Analysis of Immune Infiltration and Clinical Significance of Lysosome-Related Genes in Colon Adenocarcinoma

doi:10.21203/rs.3.rs-2741921/v1

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Analysis of Immune Infiltration and Clinical Significance of Lysosome-Related Genes in Colon Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-2741921/v1

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Background

Immunotherapy has become increasingly important in the treatment of various malignant tumors. The interaction between tumor cells and the tumor microenvironment (TME) plays a crucial role in the efficacy of immunotherapy. The phenotype of cancer cells is thought to be influenced by lysosomal activity. The overall activity of lysosomes, including autophagy, has been shown to inhibit tumor occurrence. Nonetheless, the relationship between lysosomal function in Colon Adenocarcinoma (COAD) and clinical prognosis, immune cell infiltration, and response to immunotherapy remain to be elucidated.

Methods

We thoroughly determined the relationship between lysosomes and transcription patterns, prognosis, and immune cell infiltration by carefully evaluating 32 lysosomal-related genes (LRGs). Subsequently, we evaluated their value in COAD treatment outcomes and prognosis prediction.

Results

We explored the genetic changes of LRGs in COAD samples and discovered their expression patterns using the TCGA and GEO databases. We identified two relevant subgroups as well as Established that clinical-pathological features, prognosis, and invasive TME were all associated with Coad mutations. We then developed an LRG_score to predict overall survival (OS) and demonstrated its accuracy as a predictive indicator for COAD patients. We also developed an extremely accurate nomogram chart for the clinical feasibility of LRG_score. Finally, drug sensitivity analysis revealed a strong association between LRG_score and anti-cancer drugs.

Conclusion

Overall, we identified the prognostic LRG characteristics of COAD patients. This characteristic may help to elucidate the features of TME and explore more effective immunotherapy strategies.

Colon adenocarcinoma

lysosome

prognosis

tumor microenvironment

immunotherapy

Colon adenocarcinoma (COAD) is a frequently occurring malignant tumor of the digestive tract that originates from the colon epithelium. it represents one of several pathological types of colon cancer and is the most prevalent malignancy of the digestive tract globally. it has the third-highest incidence rate and the fourth-highest mortality rate among all cancers worldwide¹. Despite substantial progress in early diagnosis, surgical intervention, and chemotherapy in recent decades, the prognosis of COAD patients remains poor². High recurrence and metastasis rates lead to poor prognosis and decreased survival rates for COAD patients. Hence, attaining a more profound insight into the molecular mechanisms governing COAD may promote the development of innovative strategies for prevention and treatment³.

Lysosomes, as an important organelle, play an essential role in various biological processes, including degradation, innate and adaptive immune responses, and nutrient sensing⁴. In addition, lysosomal activity is related to malignant cell phenotypes. Autophagy, a process involving lysosomal activity, has been demonstrated to have a dual effect on tumorigenesis. it can suppress the initiation of tumors by inhibiting the transformation of pre-cancerous cells. Conversely, it can also promote the progression of established tumors by facilitating cancer cell survival and adaptation to various stressors^5,6. Lysosomes control cancer cell growth by modifying growth factor signaling routes and supplying essential nutrients. Growth factor signals are initiated by receptor tyrosine kinases (RTKs) on the plasma membrane, and their activity can be restricted by intracellular degradation in lysosomes⁷.

The immune system has various functions, including defense, homeostasis, and surveillance, and is a complex biological system responsible for sustaining homeostasis in multicellular organisms. It shields the body against diverse pathogens and hazardous materials, eradicates intruding pathogens, supervises the internal milieu by identifying and eliminating anomalous components, and upholds immune equilibrium via immune tolerance and regulation⁸.

