Prognostic relevance of MDK and TIMP1 with immune infiltration in lung adenocarcinoma

doi:10.21203/rs.3.rs-4975882/v1

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Prognostic relevance of MDK and TIMP1 with immune infiltration in lung adenocarcinoma

https://doi.org/10.21203/rs.3.rs-4975882/v1

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Background

LUAD is a prevalent and deadly lung cancer type. MDK and TIMP1 expression shows variations in different cancers. The specific contributions of these proteins to LUAD progression and tumor immunity, however, are not well delineated.

Methods

We leveraged RNA-seq data from TCGA and applied ggpubr R package to discern the expression disparity of MDK and TIMP1 in normal versus LUAD tissues. MDK and TIMP1 levels were further validated by qRT-PCR and western blot. Subsequently, LUAD patients were stratified into high and low expression groups based on MDK and TIMP1 expression, and the impact of their expression on overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS) was analyzed. Kaplan-Meier survival curves and receiver operation characteristic curves were plotted. We also explored KEGG and GO annotations for 50 genes exhibiting expression profiles akin to MDK and TIMP1, and constructed a gene-gene interaction network using GeneMANIA. The enrichment of DEGs in the KEGG and GO pathways was scrutinized in both high and low expression groups of MDK and TIMP1. Furthermore, we investigated the mutational landscape of MDK and TIMP1 within LUAD and assessed correlation between their expression and infiltration of immune cells.

Results

MDK and TIMP1 were found to be markedly overexpressed in LUAD. LUAD patients with diminished expression of MDK and TIMP1 have extended OS, DFI, DSS, and PFI. Area under the curve values for MDK and TIMP1 were 0.943 and 0.875, respectively. Regression analysis identified TIMP1 as a risk factor influencing the OS of LUAD patients. Genes with similar expression profiles to MDK were notably enriched in the Proteasome pathway and peptidase activator activity, while those exhibit similar expression patterns to TIMP1 were predominantly involved in endopeptidase activity and the Cytoskeleton in muscle cells pathway. Functional predictions for the genes MDK and TIMP1 showed a parallel, particularly in their regulation of peptidase activity. Mutations in MDK and TIMP1 are not determinants of survival in LUAD patients. There was a negative correlation between MDK and TIMP1 expression and tumor purity. The tumor immune dysfunction and exclusion score was elevated in the group with high TIMP1 expression. The IPS_ctla_pos and IPS_pd1_pos scores are statistically significant in the high TIMP1 expression group. Infiltration of immune cells and immune-related functions is more substantial in MDK low expression and TIMP1 high expression groups.

Conclusion

A strong correlation exists between MDK and TIMP1 with both the prognosis and progression of LUAD, and the extent of immune cell infiltration, indicating that targeting these genes and their related pathways in immunotherapy could be of clinical value.

immune infiltration

lung adenocarcinoma

MDK

prognostic markers

TIMP1

Lung cancer is recognized globally as a prevalent and formidable cancer with a high fatality rate¹. The American Cancer Society’s latest statistics for 2023 report 238,340 cases of lung cancer, which is 12.17% of all cancer cases². Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is responsible for about 40% of lung cancer cases³. Despite the efficacy of surgery, chemotherapy, radiation therapy, and targeted treatments for LUAD, the 5-year survival rate remains below 20%^{4, 5}. Hence, the search for viable prognostic markers or therapeutic targets is a key focus in the research of LUAD.

Midkine (MDK), part of the growth factor protein family, is commonly found in complex with heparin⁶. The human MDK gene is situated on chromosome 11 at the q11.2 locus and is composed of five exons and four introns⁷. MDK is known to encourage cell proliferation, migration, and angiogenesis, particularly during the onset of tumors⁶. MDK is highly expressed in various cancers including melanoma, hepatocellular carcinoma, and neuroblastoma^8–10, which signifies its potential as a target for cancer treatments.

