Elucidating the Correlation between Leupaxin as a Prognostic Biomarker and Immunotherapeutic Efficacy in Esophageal Squamous Cell Carcinoma

doi:10.21203/rs.3.rs-3767284/v1

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Elucidating the Correlation between Leupaxin as a Prognostic Biomarker and Immunotherapeutic Efficacy in Esophageal Squamous Cell Carcinoma

https://doi.org/10.21203/rs.3.rs-3767284/v1

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Background: Oesophageal cancer is a prevalent malignant tumour globally, yet it often receives subpar treatment. Recent interest in immunotherapy has instilled hope for cancer patients. However, further research is necessary to comprehend the mechanisms and roles of effective biomarker in oesophageal squamous carcinoma (ESCC).

Methods: We investigated the expression, pathological and prognostic significance, protein interactions, pathway enrichment, immune microenvironment, correlations between immune regulators and infiltration of immune cells, associations with drug resistance genes, as well as chemosensitivity of leupaxin (LPXN) in ESCC. The relative expression of LPXN mRNA and protein was evaluated in both healthy surrounding tissues and ESCC tissues using quantitative polymerase chain reaction and immunohistochemistry, utilizing samples obtained from clinical cases. Furthermore, we investigated the potential role of LPXN in ESCC through a variety of techniques including cell proliferation assays, apoptosis assays, clonogenic assays, migration assays with or without extracellular matrix support. The co-expression of LPXN and PD-L1 at the protein level in oesophageal squamous cell lines was determined by western blotting. We validated the expression of the LPXN gene in ESCC using clinical samples and investigated the correlation between LPXN gene expression and the efficacy of immune therapy for ESCC.

Results:Bioinformatic analysis revealed a favourable association between LPXN and prognosis in ESCC. LPXN and PD-L1 were found to be co-expressed, with LPXN strongly associated with the immune system, related pathways, microenvironment, regulators, cell infiltration levels, genes related to drug resistance, and chemosensitivity. The expression of LPXN in ESCC at both mRNA and protein levels was confirmed in clinical samples of cancerous and normal tissues. Cell-based experiments demonstrated that knockdown of LPXN resulted in decreased cell viability, invasion, proliferation, and migration in ESCC cell lines. In samples that responded well to therapy, LPXN and PD-L1 were overexpressed at the mRNA and protein levels.

Conclusion: The results of our study have revealed the significance and involvement of the immune-related biomarker LPXN in the proliferation and migration processes of ESCC, thereby establishing a novel framework for the treatment of this disease.

leupaxin

immunotherapy response

prognostic biomarker

esophageal squamous cell carcinoma

bioinformatics

Esophageal cancer (EC) is a common malignancy, with over 570,000 new cases reported annually worldwide.^1,2 The dominant histological subtype of EC is esophageal squamous cell carcinoma (ESCC), particularly in southern Africa and southern Asia, accounting for 90% of ECs in China. The disease’s poor prognosis stems from intrusive tumor growth, substantial tumor heterogeneity, and complicated oncogenic pathways.³ Immune checkpoint inhibitors (ICIs), including cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1) inhibitors, have led to substantial developments in the immunotherapy of various cancers.^4,5 However, antigen resistance hinders long-lasting responses in many cancer patients.⁶ The identification of novel biomarkers or immunoregulatory genes could lead to a more personalized and durable immunotherapeutic approach for cancer patients.

Leupaxin (LPXN) belongs to the paxillin protein family and contains LIM domains and leucine-aspartic acid (LD) motifs in its protein–protein interaction domain structure.⁷ The LPXN gene on chromosome 11q12.1, which encodes 386 amino acids, was initially identified as a multifunctional adaptor protein prominently expressed in hematopoietic cells.⁸ LPXN is a crucial regulator of osteoclast adhesion zone growth; blocking the interaction of LPXN with the podosomal signaling complex slows migration and bone resorption.⁹ LPXN is prevalent in vascular smooth muscles, promoting the transcription of genes that encode smooth muscle markers.¹⁰ It is also the key regulator of human malignancy. LXPN expression controls cancer cell adherence and invasion in prostate cancer (PCa).¹¹ p120catenin—associated with increased PCa cell motility and invasiveness—is mediated by LPXN.¹² Increased LPXN expression has been observed in mammary carcinoma, suggesting that LPXN increases the transcriptional activity of estrogen receptors, hastening breast cancer cell proliferation and invasion.¹³ By increasing the response to granulocyte colony-stimulating factor and the chemokine CXCL12 in acute myeloid leukemia, LPXN and its fusion partner, ETS variant transcription factor 6, encourage cell proliferation and migration.¹⁴ However, the role of LPXN in ESCC progression and prognosis remains unknown.