Within the tumor microenvironment (TME), T cells are responsible for eradicating cancer cells. Upon recognition of tumor antigens by the T cell receptor, cytotoxic T lymphocytes secrete perforin and granzyme B from lysosomes, ultimately resulting in target cell death⁹. Autophagy has been shown to influence the homeostasis, differentiation, function, and senescence of T cells¹⁰. T cells (Tregs) are an immunosuppressive subset of T cells in the TME. Conditional deletion of ATG5 or ATG7 in Tregs results in impaired survival and stability, which subsequently inhibits colon adenocarcinoma (COAD) growth. This inhibition is caused by autophagy defects, which lead to the upregulation of mTORC1, c-Myc, and glycolytic metabolism¹¹. A comparable increase in glycolytic metabolism associated with mTORC1 was observed in Tregs that lacked lysosomal TRAF3 interacting protein 3 (TRAF3IP3)¹².

Although we have discovered the relationship between lysosomes and TME, there is little research on the exact role of lysosomes in TME. Therefore, to explore the correlation between lysosomes and TME, bioinformatics and predictive modeling methods were used for evaluation. Furthermore, we investigated the prognostic significance of genes related to lysosomal function. In this study, an unsupervised clustering algorithm to cluster lysosomal-related genes and obtained corresponding lysosomal subtypes. By comparing the lysosomal subtypes, significant differences were found in enrichment annotation, TME, and immune therapy response between the two subtypes. Subsequently, the key genes in the lysosomal pathway were identified as having important prognostic value for COAD patients. To further study, we used the Lasso-Cox algorithm to select six prognostic-related genes and developed a model related to lysosomal function for patients with COAD, utilizing these genes. Finally, survival probability was quantified by constructing nomograms, risk scoring, and clinical indicators.

Data Collection

RNA expression profiles and clinical information of 454 COAD patients from TCGA (https://portal.gdc.cancer.gov/cart) database were selected as training dataset¹³. The clinical data and gene expression data of the validation group were obtained from the GSE17538 in the GEO database. Samples with missing clinical pathology data and survival data were excluded from the analysis. LRGs were obtained from Gencards(Socre > 20).

Consensus Clustering Analysis of LRGs

Consensus clustering is an unsupervised clustering method. In this study, different lysosome-related patterns were defined using the PAM algorithm. The construction of the algorithm can be accomplished using the "ConsensusClusterPlus" package¹⁴.

Clinical pathology characteristics and prognosis of COAD patients

We investigated the relationship between clinical pathological characteristics and survival outcomes as well as molecular patterns to evaluate the clinical importance of clustering. Clinical variables included age, gender, stage, T stage, N stage, and M stage. Moreover, the differences in COAD between different patterns were evaluated with Kaplan–Meier analysis obtained by the “survival” and “survminer” packages¹⁵.

Relationship between COAD and TME

The CIBERSORT algorithm can calculate the levels of 22 immune cells by analyzing the expression of signature genes, subsequently providing a matrix score¹⁶.

Functional enrichment analysis and identification of DEGs

Utilizing the "limma" package with p value༜0.05 and q༜value 0.05 to identify the DEGs in different lysosome-related subgroups. Based on these DEGs, GO and KEGG analysis was carried out with the “cluster profile” package¹⁷.

Establishment and validation pertaining to the risk model.

The "glmnet" package to screen for genes with prognostic significance, followed by LASSO analysis to select the minimum λ value as the optimal value. Subsequently, utilized multiCox analysis was to identify the genes that were employed in constructing the risk model. the risk scores for both the training and validation groups were computed utilizing the following equation:= (0.0045×CTSD) + (0.0390×IDUA) + (-0.0227×GLA) + (-0.0051×IDUA) + (-0.0114×FUCA1) + (-0.0222×NAGLU) + (0.0086×M6PR)¹⁸. The risk scores and predictive efficiency of the model were evaluated using roc and mar residual methods.

LRG_score Clinical correlation analysis.

The relationship between LRGs results and clinical data was investigated. A uniCox analysis and multiCox analysis were performed on the entire cohort to confirm whether LRG_score was a prognostic autonomous variable. We also studied the relationship between tumor mutation load and LRG_score and examined the levels of immune matrix scores and immune cell infiltration at different risk levels.