Tissue inhibitor of metalloproteinase-1 (TIMP-1), a glycoprotein with 184 amino acids, is expressed in various tissues and belongs to the TIMP gene family^{11, 12}. The protein encoded by the TIMP1 gene inhibits a multitude of matrix metalloproteinases (MMPs), can stimulate cell proliferation in various cell types, and may have anti-apoptotic functions¹³. The TIMP1 gene has been a target of interest in cancer therapy, with its genetic variations linked to the development of several cancers, such as breast, prostate, and ovarian cancer^14–16. However, the impact of MDK and TIMP1 on LUAD remains largely unknown.

Through our research, facilitated by the Cancer Genome Atlas (TCGA) database, we revealed that MDK and TIMP1 possess significant prognostic relevance in LUAD and are associated with the infiltration of immune cells in LUAD patients. Our research also considered the predictive significance of genes related to MDK and TIMP1, introducing new insights and methods for the prognosis of LUAD patients.

2.1 Data acquisition

RNA-Seq expression data for LUAD patients, consisting of 59 normal and 541 cancer samples, were downloaded from the TCGA database, along with single nucleotide variant (SNV) data, phenotype, and survival data. From the UCSC Xena (https://xena.ucsc.edu/) platform, we obtained Disease-Free Interval (DFI), Progression-Free Interval (PFI), and Disease-Specific Survival (DSS) data, applying a filter for survival analysis to include only samples with a survival time exceeding 10 days.

2.2 Cell culture

For our research, we employed the human lung epithelial cell line BEAS-2B (BNCC359274) and LUAD cell lines A549 (BNCC337696), H1299 (BNCC100268), and Calu-1 (BNCC340251), all sourced from the BeNa Culture Collection (BNCC). The cells were grown in DMEM-H medium containing 10% FBS, which was kept in an incubator at 37°C with 5% CO₂. They were subcultured every 2–3 days, and only the fourth passage cells were utilized in our experiments.

2.3 qRT-PCR

Cells in the logarithmic growth phase were collected for RNA extraction using TRIzol (Invitrogen, USA) as per the manufacturer’s instructions. The PrimeScript RT reagent Kit (TaKaRa, USA) was utilized for the synthesis of complementary DNA (cDNA). qRT-PCR was carried out on the Applied Biosystems 7500 Real-Time PCR System (Applied Biosystems) with the SYBR Premix Ex Taq II kit (TaKaRa, Dalian, China), including three replicates for each sample. GAPDH served as the internal reference gene for MDK and TIMP1. The experiment was independently repeated three times, and the primer sequences are listed in Table 1. The 2^−ΔΔCt method was applied to calculate the relative expression levels of MDK and TIMP1.

Table 1

Primer sequences
Gene	Primer
GAPDH	F:5'-GACAGTCAGCCGCATCTTCT- 3'
	R:5'-GCGCCCAATACGACCAAATC- 3'
MDK	F:5'-GAGTCGCCTCTTAGCGGATG- 3'
	R:5'-CATTGTAGCGCGCCTTCTTC- 3'
TIMP1	F:5'-TTGGCTGTGAGGAATGCACA- 3'
	R:5'-GTCCACAAGCAATGAGTGCC- 3'

2.4 Western blot (WB)

Logarithmically growing cells were rinsed three times with precooled PBS, followed by the application of RIPA lysis buffer and incubation on ice for 30 min. After centrifugation, the BCA Protein Assay Kit from Thermo Fisher Scientific (USA) was used to quantify protein concentrations. Proteins were denatured at 95°C and separated via 10% SDS-PAGE. Once the electrophoresis was concluded, the proteins were transferred to a PVDF membrane and blocked with 5% skim milk for 2 h. The primary antibody was then added for overnight incubation at 4°C. The next day, the membrane was washed three times with TBST at room temperature for 15 min each, followed by incubation with the secondary antibody goat anti-rabbit IgG (ab6721, 1:3000, Abcam) at 37°C for 120 min. After another three washes with TBST, the protein bands were visualized through ECL chemiluminescence, and their relative expression levels were analyzed using the Image Pro Plus 6.0 software. The primary antibodies included: MDK (ab52637, 1:1000, Abcam), TIMP1 (ab211926, 1:1000, Abcam), glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (ab8245, 1:10000, Abcam).

