment, we annotated the metabolic pathways of the KEGG database and analyzed the differential metabolic pathways between the two MLIRS groups. The results indicated reprogramming of glucose metabolism, protein metabolism, lipid metabolism, and immune cell metabolism (Fig. S5F). GSEA analyses suggested alterations of the “cell cycle,” “chemokine pathways to DNA damage repair,” and “activation of immune cell” (Fig. S5G).

3.4 Preliminary exploration of the MLIRS system in chemotherapy sensitivity and immunotherapy prediction

We validated the predictive ability of our MLIRS system for chemosensitivity in the CGP, CTRP, and GDSC databases. In the CGP database, the IC50 values of gemcitabine were higher in the high MLIRS group, suggesting that patients in this group have lower chemotherapeutic sensitivity (Fig. S6A). In the prediction results based on the CTRP database, the IC50 values of myricetin and etoposide were all lower in the low MLIRS score group, demonstrating high chemotherapeutic sensitivity in this group (Fig. S6B). In the GDSC database, the high MLIRS group was predicted to have a higher IC50 and lower sensitivity to cisplatin, gemcitabine, and oxaliplatin (Fig. S6C).

Due to the relevance of our MLIRS system to the immune microenvironment and immune checkpoints, we further explored the possibility of its application in immunotherapy. We collected the clinical characteristics of immunotherapy and targeted therapy cohorts, including the IMvigor210 cohort (anti-PDL1 therapy) and GSE78220 (anti-PD1 therapy)) to explore the ability of the MLIRS system to predict immunotherapy. In the IMvigor210 cohort, the survival time of the high MLIRS group that had undergone anti-PDL1 treatment was significantly lower than that of the low MLIRS group (Fig. 5A). The patients exhibited an overall immunotherapy response as patients with PD/SD also had significantly higher MLIRS scores than patients with CR/PR (Fig. 5B). Specifically, patients whose treatment response was CR, PR, or SD had significantly lower MLIRS scores than those with PD (Fig. 5C). In addition, the proportion of patients with PD and SD was higher in the high MLIRS score group than in the low MLIRS score group (Fig. 5D). The predicted AUC of the response to PDL1 treatment using MLIRS reached 0.62 (Fig. 5E). In the GSE78220 cohort, the prognosis of the high MLIRS group was significantly worse than that of the low MLIRS group after anti-PD1 treatment (Fig. 5F). Patients with PD exhibited significantly higher MLIRS compared to those with CR or PR (Fig. 5G). The proportion of patients with PD was significantly higher in the high MLIRS group than that in the low MLIRS group (Fig. 5H). The AUC value of MLIRS reached 0.91 for predicting immunotherapy response (Fig. 5I).Fig. 5 Predictive competence of MLIRS system in immunotherapy response (A) Survival curve between the high and low MLIRS score groups after anti-PDL1 therapy in the IMvigor210 cohort. (B) Difference of MLIRS in groups with various anti-PD-L1 responses (*P < 0.05, ns P > 0.05). (C) Difference of MLIRS in groups with various anti-PD-L1 responses (*P < 0.05, ns P > 0.05). (D) Cumulative histogram exhibiting the difference in anti-PD-L1 response between various MLIRS groups. (E) ROC curve of the MLIRS in the IMvigor210 cohort. (F) The survival curve between the high and low MLIRS groups after anti-PD1 therapy in GSE78220. (G) Difference of MLIRS in groups with various anti-PD1 responses (*P < 0.05, ns P > 0.05). (H) Cumulative histogram exhibiting the difference in anti-PD-1 response between high and low MLIRS groups in GSE78220. (I) ROC curve of MLIRS in GSE78220.