Establishment and prediction analysis of nomograph chart

The nomograph chart displays the risk scores and other clinical pathology characteristics of COAD patients and predicts the clinical outcome of 1, 3, and 5-year OS. Subsequently, Decision curve analysis (DCA) and calibration curve analysis were employed to validate the clinical reliability of the created nomogram chart.

Mutation and Drug Sensitivity Analysis

Based on the largest pharmacogenomics database, named Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/), we used the R package “pRRophetic” to predict the chemosensitivity of each tumor sample¹⁹.

Statistical Analysis

The R software (version 4.1.2) and associated utilities were employed for data processing, analysis, and visualization.

Genetic Mutation Landscape of LRGs in COAD

Somatic mutations were identified by examining the occurrence rates of 31 LRGs. As shown in Figure A1, among 454 COAD samples, 116 (25.55%) had gene mutations. The results revealed that NPC1 had the highest mutation rate among the 31 LRGs, followed by GAA and GNPTAB. We also discussed the occurrence rates of CNV mutations, where significant variations were observed in CNV mutations of the 31 LRGs (Fig. 1B). In addition, we constructed a protein-protein interaction (PPI) analysis through the STRING website to illustrate the interactions between LRGs (Fig. 1C). The transcript abundance of the 31 LRGs in tumor and normal tissues was determined using the TCGA-COAD dataset, and 24 DEGs were identified, most of which were abundant in tumors (Fig. 1D). Our study suggests that LRGs have potential roles in the formation and advancement of COAD, as they exhibit significant differences in expression levels and genomic background between cancerous and normal tissues.

Generation of Lysosomes Subgroups in COAD

A total of 424 COAD individuals from the TCGA-Coad repository were included as subjects in this research. the correlation between lysosomes and cancer was evaluated by analyzing the selected patients. Table S1 displays the complete data for the patients. In 31 COAD patients, we evaluated the prognostic value of LRGs using uniCox. Subsequently, using consensus clustering analysis, we additionally explored the connection between the expression patterns of LRCs and COAD subtypes. Based on the expression levels of LRG, COAD patients were classified. According to the research findings, the optimal value for the clustering variable was 2, enabling an effective distribution of patients across the entire cohort between C1 (n = 246) and C2 (n = 178) (Fig. 2A). Next, we assessed the overall survival (OS)of both patient groups and discovered a substantial distinction in the survival rate between them (Fig. 2B). Finally, through genome comparison, significant differences were observed in the expression of LRGs in the clinical and pathological characteristics of the two clusters, as presented in Fig. 2C.

Features of the TME in Various Subgroups

To evaluate the penetration levels of 22 distinct human immune cell categories in the two clusters utilizing the CIBERSORT approach to investigate the association between LRGs and the TME in COAD patients (Fig. 3A). The study revealed significant variations in the abundance of immune cells between the two clusters. The C1 group was significantly better than the C2 group in terms of plasma cells, CD4 + memory-activated T cells, CD4 + memory-activated T cell concentration, and expression reversal of regulatory T cells (Tregs), NK cell activation, macrophages, and so on, while the opposite was true for M0 dendritic cells at rest, dendritic cell activation, and eosinophils expression. As the TME score can assess the levels of immune and matrix components in the TME, we utilized estimation algorithms to obtain the TME score in all clusters. Based on the study, we found that the TME score of cluster C2 was notably greater than that of cluster C1 (Fig. 3C).

Gene Subgroup Identification Using DEGs and Functional Analysis

Utilizing the "limma" package, we identified 1,601 differentially expressed genes (DEGs) associated with lysosomes. To further explore this, we conducted a functional enrichment analysis on the potential biological activities of these DEGs (Table S2). GO enrichment indicated that DEGs are enriched in leukocyte-mediated immunity, positive regulation of cell activation, negative regulation of immune system activity, leukocyte migration, endocytic vesicles, and immune receptor activity (Fig. 4A). Similarly, KEGG enrichment analysis also identified several immune and lysosome-related processes, signaling pathways, and diseases, including phagosome, rheumatoid arthritis, cell adhesion molecules, the IgA-mediated intestinal immune network, and chemokine signaling pathways (Fig. 4B). the GO and KEGG functional analyses demonstrated significant correlations between these processes and tumor development and immunity, emphasizing the potential association between lysosomal function and immunoregulation in COAD.