2.5 Differential expression analysis

With the aid of the ggpubr R package, we graphically represented the disparity in expression levels of MDK and TIMP1 between LUAD and normal controls. Then we investigated how the mRNA expression of MDK and TIMP1 correlates with the clinical and pathological attributes of LUAD patients.

2.6 Survival analysis and prognostic evaluation

Utilizing TCGA-LUAD data, we determined the optimal cutoff points for MDK and TIMP1 expression and stratified LUAD patients into high and low expression cohorts. We then conducted a comparative analysis of their survival indicators, focusing on OS (overall survival), DFI (disease-free interval), PFI (progression-free interval), and DSS (disease-specific survival). For a clearer evaluation of how MDK and TIMP1 mRNA expressions affect LUAD patient outcomes, we created Kaplan-Meier survival plots to compare survival rates across varying expression levels. To determine the prognostic potential of MDK and TIMP1 in LUAD, we used the pROC package to generate receiver operation characteristic (ROC) curves and calculate area under the curves (AUCs). To further examine the risk factors affecting LUAD patient OS, we conducted both univariate and multivariate Cox regression analyses.

2.7 Enrichment and interaction analyses

We filtered out the top 50 genes with expression profiles resembling those of MDK and TIMP1 from the GEPIA (http://gepia.cancer-pku.cn/) database. Applying the clusterProfiler package, we performed the enrichment analysis based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to reveal the biological functions of the 50 genes that are similar to those of MDK and TIMP1. Moreover, we leveraged the ggplot2 package to graphically represent the outcomes of these analyses. To delve into the interplay between the MDK and TIMP1 genes, we used the GeneMANIA (http://genemania.org/) platform to forecast and map out the gene-gene interaction network. To appraise the pathways and molecular mechanisms of MDK and TIMP1 in LUAD, we undertook gene set enrichment analysis (GSEA) with R software. After stratifying the LUAD patients into high and low expression cohorts, we identified differentially expressed genes (DEGs) between the groups using the edgeR package. We then conducted KEGG enrichment analysis on these genes with the clusterProfiler package. Finally, we prioritized the pathways by normalized enrichment score (NES) and selected those with p.adjust values less than 0.05 and q.value values less than 0.05, visualizing the top 10 pathways with appropriate tools.

2.8 Mutation landscape of MDK and TIMP1

To delve deeper into how genetic changes in the MDK and TIMP1 genes relate to clinical traits, we utilized the cBioPortal (https://www.cbioportal.org/) database We started by analyzing the genetic variations of MDK and TIMP1 in LUAD and combined this analysis with clinical data. To further study the mutations in the groups with high and low expression levels of MDK and TIMP1, we used the maftools package. Patients were stratified based on the expression levels of MDK and TIMP1, with mutational profiles being charted and compared, highlighting mutation types, SNV categories, and mutation rates. This comprehensive analysis sheds light on the impact of MDK and TIMP1 expression levels on mutation patterns in LUAD, probably guiding future research endeavors.

2.9 Immune properties of MDK and TIMP1

To investigate the roles of MDK and TIMP1 in LUAD, we started by employing the TIMER (https://cistrome.shinyapps.io/timer/) tool to scrutinize the potential associations between the expression levels of MDK and TIMP1 and immune cell infiltration in LUAD. Subsequently, based on the Tumor-Immune System Interaction Database (TISIDB), we explored the immune subtypes of MDK and TIMP1 in LUAD. To evaluate the specific effects of MDK and TIMP1 expression on the tumor microenvironment (TME), we used the “estimate” method to ascertain the immune score, stromal score, and ESTIMATE score for each sample in both the high and low expression groups. The Wilcoxon rank-sum test was employed to compare scores between the risk groups, with violin plots illustrating these disparities. To further explore the influence of MDK and TIMP1 expression on the immune cell landscape and functionality, we conducted the single sample gene set enrichment analysis (ssGSEA). Predicting the impact of MDK and TIMP1 expression on immunotherapy outcomes was achieved through the tumor immune dysfunction and exclusion (TIDE) scoring system. By leveraging the Wilcoxon rank-sum test and corresponding violin plots, we contrasted the predicted responses to immunotherapy between the risk groups, yielding biomarker candidates for clinical immunotherapy. To conclude, we sourced the IPS scores from the Cancer Immunome Atlas (TCIA) database for each patient and scrutinized the differences in IPS scores between the risk groups.