Fig. 5

3.5 External validation of our MLIRS system and identification of GLUT1 as an immunosenescence-related regulator

We detected the relative expression of these 13 model-construct genes using qRT-PCR and constructed MLIRS scores in the in-house SYSU cohort using the aforementioned methodology. Kaplan–Meier analysis showed that in the SYSU cohort the group with high MLIRS scores still had poorer OS and disease-free survival time (DFS), consistent with our previous findings (Fig. 6A and B). We further performed a multivariate analysis combining our MLIRS with clinical factors and found that our MLIRS had independent predictive ability in both OS and DFS (Fig. 6C and D). We further assessed the stability of MLIRS in predicting OS and DFS in the SYSU cohort using ROC curves and the results suggested that the 1-, 2-, and 3-year predictive AUCs of MLIRS for OS and DFS were over 0.8 (Fig. 6E and F). To further unearth indicators associated with both the immune immunosenescence microenvironment and pancreatic cancer prognosis, we first demonstrated the expression of core MLIRS genes as well as clinical features in the SYSU cohort using heatmaps. The result suggested that GLUT1 showed the highest expression in the high-risk group of patients (Fig. 6G). In addition, the MLIRS high group exhibited higher age compared to the MLIRS low group (Fig. 6H). Further correlation analysis showed the highest correlation between GLUT1 and MLIRS (r = 0.654, p = 8.16e-23), which indicated GLUT1 as a potential immune microenvironment risk windsock. Additionally, CD8 and PDL1 also exhibited a certain relationship with relation with MLIRS (Fig. 6I). GLUT1 exhibited a positive correlation with FOXP3 and a negative correlation with CD8, indicating its immunosuppressive potential (Fig. 6I). We further verified the relationship between MLIRS, GLUT1, and the immunosenescence microenvironment by immunohistochemistry at the tissue level. In the high-risk group, staining of CD8 was lower, however, that of FOXP3 and Ki67 were relatively higher, though the level of PDL1 staining was not significantly different between the two groups (Fig. 6J). As one of the key biomarkers for constructing the MLIRS system, GLUT1 staining level was higher in the high-risk group and formed a certain degree of co-expression with FOXP3 and Ki67; the specific mechanism should be carefully explored (Fig. 6J). In addition, the GLUT1 level was consistent with IL6, a significant immunosenescence and inflammation marker.Fig. 6 External validation of our MLIRS system and identification of GLUT1 as an immunosenescence related regulator (A) Kaplan–Meier curve of OS in the SYSU cohort. (B) Kaplan–Meier curve of DFS in the SYSU cohort. (C) Multivariable Cox analysis of OS for our MLIRS and clinical factors in the SYSU cohort. (D) Multivariable Cox analysis of DFS for our MLIRS and clinical factors in the SYSU cohort. (E) ROC curve for OS in the SYSU cohort. (F) ROC curve for DFS in the SYSU cohort. (G) Expression of MLIRS constructs genes in the SYSU cohort. (H) Age difference in various MLIRS groups. (I) Correlation analysis of MLIRS, GLUT1 with CD8, PDL1, and FOXP3. (J) Immunohistochemistry showing the relationship between GLUT1 and the immunosuppressive microenvironment.

Fig. 6

3.6 GLUT1 exhibits crosstalk with pancreatic cancer progression and gemcitabine resistance

We deleted the expression of GLUT1 in various pancreatic cancer cell lines and found the highest level in PANC-1 and MiaPaCa-2 (Fig. 7A). We then knocked down the expression level of GLUT1 in PANC-1 and MiaPaCa-2 cell lines with small interfering RNA to explore whether it has a promotional effect on pancreatic carcinogenesis (Fig. 7B). We performed Transwell assays as well as EDU proliferation assays at the cytological level to verify the effect of GLUT1 on the proliferation and cell migration of pancreatic cancer cells. The results showed a significant decrease in proliferative and migratory abilities in the GLUT1-knockdown group compared with that in the control group (Fig. 7C–G). To further investigate the role of GLUT1 in gemcitabine resistance in pancreatic cancer, we constructed pancreatic cancer-resistant cell lines of BXPC-3 and CFPAC-1 (for gemcitabine) and silenced the expression of GLUT1 with siRNA (Fig. 7H). IC50 assay experiments suggested a significant decrease in the IC50 of gemcitabine resistance in the GLUT1-silenced group, with the IC50 of BR (gemcitabine-resistant BXPC-3) decreasing from 950 nmol to 150 nmol. The IC50 of CR (gemcitabine-resistant CFPAC-1) also decreased from 276 nmol to 67 nmol (Fig. 7I). In addition, the proliferative ability of the GLUT1 knockdown group was also significantly decreased in the gemcitabine environment, whereas the level of apoptosis was significantly increased, which suggests that GLUT1 regulates, to a certain extent, gemcitabine resistance to pancreatic cancer (Fig. 7J–L).Fig. 7 GLUT1 exhibiting crosstalk with pancreatic cancer progression and gemcitabine resistance (A) The expression level of GLUT1 in various pancreatic cancer cell lines. (B) The gene silencing of GLUT1 in PANC-1 and MiaPaCa-2. (C–D) Transwell experiment showing that GLUT1 promotes migration of pancreatic cancer. (E–G) EDU experiment showing that GLUT1 promotes migration of pancreatic cancer. (H) The gene silencing of GLUT1 in resistant BXPC-3 and CFPAC-1. (I) The gemcitabine IC50 in various gemcitabine-resistant groups. (J) The cell viability of various gemcitabine-resistant groups. (K, L) The apoptosis rate of various gemcitabine-resistant groups.