Here, we employed Gene Expression Profiling Interactive Analysis (GEPIA) and UALCAN Omnibus to compare the expression levels of LPXN in EC/ESCC with those in normal human tissues. Using quantitative polymerase chain reaction (q-PCR) and immunohistochemistry (IHC), we verified the relative expression levels of LPXN mRNA and protein in surrounding healthy and ESCC tissues obtained from clinical cases. We investigated the relationships between LPXN expression, pathological staging, and overall survival (OS) in patients with ESCC. Furthermore, we investigated the potential role of LPXN in ESCC through western blotting and cell proliferation, apoptosis, clonogenic, and migration assays with or without extracellular matrix support. Furthermore, we aimed to elucidate the molecular regulatory network of LPXN in relation to tumor stemness, drug sensitivity, and immune infiltration.

Gene Expression Profiling Interactive Analysis

We compared LPXN expression between esophageal malignancies and adjacent normal tissues using GEPIA. The transcriptional data of 9,736 tumors and 8,587 normal samples from the Cancer Genome Atlas (TCGA)¹⁵ and the Genotype–Tissue Expression project were evaluated using the GEPIA web-based tool (http://gepia.cancer-pku.cn/index.html). Tumor and normal differential expression analysis, profiling by cancer type or pathological stage, patient survival analysis, dimensionality reduction analysis, comparable gene detection, and correlation analysis are features that can be modified to meet user requirements. The fold-change cutoff was 0.5, and the p value cutoff was 0.05.

UALCAN Omnibus

Using clinical data and RNA sequencing (seq-RNA) from 31 different cancer types from TCGA, UALCAN was identified as an interactive and user-friendly web resource. We used UALCAN to evaluate changes in LPXN expression during the disease and to confirm that its expression in ESCC differs from that in healthy tissues.

GSE53625 from the GEO database

GSE53625, downloaded from GEO, comprises the clinical data and RNA sequencing results of 179 cases of ESCC. The samples submitted by the published literature and used in the GSE53625 dataset were obtained from Chinese patients.¹⁷

STRING database

Known and expected protein–protein interactions (PPIs) were obtained from the STRING database (https://string-db.org/).¹⁸ We analyzed the PPI networks of the co-expressed genes in LPXN to identify the functions of the individual genes in this network. With Homo sapiens as the selected species, statistical significance was determined as total score > 0.4. In this database, nodes represent proteins, and edges represent the relationships between them.

GeneMANIA and TCGA databases

The GeneMANIA database (http://genemania.org/), used to examine the links between gene sets,¹⁹ was utilized to construct the LPXN gene interaction network.

RNA was sequenced from 80 patients with ESCC based on information retrieved from TCGA website (http://tcga-data.nci.nih.gov/tcga).²⁰ TCGA contains information on the genomic, epigenomic, transcriptomic, and proteomic characteristics of 33 different cancer types and their matched normal samples. Correlations were tested using Spearman’s correlation; statistically significant differences were defined as p < 0.05 and coefficient > 0.7,. Based on the genes whose expression levels are strongly correlated with LPXN expression, GO and KEGG analyses were conducted using clusterProfiler and KEGG.db, respectively.

The stromal and immunological scores in each ESCC sample in TCGA database were determined by enrichment analysis using a gene set of a single sample (ssGSEA) and the GSVA R package. Stromal cells and immune infiltration in distinct tumor tissues were evaluated using these scores.²¹ We investigated the relationship between LPXN expression and ratings using the Spearman correlation test. Using features gleaned from the transcriptome and epigenetics of tumor samples from TCGA, stem cell-like properties of tumors can be quantified.²² The link between the infiltrating immune subtype and LPXN expression was investigated using one-way analysis of variance (ANOVA). Using Spearman’s correlation, the relationships between LPXN and PD-L1/PD1, MRP, and 47 other widely known immune checkpoint genes were assessed. Statistical significance was set at p < 0.05.