2.10 Statistical analysis

For all statistical analysis, we relied on GraphPad Prism software, version 8.0. The data from our experiments are shown as mean ± standard deviation (SD). Student’s t-test was utilized for assessing the differences between two groups. A p-value below 0.05 was considered significant.

3.1 Differential expression analysis and clinicopathological correlation analysis

The comparative analysis of MDK and TIMP1 expression in LUAD samples against normal samples indicated that both genes are substantially overexpressed in LUAD patients (Fig. 1A and 1B). qRT-PCR and WB were applied to validate the expression levels of MDK and TIMP1 in lung epithelial and LUAD cells. Results demonstrated that, compared to the control group, the expression levels of MDK and TIMP1 in LUAD cells are significantly elevated (Fig. 1C and 1D).

The correlation analysis of MDK and TIMP1 expression levels with clinical pathology revealed that TIMP1 expression is linked to the survival and death of LUAD patients, with higher levels observed in deceased patients (Fig. 1E) (P = 0.016). The expression levels of MDK and TIMP1 are also related to age, gender, TNM staging, and tumor stage, but these relationships do not hold statistical significance (Figure S1).

The Kaplan-Meier curves showed that LUAD patients with lower expression of MDK and TIMP1 experienced prolonged OS, DFI, DSS, and PFI (Fig. 2). ROC analysis highlighted that MDK and TIMP1 had AUCs of 0.943 and 0.875, respectively, indicating their strong predictive potential for patient prognosis (Fig. 3A, 3B). Univariate Cox regression analysis pointed to the prognostic significance of TNM stage, tumor stage, and TIMP1 (P < 0.05) (Fig. 3C). Multivariate Cox regression analysis substantiated the significant prognostic value of TNM stage and TIMP1 (P < 0.05) (Fig. 3D), establishing TIMP1 as an independent prognostic factor in LUAD.

3.2 Enrichment and interaction analyses

The KEGG pathway analysis for the 50 genes with expression patterns similar to MDK from the GEPIA database revealed significant enrichment of these genes in the pathways of Various types of N-glycan biosynthesis, Proteasome, and N-Glycan biosynthesis (Fig. 4A); The GO analysis for these genes showed that these genes were mainly involved in peptidase activator activity and endopeptidase activator activity (Fig. 4B). For the 50 genes with similar expression to TIMP1, the GO analysis highlighted a concentration in cellular components such as the collagen-containing extracellular matrix and in molecular functions such as serine-type endopeptidase activity, serine-type peptidase activity, serine hydrolase activity, and endopeptidase activity (Fig. 4C); The KEGG analysis showed that these genes were enriched in pathways like Cytoskeleton in muscle cells and Protein digestion and absorption (Fig. 4D).

To predict the functions of MDK and TIMP1, we employed the GeneMANIA website to identify genes with analogous functions and build a protein-protein interaction (PPI) network involving MDK, TIMP1, and these genes, while also projecting their possible functions. The findings showed that these genes were predominantly associated with functions like regulation of metallopeptidase activity, negative regulation of peptidase activity, response to UV-A, extracellular matrix organization, regulation of peptidase activity, negative regulation of proteolysis, and regulation of membrane protein ectodomain proteolysis (Fig. 5).

Using the edgeR package, we carried out differential expression analysis on the high and low expression groups for MDK and TIMP1, followed by KEGG enrichment analysis for the upregulated and downregulated genes with the clusterProfiler package. DEGs identified as upregulated by MDK were predominantly enriched in pathways such as Ribosome, DNA replication, Proteasome, Spliceosome, and Carbon metabolism (Fig. 6A); whereas the downregulated DEGs were mainly enriched in pathways like Neuroactive ligand-receptor interaction, Alcoholism, Calcium signaling pathway, Olfactory transduction, and Steroid hormone biosynthesis (Fig. 6B). Under the influence of TIMP1, the upregulated DEGs were primarily enriched in pathways including Ribosome, Viral protein interaction with cytokine and cytokine receptor, Intestinal immune network for IgA production, Complement and coagulation cascades, and Rheumatoid arthritis (Fig. 6C); the downregulated DEGs were predominantly found in pathways such as Staphylococcus aureus infection, Viral carcinogenesis, Cortisol synthesis and secretion, Olfactory transduction, and Alcoholism (Fig. 6D).