Fig. 7

4 Discussion

The five-year survival rate for pancreatic cancer remains around ten percent compared to better survival rates for other gastrointestinal tumors [1]. Aside from early surgical resection and advanced combination chemotherapy, treatment options for patients with pancreatic cancer remain relatively limited [15]. Despite extensive research into the pathogenesis and chemotherapeutic resistance mechanisms of pancreatic cancer, there remains a lack of effective clinical targets [16,17]. Increasing evidence has indicated that immunosenescence may exert a crucial effect on the immune microenvironment in pancreatic cancer [18,19]. However, fundamental immunosenescence-related mechanistic explorations are still based on cellular or animal experiments and multiomics machine learning approaches to explore the pancreatic cancer immunosenescence microenvironment are lacking. To fill this gap, we extracted more immunosenescence-related prognostic biomarkers from transcriptome immunosenescence heterogeneity phenotypes, applied multiple machine learning methods to calculate the average C-index values in training and validation datasets, and developed a composite machine learning immunosenescence risk score, MLIRS. Our MLIRS score perfectly differentiated patient prognosis, immune infiltration status, mutation landscape, chemotherapy, and immunotherapy sensitivity. Among immunosenescence-related genes, we found that GLUT1 may be related to immune microenvironmental properties and can promote the development of pancreatic cancer.

Different immunosenescence statuses in the microenvironment may exert an important effect on tumor progression and responsiveness to various treatments [20,21]. We identified different phenotypes of the immunosenescence microenvironment at the transcriptome level. The results indicated that immunological features, cytokine infiltration, immune cell abundance, and immune scores were significantly higher in IMSP2 than in IMSP1. IMSP1 may represent an immunosenescence phenotype at the transcriptome level, whereas IMSP2 may reflect an immune-infiltrated phenotype. Further, single-cell and transcriptome sequencing may be needed for clinical application and translation.

Application of machine learning algorithms centered around artificial intelligence in the screening of pancreatic cancer biomarkers is gradually increasing [22,23]. However, the application of 10-fold composite models in pancreatic cancer is still lacking. In our study, we compared the training results of 101 composite machine-learning methods in 10 datasets to obtain the best composite machine-learning scoring system. Overall, our scoring system was able to perfectly differentiate the prognostic outcomes, immune infiltration characteristics, and mutation landscape of different risk groups. The high-risk group showed a higher number of mutations and mutation burden and a lower abundance of infiltrating immune cells. Conversely, the low-risk group showed better sensitivity to chemotherapy and immunotherapy. With regard to clinical application, our scoring system is based on the expression of immunosenescence-related biomarkers and the calculation of this score can be realized by clinical sequencing or PCR quantification. Further improvement by combining some clinical indicators may render our scoring system more accurate and easier to apply, which will be the direction of our future efforts.

We attached some concerns on enriching and analyzing the pathways and molecule variations between different MLIRS risk groups, as this may help us unearth molecular mechanisms and targets for immunosenescence alterations. Our results suggest that immunosenescence-associated genes contribute to alterations in various pathways, including the epithelial-mesenchymal transition, cell cycle, DNA damage repair, and the hormone AR pathway. We also found that altered immunosenescence scores are related to pathway enrichment mainly in metabolic, immune, and drug-related pathways. Further differential analysis of metabolic pathway enrichment suggested that the alterations in metabolic reprogramming caused by immunosenescence score changes mainly involve lipid metabolism, glucose metabolism, and protein metabolism. These results suggest that alterations in the immunosenescence microenvironment are often accompanied by metabolic reprogramming. Overall, synergistic analysis of metabolism and the immunosenescence microenvironment may bring new breakthroughs for identifying therapeutic targets for pancreatic cancer.