TIMER interface

The infiltration of many immune cells and their clinical effects were considered²³ using the intuitive TIMER web interface (https://cistrome.shinyapps.io/timer/). B-cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells are among the six key analytical modules that communicate through this interface. The relationship between immune infiltration and integrin α (ITGA) gene expression depth in ESCC was visualized using scatterplots, with the ITGAs serving as inputs for the ‘Gene’ module.

Chemotherapy sensitivity analysis

CellMiner was used to gain entry into NCI-60 (https://discover.nci.nih.gov/cellminer), using nine different tumor types comprising 60 unique cancer cell lines. The link between drug sensitivity and LPXN expression was analyzed using Pearson’s test. We compared 263 medicines that received FDA clearance or had undergone clinical studies for efficacy (Supplementary Table 1).

Quantitative polymerase chain reaction

Twenty samples were obtained from the Chongqing University Cancer Hospital, including 10 pairs of tumor and healthy esophageal tissues, 14 immune-responsive ESCC tissues, and 8 immune-resistant ESCC tissues. Twenty-two patients with ESCC who had been treated with adjuvant immune therapy were included. Nanoparticle albumin-bound paclitaxel, cisplatin, and pembrolizumab were used in a four-cycle therapy plan for patients with ESCC in compliance with the 2021 Chinese Society for Clinical Oncology guidelines. Patients’ responses to immune treatment were measured based on the iRECIST criteria.²⁴ Chongqing University Cancer Hospital ethics board approved this study. We analyzed LPXN expression in ESCC and normal esophageal tissue, as well as immune-resistant ESCC tissues and LPXN expression using q-PCR. RNA from each sample was acquired independently using TRIzol (Servicebio). Using a Thermo Revert Aid First Strand cDNA Synthesis Kit and reverse transcription, RNA was converted to cDNA. Step One Plus (Applied Biosystems) q-PCR analysis was performed using the Fast Start Universal SYBR Green Master Mix (Roche). The 2^−ΔΔCT technique was used for data analysis, and GAPDH was used as the endogenous control. The primer sequences for LPXN and ATCB are as follows: LPXN forward, 5′-CCACCACCTTCTAAAACGTCAG-3′, reverse, 5′-CCCAAGCATTGAGTCCAG-GG-3′; ATCB forward 5′-GCGTGACATTAAGGAGAAGC-3′, reverse, 5′-CCACG-TCACACTTCATGATGG-3′.

Immunohistochemistry staining, assessment, and scoring

Forty-two clinical samples were obtained from Chongqing University Cancer Hospital, including 10 pairs of ESCC tissues and adjacent non-cancerous tissues, and 14 immune-responsive ESCC tissues and 8 immune-resistant ESCC tissues. The acquisition of these samples was approved by the Institutional Review Board of the hospital.

All specimens were immersed in paraffin, fixed in 10% formalin at 25 ℃, and sectioned at 4 mm thickness for IHC. Antigen recovery was performed by boiling the dewaxed hydrated tissue slices in 10 mmol/L citrate solution (pH 6.4) for 10 min. Using methanol, hydrogen peroxide 3% was used to inactivate endogenous peroxidase, and citric acid buffer (pH 6.0) was used to treat the slices to recover as much antigen as possible. Bovine serum albumin (1% concentration) was treated with a phosphate buffer for 30 min to reduce non-specific binding. The slices were treated with primary antibody for 12 h at 4 ℃. The sections were then rinsed thrice in saline phosphate buffer for 5 min each. The slices were then incubated for 50 min with a 1:200 dilution of secondary antibody (horseradish peroxidase polymer). The samples were sealed and examined under a light microscope. The primary antibodies used were anti-LPXN (PA597933, Thermofisher) and anti-PD-L1 (PA528115, Thermofisher). Supplementary Table 2 lists the principal antibodies.

To evaluate the IHC results, we followed an established scoring method²⁵. PFN2 expression was assessed using a semi-quantitative approach based on the percentage of positive cells and the intensity of cytoplasmic/nuclear staining. The percentage of positive cells was categorized as follows: 0 (< 5% positive cells), 1 (6–25% positive cells), 2 (26–50% positive cells), 3 (51–75% positive cells), or 4 (> 75% positive cells). Cytoplasmic/nuclear staining intensity was classified as 0 (negative), 1 (light yellow), 2 (yellow), or 3 (brown). The product of the percentage of positive cells and staining intensity yielded the immunohistochemistry score (IS) for each patient. Two independent pathologists assessed all immunostaining results and arrived at a consistent score. Disagreement between the two pathologists regarding IHC results was resolved by consulting a third pathologist. Therefore, the IS ranged from 0 to 12, reflecting the scoring system used.