3.3 Mutation profiles of MDK and TIMP1

Predictive modeling of MDK and TIMP1 mutations disclosed that MDK was mainly affected by Amplifications, Deep Deletions, and a limited number of Missense Mutations (Fig. 7A, 7C). The mutation profile of TIMP1 was characterized mainly by Amplification and Deep Deletion (Fig. 7B, 7D). Additionally, we predicted the mutation patterns for MDK and TIMP1 in their respective high and low expression groups. The data revealed that in the MDK high expression group, the principal gene mutation type was Missense Mutation, predominantly manifesting as SNPs, with C > A being the leading single nucleotide mutation. The genes with the highest mutation rates were TTN, RYR2, and CSMD3 (Fig. 8A). In the MDK low expression group, the main mutation types were Missense Mutations and SNPs, with C > A as the most recurrent single nucleotide mutation, and the top three mutated genes by rate are TTN, MUC16, and CSMD3 (Fig. 8B). For TIMP1, the mutation profiles for both high and low expression groups were analogous, with Missense Mutations as the main type, SNPs as the principal mutation category, and C > A as the predominant single nucleotide mutation. The genes with the top mutation rates were consistently TTN, MUC16, and CSMD3 (Figs. 8C and 8D).

Building on our predictions, we examined the impact of MDK and TIMP1 mutations on patient survival compared to non-mutation groups, and observed that the mutation groups had some influence on DSS, OS, PFI, and DFI, but these influences were not statistically significant as per P-values (Figure S2). This finding suggests that mutations in MDK and TIMP1 are not the primary influences on LUAD progression.

3.4 Immune attributes of MDK and TIMP1

We assessed the correlation of MDK and TIMP1 with immune cells via the TIMER website. Results indicated that MDK expression was negatively linked to tumor purity, exhibiting a negative correlation with the infiltration levels of CD8⁺T cells, macrophages, and neutrophils, and a positive correlation with dendritic cells, CD4⁺T cells, and B cells (Fig. 9A). TIMP1 expression correlated negatively with tumor purity and positive correlated with immune cell infiltration levels (Fig. 9B).

We also conducted an analysis of the influence of MDK and TIMP1 expression levels on immune subtypes, finding that these genes have distinct impacts in the C1-C6 immune subtypes (Fig. 10A, 10B). The TIDE scores, which measure immune evasion, were higher in the MDK high expression group, but the difference was not statistically significant (Fig. 10C). The high expression group of TIMP1 had a higher TIDE score, suggesting a lower likelihood for benefiting from immune intervention (P = 0.0047) (Fig. 10D).

Moving on, we analyzed the IPS data for MDK and TIMP1 expression groups. Results indicated that the IPS_ctla4_neg_IPS_pd1_neg group in the MDK high expression group was statistically significant in contrast with the low expression group (P < 0.05) (Fig. 11A). For TIMP1, the IPS_ctla_neg_IPS_pd1_pos and IPS_ctla_pos_IPS_pd1_pos groups in the high expression group showed statistically significant differences from the low expression group (P < 0.01) (Fig. 11B). Additionally, we applied the “estimate” scoring system to assess the stromal, immune, and ESTIMATE scores for the high and low expression groups of MDK and TIMP1. The MDK low expression group had higher stromal, immune, and ESTIMATE scores (P < 0.001, P < 0.05, P < 0.01 respectively) (Fig. 11C). The TIMP1 high expression group displayed higher stromal, immune, and ESTIMATE scores (P < 0.001) (Fig. 11D).