We shifted some of our research focus to the identification of immunosenescence microenvironmental regulatory targets. Through our machine learning process of the MLIRS system and validation in our SYSU cohort, we identified an immunosenescence microenvironmental indicator, GLUT1, for pancreatic cancer. Previous studies have shown that knockdown of GLUT1 sensitizes tumors to antitumor immunity and synergizes with anti-PD-1 therapies via the TNF-α pathway [24]. Additionally, GLUT1-dominated regulation of metabolic reprogramming promotes lipopolysaccharide-dependent inflammatory responses in macrophages and further regulates microenvironmental activity [25]. Some scientists found that GLUT1 may be a poor prognostic factor and linked to NACRT adjuvant therapy in pancreatic cancer [26,27]. However, a gap remains in the relationship between GLUT1 and pancreatic cancer progression and gemcitabine resistance at the basal cellular level. In our current research, we elucidated the importance of GLUT1 in the pancreatic cancer immunosenescence microenvironment and gemcitabine resistance. We found that GLUT1 had a positive correlation with the immunosenescence regulator IL6. Mechanically, the inflammatory response induced by GLUT1 may be a potential cause of elevated IL6. Cronwell et al. found that GLUT1-induced metabolic reprogramming promoted chronic inflammation, and this inflammatory process coincided with elevated levels of IL6 expression. GLUT1 and IL6 may have a certain synergistic regulatory role in the chronic inflammatory response [3,4]. Additionally, pharmacological regulation of GLUT1 expression also attenuates lung tissue damage, neutrophil accumulation and release of pro-inflammatory factors such as IL6, further corroborating that GLUT1 may regulate IL6 expression by modulating the level of inflammatory response [5]. Interestingly, GLUT1 can also regulate changes in IL6 expression through immunomodulation-related mechanisms. Cai et al. found that LMP1/GLUT1mediated glycolytic processes can regulate the production of IL-1β, IL-6, and GM-CSF through the NLRP3 inflammasome, COX-2, and P-p65 signaling pathways, which enhances the tumor-associated MDSC amplification, leading to nasopharyngeal carcinoma tumor immunosuppression [6]. We also verified the potential driving role of GLUT1 in pancreatic cancer migration. From the cellular level, many scientists have identified the effects of GLUT1 on cell migration in other tumors. Zhang et al. found that GLUT1 promotes cell migration and glycolysis in colorectal cancer [7]. Chen et al. also found that GLUT1 promotes cell migration and proliferation in glioblastoma [8]. Research findings by some scientists may reveal the mechanism behind this. Glucose metabolism reprogramming and extracellular matrix remodeling may be potentially important causes. Extracellular matrix remodeling associated with enhanced glycolysis has been shown to lead to GLUT1 internalization and enrichment on the lipid membrane, which ultimately leads to increased cell migration [9]. Additionally, GLUT1 degradation through SUMOylation and ubiquitination may regulate the AMPK-mTOR pathway [10], inhibit glycolysis and result in the proliferation and migration of nasopharyngeal carcinoma. From the perspective of these studies, we may find thatGLUT1 could serve as an important biomarker in the immunosenescence microenvironment and pathogenic process of pancreatic cancer; however, further animal experiments and clinical work are needed to promote the research and application of GLUT1 in pancreatic cancer.

The innovation points of our study lie in the integration of multiomics and multidatasets and in the application of a composite multimodel machine learning approach, which greatly reduces randomness and increases the stability of the results. Our study is based on high-throughput analysis of transcriptome sequencing, which ultimately finds the therapeutic value of the core genes and is also well validated in local datasets. However, our study lacks animal experiments and the development of clinical applications and we still need to refine our research in future studies.

5 Conclusions

To conclude, our study revealed immunosenescence-related phenotypes based on transcriptome level and identified the MLIRS system and immunosenescence indicator GLUT1 with robust performance based on a composite machine-learning approach. Our machine-learning model perfectly distinguished different groups with respect to immune infiltration abundance, and sensitivities to chemo- and immuno-therapeutic treatments. The identified machine learning prediction target, GLUT1, also demonstrated potential competence for clinical practice.

Ethics approval and consent to participate

The Institutional Research Ethics Committee of Sun Yat-Sen University (SYSU) approved this study, and written informed consent was obtained from all patients prior to the investigation. The ethics approval number was IIT-2021-719. We confirmed that the experiment on human tissue samples were performed in accordance with relevant guidelines and regulations.

Funding

The authors greatly acknowledge the financial support from National Natural ScienceFoundation of China (82072644 , 82002501 , 82103401 , 82203473 , 82103267 ), the 10.13039/501100021171 Guangdong Basic and Applied Basic Research Foundation (2024A1515010487 , 2024A1515010400 , 2021A1515111104 , 2021A1515010123 ) and the 10.13039/501100002858 China Postdoctoral Science Foundation (2023M744050 ).

Consent for publication

Not applicable.

Data availability statement

The data used in this study are all available from the TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/) and ICGC database (https://dcc.icgc.org/). The Materials and Methods section listed the accession numbers of publicly available datasets. The corresponding author would provide other relevant data upon reasonable request.

CRediT authorship contribution statement

Si-Yuan Lu: Formal analysis, Data curation, Conceptualization. Qiong-Cong Xu: Conceptualization. De-Liang Fang: Methodology, Conceptualization. Yin-Hao Shi: Software, Resources. Ying-Qin Zhu: Validation, Supervision, Software. Zhi-De Liu: Validation. Ming-Jian Ma: Software, Project administration, Conceptualization. Jing-Yuan Ye: Writing – original draft. Xiao Yu Yin: Visualization, Validation.

Declaration of competing interest

The authors declare that they have no competing interests.

Appendix A Supplementary data

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Acknowledgments

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36684.
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