Establishment of LRG-Based Risk Model.

To assess the prognostic significance of LRGs in COAD, a risk model was developed. First, by using LASSO-COX analysis, identified candidate genes were for developing a risk model. and the selected genes were then evaluated (Fig. 5A). Through calculation, we identified six key genes: CTSD, IDUA, GLA, FUCCA1, NAGLU, and M6PR, and demonstrated via Kaplan-Meier analysis that these genes could be crucial prognostic indicators for COAD patients, As shown in Fig. 5B. Furthermore, the established risk model could effectively stratify COAD patients into high-risk and low-risk groups based on the expression levels of identified genes, with higher expression observed in the high-risk group as compared to the low-risk group. (Fig. 5C). Generally, patients in the low-risk group are expected to have longer survival than those in the high-risk group. As illustrated in Fig. 5D, the performance evaluation results of the risk model were represented using the ROC curve. The analysis indicated that the established risk model had high accuracy for predicting patient prognosis within five years, with corresponding areas under the ROC curve at one, three, and five years being 0.701, 0.677, and 0.775, respectively (Fig. 5E). To evaluate the prediction stability of the risk model, we studied the risk model for both test and training cohorts. Patients were stratified into different subgroups based on the median of the training cohort. The subsequent survival analysis revealed a significantly higher OS rate in the low-risk group of patients compared to the high-risk group, which was based on the median of the training cohort. The high AUC value of this risk model indicates its effectiveness in assessing the prognosis of COAD patients. Finally, we also predicted the survival probabilities for one, two, and three years.

Clinical Analysis of the Prognostic

Using uniCox and multiCox analyses to investigate the prognostic independence of various clinical parameters, in order to evaluate the independent prognostic significance of LRGs in COAD patients. The study showed that age, T stage, and risk scores were independent factors that could be used for effectively assessing the prognosis of COAD patients, as illustrated in Fig. 6A. Subsequently, a nomogram was created as a clinically relevant quantitative method, and this nomogram can be used in conjunction with various prognostic factors to help clinicians predict the 1, 3, and 5-year OS of COAD patients (Fig. 6B). The Nomogram's Calibration Curve Showed a High Degree of Accuracy in Predictions. (Fig. 6C).

Features of the TME in Various Risk Scores

The CIBERSORT algorithm is used to evaluate the relationship between the risk group and immune cell infiltration. As demonstrated in Fig. 7A, a positive correlation is observed between the risk group and the infiltration of macrophage M0, resting NK cells, and regulatory T cells (Tregs), but is negatively correlated with follicle-assisted plasma cells and resting CD4 memory T cells. Furthermore, the research identified a strong correlation between the immune score and matrix score within the risk group. (Fig. 7B). Next, we investigate the connection between immune cell enrichment and prognostic genes. The research results indicate that the selected genes exhibited a close association with the majority of immune cells. (Fig. 7C).

Association of Risk scores with TMB

Our findings demonstrated that the high-risk group had a greater Tumor Mutational Burden (TMB) compared to the low-risk group. (Fig. 8A), Implying that high-risk patients may derive greater benefits from immunotherapy. We also examined the distribution of somatic mutations in the TCGA-COAD dataset according to the risk scores model. The mutation rates of APC, TP53, TTN, KRAS, PIK3CA, SYNE1, MUC16, and FAT4 were observed to be equal to or greater than 20% among COAD patients in both risk groups. as shown in Figs. 8B and C.

Drug Sensitivity Analysis

Through computation of IC50 values of 138 drugs in TCGA-COAD patients. we identified the risk scores as a biomarker to predict treatment response among COAD patients. The research indicates that patients with low-risk scores could potentially respond favorably to ABT263, AZD6244, BI2536, Elesclomol, MS.275, NVPTAE684, Pazopanib, and Roscovitine (Figs. 9A-H). Overall, these findings suggest a relationship between LRGs and drug sensitivity.