Using ssGSEA, we determined the levels of immune cell and immune function infiltration for the high and low expression groups of MDK and TIMP1. The low expression group of MDK demonstrated higher infiltration levels of aCDs, B_cells, DCs, Macrophages, Mast_cells, Neutrophils, T_helper_cells, TIL, and Treg cells (Fig. 12A). The MDK low expression group also displayed elevated infiltration of immune functions CCR and Type_II_IFN_Response (Fig. 12B). The TIMP1 high expression group had greater infiltration of B_cells, CD8+_T_cells, DCs, iDCs, Macrophages, pDCs, T_helper_cells, Tfh, Th1_cells, Th2_cells, TIL, and Treg cells (Fig. 12C). Additionally, this group had higher levels of immune functions including APC_co_inhibition, APC_co_stimulation, CCR, Check-point, Cytolytic_activity, HLA, Inflammation-promoting, Parainflammation, T_cell_co-inhibition, and T_cell_co-stimulation (Fig. 12D). Heat maps of immune cells and immune function pathways revealed higher infiltration in the MDK low expression group (Fig. 13A) and in the TIMP1 high expression group (Fig. 13B).

As a subtype of NSCLC, LUAD is marked by significant morbidity and mortality¹⁷. The high sensitivity of MDK has identified it as a biomarker for cancers like hepatocellular carcinoma¹⁸. Shin et al.¹⁹ found that MDK-targeted treatments can prevent the progression and spread of NXCLC in spontaneous metastatic models. The influence of TIMP1 has been noted in multiple cancers, including prostate, pancreatic, and gastric cancer^{15, 20, 21}. Furthermore, studies have associated the TIMP1 gene with an unfavorable prognosis in NSCLC²². Consequently, our study is committed to an exhaustive examination of the prognostic value and immune attributes of MDK and TIMP1 in LUAD patients.

With the aid of the TCGA database, we investigated the transcriptional activity of MDK and TIMP1 in LUAD and normal samples, observing pronounced overexpression of both in LUAD patients. Following this, we dissected the association between MDK, TIMP1, and clinical pathological parameters of LUAD, identifying higher TIMP1 levels in deceased patients. Incorporating survival analysis, we propose MDK and TIMP1 as prospective biomarkers in LUAD.

Genes that are functionally similar to MDK and TIMP1 were found enriched for the regulation of metallopeptidase activity, negative regulation of peptidase activity, and modulation of peptidase activity. These functions aid in strengthening the immune system and the body’s resistance to pathogens²³. Furthermore, inhibiting peptidase activity can slow tumor growth and suppress inflammatory responses²⁴. Zhang et al.²⁵ found that the inhibition of TIMP1 expression activates MMP9, thereby suppressing the invasion and migration of thyroid cancer. Moreover, MDK and TIMP1 are involved in the restructuring of the extracellular matrix, an element that changes in both density and composition during tumorigenesis. The rigidity and degradation of the matrix are altered in a way that can foster the growth of tumor cells²⁶. These genes also play a role in the negative regulation of proteolysis and modulation of ectodomain proteolysis of membrane proteins. Overactivity of hydrolytic enzymes in certain cancers might cause excessive protein breakdown and cellular malfunction. Thus, targeting the negative regulatory mechanisms of proteolysis may provide new ideas and methods for the treatment of these diseases²⁷. Collectively, our research indicates that MDK and TIMP1 may contribute to tumorigenesis and the tumor immune microenvironment through pathways such as peptidase activity and proteolysis.