Small hairpin RNAs (shRNAs) and lentiviruses

The shRNA polynucleotides shLPXN-1 (CCAACGACTACCACCAACTTT) and shLPXN-2 (GCACTTCTTCTGCTCTCACTG) targeting human LPXN were synthesized, annealed, and cloned into the lentiviral vector GV493 carrying a green fluorescent protein expression cassette (GeneChem Co, Ltd., Shanghai, China). An empty GV493 vector served as the control shRNA. The models were tested using these sequences.

Western blotting

We conducted western blotting using cell lysates obtained from KYSE-150 human ESCC cell lines and ESCC tissues. An equal amount of protein lysate was loaded and separated by 10% SDS-polyacrylamide gel electrophoresis and transferred onto a polyvinylidene fluoride membrane (Millipore, USA). The membranes were blocked with 5% fat-free milk in TBS buffer and incubated overnight at 4 ℃ with anti-LPXN (PA597933, Thermofisher) and anti-PD-L1 (PA528115, Thermofisher). Bound antibodies were detected using a secondary antibody (horseradish peroxidase polymer) at room temperature for 2 h. After washing, the resulting bands were visualized using the standard Electron Cryo-Microscopy (ECL) procedure (Kangwei, Beijing). The acquired images were analyzed for grayscale values using the image acquisition system (Bio-Rad, USA) and ImageJ (National Institutes of Health, USA).

Cell culture and transfection

KYSE-150 human ESCC cell line cultures obtained from GeneChem Co., Ltd. (Shanghai, China) were maintained in 6 cm plates with 3 mL complete media in a 37 ℃, 5% CO₂ incubator. Tests were conducted using logarithmically growing cells. shRNAs (PSC107025 and PSC110829) and negative control sequence viruses were transfected into KYSE-150 cells cultured in 6-well plates or 60 mm² dishes using the same method as described for lentivirus generation. The efficacy of the infection was assessed 72 h after transfection using a fluorescence microscope (IX71; Olympus, Japan). Blotting was performed using RT-PCR to analyze the mRNA and protein expression of LPXN in cells infected with shLPXN-1, shLPXN-2, or shCtrl to assess the effectiveness of knockdown.

Celigo image cytometry assay

Logarithmic-phase experimental cells were dissociated with trypsin and collected via centrifugation. In 96-well plates, the cells were resuspended, quantified, and seeded at a density of 2 × 10³ cells/well in 100 µL growth medium. Seeding was performed to obtain the same number of cells from each well. The cells were cultured at 37 ℃ and 5% CO₂. The fluorescence intensity of the cells was measured daily for five consecutive days using the Celigo Imaging Cytometer System (Nexcelom Bioscience, St. Lawrence, MA, USA), and the cell number was automatically calculated. A proliferation curve was created using the number of green fluorescent cells.

Flow cytometry analysis of apoptosis and cell cycle

A single-cell suspension of trypsin-digested logarithmic-phase cells that had undergone two cold PBS washes was administered to each test subject group. Ten microliters of Annexin V-APC (eBioscience, San Diego, CA, USA) was used to stain the cell suspensions at room temperature for 10–15 min in the dark. A flow cytometer (BD Biosciences, San Jose, CA, USA) was used to calculate the percentage of dead cells.

Colony-forming assay

Each cell was plated at a density of 500 in well plates for 14 days in full medium and observed every 3 days. After incubation in 4% paraformaldehyde for 30–60 min, the wells were washed with PBS to remove any remaining fixative. Filtered crystal violet staining solution (1000 µL) was used to stain the cells for 10–20 min. After multiple ddH₂O washes and drying at room temperature, visible colonies were counted using the Image-Pro Plus software (Version 6.0.0; Media Cybernetics Inc.).