Lysosomes are integral constituents of the cellular membrane system and participate in various cellular processes. Apart from their role in the degradation of cellular components, lysosomes also play a crucial role in various cell-mediated processes like intracellular pathogen clearance, membrane restoration, antigen presentation, cell adhesion and migration, programmed cell death, metabolic signaling, extracellular vesicle release, and regulation of gene expression. These processes are intimately associated with the development and progression of cancer^20,21. Existing research indicates that lysosomes mainly help cancer cells cope with environmental stress and participate in the occurrence and development of cancer by directly degrading non-essential macromolecules via the autophagy pathway²². Furthermore, lysosomes can participate in cancer immune regulation by regulating, transporting, and degrading cell surface molecules such as cytotoxic T lymphocyte antigen-4 (CTLA-4)^23–25.

Further research indicates that many enzyme activities in lysosomes are significantly enhanced in cancer tissues compared to precancerous tissues, indicating the active function of lysosomes²⁶. Additionally, the spatial distribution of lysosomes is linked to their function. In cancer cells, the driving proteins and motor proteins that govern lysosome movement are subject to dynamic alterations, thereby influencing the position of lysosomes within the cell. The spatial arrangement of lysosomes is closely associated with drug resistance and tumor cell metastasis. Hence, modifications in lysosome function and spatial distribution are intimately linked to the development and progression of tumors^27,28.

However, many reports only focus on individual LRGs or specific immune cell subpopulations. Consequently, it is imperative to provide a more comprehensive elucidation of the overarching implications and regulatory characteristics of LRG combinations on TME permeability. Based on the TCGA-COAD dataset, we determined the transcriptional data and expression of LRGs. Although the mutation intensity of LRGs is low, the majority of the highly upregulated LRGs are linked to the prognosis of COAD. Using unsupervised clustering algorithms, the COAD patients were segregated into clusters C1 and C2. The differences between these two types in clinical outcomes, immune infiltration, and functional features were obvious. Since the response of COAD to immunotherapy may be affected by genetic changes, two COAD gene clusters were created based on DEGs associated with subgroups, each with different clinical features, immune activity, and function. LRG_score was quantified for the subgroups using the Lasso Cox regression method, and it was found that the LRG_score was highest in the clinical outcome worst gene cluster C1. It is worth noting that patients with high LRG_score often have poorer COAD, this suggests that a high LRG score might possibly suggest a poor prognosis. The data showed that, under the control of confounding variables, LRG_score serves as an independent prognostic indicator of the survival outcomes in COAD patients. Additionally, this model's predictions had high robustness at 1, 3, and 5 years, which was verified in the ROC. Because cancer development is the result of accumulated gene mutations, we have created lysosomal risk scores based on clinical outcomes and lysosome-related genes in COAD patients, which enables LRG_SCORE to be a reliable predictor of patient prognosis.

Our results suggest that individuals with low LRG scores and high LRG scores have significantly different genetic alterations. Elevated TMB is linked to a more favorable prognosis in patients, which is in line with our study results. The low LRG_score group exhibited superior clinical efficacy as compared to the low TMB group, implying that the LRG_score can independently predict immune efficacy. The TME primarily comprises immune cells and stromal cells, and immunologic and stromal scores are linked to clinical characteristics and prognosis. We used estimation algorithms to estimate these scores and found that groups with higher LRG_score had higher immune and stromal scores. This suggests that lysosomes may control the occurrence and growth of tumors by interacting with the TME. In addition, the study found a correlation between a high number of T cells (helper T cells, CD4+, and CD8 + T cells) and a low LRG_score. Enrichment of Tregs that inhibit anti-tumor immune reactivity is associated with lower survival rates, and high LRG_score patients have abundant Tregs in the TME, which is consistent with our observation. Therefore, lysosome-targeted therapy for COAD may be effective immunotherapy. We identified drugs that may be sensitive to different LRG_score groups and combining these treatments with lysosome-targeted therapy may assist in diminishing drug resistance and enhancing clinical results. Our study has some limitations, as the information from public databases is retrospectively collected, and its robustness may be influenced by inherent selection bias. To correctly investigate the clinical effectiveness of LRG_score, there should be additional clinical factors included in the study. Large-scale prospective studies and additional in vivo and in vitro experimental research are warranted to comprehensively comprehend the correlation between risk score and TME, as well as to authenticate and reinforce our findings.