TME refers to the microenvironment in which tumor cells exist. Immune cells and stromal cells, which constitute the non-neoplastic components, are vital in controlling the development of cancer and modulating the therapeutic outcomes²⁸. In our research, we examined the interplay between MDK and TIMP1 with respect to immune infiltration in LUAD. It was observed that the stroma, immune, and ESTIMATE scores were higher in the low MDK expression group and the high TIMP1 expression group. Moreover, we discovered that TIMP1 expression was negatively linked to tumor purity and positively to the infiltration levels of immune cells including CD8⁺T cells, macrophages, neutrophils, dendritic cells, CD4⁺T cells, and B cells, which bolster the anti-tumor response. MDK expression levels, were also negatively related to tumor purity but displayed a negative correlation with CD8⁺T cells, macrophages, and neutrophils infiltration, and a positive correlation with dendritic cells, CD4⁺T cells, and B cells. This could imply a more substantial role for dendritic cells, CD4⁺T cells, and B cells in the anti-tumor reaction. Dendritic cells (DCs) are infrequently found, migratory bone marrow-derived leukocytes that bridge the innate and adaptive immune systems²⁹. DCs are integral to the immune system and are gaining prominence in the fight against cancer due to their role in promoting T-cell immunity and their responsiveness to immunotherapy³⁰. The prospect of DC-based vaccines in breast cancer treatment is looking very promising³¹. Originating from bone marrow lymphoid progenitor cells, CD4⁺T cells, or Helper T cells, migrate to the thymus through the bloodstream and mature into T cells³². They aid CD8⁺T cells in the cellular immune response that eradicates tumor cells and cells invaded by viruses³³. CD4⁺T cells also secrete cytokines, including interleukin-2 and interleukin-10, which activate CD8⁺T cells to indirectly kill cells compromised by viral or bacterial infections³². B cells, integral to the humoral immune system, are a crucial component of immunity. They emerge from multipotent stem cells in the bone marrow and differentiate into immune cells with specific roles³⁴. B cells and plasma cells can combat tumors through antibody-dependent cellular cytotoxicity (ADCC) and by triggering the complement cascade³⁵. Our research revealed a positive correlation between MDK/TIMP1 expression and the infiltration of Dendritic Cells, CD4⁺T cells, and B cells, suggesting that increased expression of MDK and TIMP1 may impede the adverse progression of LUAD tumors. Checkpoint inhibitors that target PD-1, PD-L1, and CTLA-4 have secured a principal role in oncology for NSCLC patients. These inhibitors block specific immune checkpoints, effectively reactivating the patient's immune system to increase its assault on tumor cells and providing significant therapeutic benefits to many NSCLC patients³⁶. In our study, the high expression group of TIMP1 displayed statistical significance for IPS_ctla_neg and IPS_pd1_pos, and for IPS_ctla_pos and IPS_pd1_pos, indicating a response to both anti-CTLA-4 and anti-PD-1 antibodies in LUAD patients. Our research provides an in-depth analysis of the immunological profiles of MDK and TIMP1 in LUAD, indicating their potential as effective targets for LUAD immunotherapy. Further studies are needed to determine if MDK and TIMP1 can be key facilitators in immunotherapeutic strategies.

To encapsulate, our research, through the agency of bioinformatics, has discovered that MDK and TIMP1 genes could serve as prognostic indicators in LUAD, with notable disparities in immune infiltration and scoring between their expression groups. Nonetheless, our study has certain limitations. First, our conclusions are grounded in the analyses of data available from online databases. Moreover, there is a pressing need for extensive prospective studies and additional experimental research to delve into the mechanisms and confirm our findings. In summary, our investigation illuminates the connection between MDK and TIMP1 with LUAD, providing new reference points and a theoretical groundwork for upcoming LUAD research and clinical therapies.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors consent to submit the manuscript for publication.

Availability of data and materials

The data and materials in the current study are available from the corresponding author on reasonable request.

Competing interest

The authors declare no conflicts of interest.

Funding

The study was sponsored by Jinhua Science and Technology Plan Project (2023C22864).

Authors’ contributions

Conceptualization: Qinghua Zhu and Junkai Fu

Data curation: Qingqing Huang and Xiaohua He

Formal Analysis: Miaomiao Jiang and Chenyuan Ding

Writing: Qinghua Zhu and Junkai Fu

Acknowledgments

Not applicable.

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No competing interests reported.

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Prognostic relevance of MDK and TIMP1 with immune infiltration in lung adenocarcinoma

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1 Data acquisition

2.2 Cell culture

2.3 qRT-PCR

2.4 Western blot (WB)

2.5 Differential expression analysis

2.6 Survival analysis and prognostic evaluation

2.7 Enrichment and interaction analyses

2.8 Mutation landscape of MDK and TIMP1

2.9 Immune properties of MDK and TIMP1

2.10 Statistical analysis

3. Results

3.1 Differential expression analysis and clinicopathological correlation analysis

3.2 Enrichment and interaction analyses

3.3 Mutation profiles of MDK and TIMP1

3.4 Immune attributes of MDK and TIMP1

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

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