In a nutshell, we conducted a systematic study on LRGs and established a comprehensive program to modulate the prognosis, TME, and clinical features of COAD patients. We further elucidated the effectiveness of LRGs as therapeutic response markers. This study highlights the key therapeutic role of LRGs and provides an important foundation for future research on individualized treatment for COAD patients.

Acknowledgments

We are grateful to Prof. Qian Xu (Nanyang Normal University) for providing technical assistance.

Author contributions

C-D Tang, L-G Yao, X Zhang, and Y-C Kan conceived the study; Y-L Xiao, B Liu, and C Yue were responsible for data curation; Y-L Xiao and B Liu were responsible for formal analysis. C-D Tang and N Ma were responsible for funding acquisition; X Zhang, C-D Tang, and Y-L Xiao were responsible for the investigation; C-D Tang, L-G Yao, N Ma, and Y-C Kan provided supervision; X Zhang and X Shi were responsible for visualization and wrote the original draft; C-D Tang reviewed and edited the manuscript. All authors read and approved the ﬁnal manuscript.

Funding

This paper was supported by the grants from Scientific and Technological Project of China (31900916), the Program for Science＆Technology Innovation Talents in Universities of Henan Province (No. 21HASTIT041), Youth Talent Support Project of Henan Association for Science and Technology (No. 2021HYTP036), Henan Science and Technology Research Project (222102110260), the Key Scientific Research Projects of Higher Education Institutions in Henan Province(22A180026).

Data availability

All data generated or analyzed during this study were included either in this article's methods section. Other data that support the ﬁndings of this study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

This study was approved by the Institutional Animal Care and Use Committee of Nanyang Normal University (Approval number 2201015) and conformed to the guidelines of the National Institute of Health (NIH) policies in the Guide for the Care and Use of Laboratory Animals (NIH Publications No. 80-23, revised 1996).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no known competing for ﬁnancial interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.

Siegel, R. L. et al. Colorectal cancer statistics, 2017. CA Cancer J Clin 67, 177–193 (2017).
Punt, C. J. A., Koopman, M. & Vermeulen, L. From tumour heterogeneity to advances in precision treatment of colorectal cancer. Nat Rev Clin Oncol 14, 235–246 (2017).
Kgm, B. & Mj, S. Progress and future direction in the management of advanced colorectal cancer. The British journal of surgery 105, (2018).
Trivedi, P. C., Bartlett, J. J. & Pulinilkunnil, T. Lysosomal Biology and Function: Modern View of Cellular Debris Bin. Cells 9, 1131 (2020).
Maiuri, M. C. & Kroemer, G. Therapeutic modulation of autophagy: which disease comes first? Cell Death Differ 26, 680–689 (2019).
Rybstein, M. D., Bravo-San Pedro, J. M., Kroemer, G. & Galluzzi, L. The autophagic network and cancer. Nat Cell Biol 20, 243–251 (2018).
Goh, L. K. & Sorkin, A. Endocytosis of receptor tyrosine kinases. Cold Spring Harb Perspect Biol 5, a017459 (2013).
Sui, Y., Li, S., Fu, X.-Q., Zhao, Z. J. & Xing, S. Bioinformatics analyses of combined databases identify shared differentially expressed genes in cancer and autoimmune disease. Journal of Translational Medicine 21, 109 (2023).
Voskoboinik, I., Whisstock, J. C. & Trapani, J. A. Perforin and granzymes: function, dysfunction and human pathology. Nat Rev Immunol 15, 388–400 (2015).
Macian, F. Autophagy in T Cell Function and Aging. Front Cell Dev Biol 7, 213 (2019).
Wei, J. et al. Autophagy enforces functional integrity of regulatory T cells by coupling environmental cues and metabolic homeostasis. Nat Immunol 17, 277–285 (2016).
Yu, X. et al. Metabolic control of regulatory T cell stability and function by TRAF3IP3 at the lysosome. J Exp Med 215, 2463–2476 (2018).
Bu, F. et al. Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response. Cell Oncol (Dordr) 45, 1363–1381 (2022).
Seiler, M., Huang, C. C., Szalma, S. & Bhanot, G. ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data. OMICS 14, 109–113 (2010).
Rich, J. T. et al. A practical guide to understanding Kaplan-Meier curves. Otolaryngol Head Neck Surg 143, 331–336 (2010).
Zhou, S., Lu, H. & Xiong, M. Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Rheumatoid Arthritis by Bioinformatics Analysis. Front Immunol 12, 726747 (2021).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Zhao, Z. et al. Comparison of Microvascular Decompression and Two Isocenters Gamma Knife for the Treatment of Trigeminal Neuralgia Caused by Vertebrobasilar Dolichoectasia. Front Neurol 12, 707985 (2021).
Rao, B. et al. Clinical outcomes of active specific immunotherapy in advanced colorectal cancer and suspected minimal residual colorectal cancer: a meta-analysis and system review. J Transl Med 9, 17 (2011).
Ballabio, A. The awesome lysosome. EMBO Mol Med 8, 73–76 (2016).
Eskelinen, E.-L. Roles of LAMP-1 and LAMP-2 in lysosome biogenesis and autophagy. Mol Aspects Med 27, 495–502 (2006).
Levy, J. M. M., Towers, C. G. & Thorburn, A. Targeting autophagy in cancer. Nat Rev Cancer 17, 528–542 (2017).
Romao, S., Gannage, M. & Münz, C. Checking the garbage bin for problems in the house, or how autophagy assists in antigen presentation to the immune system. Semin Cancer Biol 23, 391–396 (2013).
van Kasteren, S. I. & Overkleeft, H. S. Endo-lysosomal proteases in antigen presentation. Curr Opin Chem Biol 23, 8–15 (2014).
Settembre, C., Fraldi, A., Medina, D. L. & Ballabio, A. Signals from the lysosome: a control centre for cellular clearance and energy metabolism. Nat Rev Mol Cell Biol 14, 283–296 (2013).
Kirkegaard, T. & Jäättelä, M. Lysosomal involvement in cell death and cancer. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1793, 746–754 (2009).
Pu, J., Guardia, C. M., Keren-Kaplan, T. & Bonifacino, J. S. Mechanisms and functions of lysosome positioning. J Cell Sci 129, 4329–4339 (2016).
Davidson, S. M. & Vander Heiden, M. G. Critical Functions of the Lysosome in Cancer Biology. Annu Rev Pharmacol Toxicol 57, 481–507 (2017).

Supplementary Tables S1-S2 are not available with this version

No competing interests reported.

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Analysis of Immune Infiltration and Clinical Significance of Lysosome-Related Genes in Colon Adenocarcinoma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Data Collection

Consensus Clustering Analysis of LRGs

Clinical pathology characteristics and prognosis of COAD patients

Relationship between COAD and TME

Functional enrichment analysis and identification of DEGs

Establishment and prediction analysis of nomograph chart

Mutation and Drug Sensitivity Analysis

Statistical Analysis

Results

Genetic Mutation Landscape of LRGs in COAD

Generation of Lysosomes Subgroups in COAD

Features of the TME in Various Subgroups

Gene Subgroup Identification Using DEGs and Functional Analysis

Clinical Analysis of the Prognostic

Features of the TME in Various Risk Scores

Association of Risk scores with TMB

Drug Sensitivity Analysis

Discussion

Conclusion

Declarations

References

Supplementary Tables S1-S2

Additional Declarations

Status:

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