3-[4,5-Dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assay

Trypsin was used to loosen the developing cells in the logarithmic phase, which were then separated by centrifugation. Cells at a density of 2 × 10³ cells/well were resuspended and seeded in a 96-well plate. After 2 days, cells in each group were treated with sterile MTT (5 mg/mL) at 37 ℃ for 4 h on days 3, 4, and 5. Dimethyl sulfoxide was added after the culture medium was discarded, and the mixture was stirred for 2–5 min in a vortex mixer. An enzyme-linked immunosorbent assay reader (M2009PR; Tecan Infinite, Switzerland) was used to measure absorbance at 490 nm. All experiments were performed at least in triplicate.

Celigo scratch assay

After resuspending the cells in a complete medium, the logarithmic phase experimental cells were counted after digestion with trypsin. The cells were seeded at a density of 5 × 10³ cells/well and cultivated in 100 µL medium per well, three replicates per group, 37 ℃, 5% CO₂ culture condition. The following day, a scratch tool was used to form a small indentation at the top-center of each well. At 0 h, the plates were scanned after being gently rinsed twice or thrice with PBS, followed by adding 1% FBS serum-containing medium. The plates were incubated at 37 ℃ for 24 h at 5% CO₂ and scanned with Celigo to detect the extent of migration.

Cell invasion and migration assays

The experimental cells in every group were dislodged with trypsin, centrifuged for collection, reconstituted in a serum-free medium, and counted. In the top chamber, cells were injected into a serum-free medium with or without a Matrigel matrix membrane. Cells (5 × 10⁵) were seeded into each well, and 750 µL of 30% FBS culture medium was added to the lower chamber. The steps for developing the cells followed a prescribed pattern. The non-invasive cells were removed from the inner wall of the incubator at 37 ℃ with a moist brush. The outer cell walls were gently washed with PBS, stained with crystal violet, and fixed in 4% paraformaldehyde. The number of cells in each of the nine distinct minute visual areas was measured under a microscope. Each experimental group was tested in triplicate.

Statistical analysis

The Mann–Whitney U test was used to compare LPXN immunological pathways; p-values were adjusted using the Bonferroni technique. The variation in OS across groups was investigated using Kaplan–Meier plots. Using Spearman’s or Pearson’s correlation analyses, we investigated the relationships between LPXN expression and stemness, stromal score, drug resistance genes, regulators of immunity, and drug sensitivity. We compared the experimental groups using one-way ANOVA and Bonferroni post-hoc tests. Charts were created using the R programming language (version 4.2.1) and the ggplot2, pheatmap, ggpubr, corrplot, and Survminer applications. A two-tailed p < 0.05 was utilized to indicate significance.

Characterization of differentially expressed LPXN in patients with EC/ESCC

We compared the expression levels of LPXN in EC/ESCC with those in normal tissues using the GEPIA and UALCAN databases. mRNA levels of LPXN in EC were elevated (p < 0.001, Fig. 1A), and LPXN was overexpressed in ESCC tissues compared to that in healthy tissues (Fig. 1B).

Verification of LPXN expression in ESCC tissues and adjacent non-tumorous tissues

Supplementary Table 3 presents clinical information regarding the analysis of the tissue samples. q-PCR revealed that LPXN was significantly overexpressed in ESCC tissues compared to neighboring non-tumorous tissues (Fig. 1C, p < 0.001). IHC staining results matched with q-PCR findings (Fig. 1D, E, p < 0.001). The validation outcomes and LPXN expression were consistent between the GEPIA and UALCAN RNA sequencing datasets.

Association of LPXN with tumor pathological stage and patient survival in EC or ESCC

To ascertain the predictive utility of LPXN in EC/ESCC, the expression of differentially expressed LPXN was independently linked to the clinical stage and OS of patients with EC and ESCC. The UALCAN dataset revealed that, while stages IV and I in EC did not significantly differ from each other, stages II and III varied in terms of LPXN expression (Fig. 2A). The “survival” tool in R was utilized to analyze the ESCC dataset GSE53625, revealing a significant association between higher LPXN expression and worse overall survival (hazard ratio = 0.78, p = 0.015) through Kaplan–Meier survival analysis (Fig. 2B). However, the largest available dataset for ESCC, GSE53625, lacks clinical data on disease-free survival (DFS).

LPXN knockout inhibits ESCC cell proliferation

Knockdown cell lines were generated to substantiate the prognostic significance of LPXN in the development and progression of ESCC; q-PCR and western blotting revealed that the expression of LPXN in KYSE-150 cells was lower than that in empty vector-transfected cells (Fig. 3A, B). LPXN knckdown significantly decreased the number of KYSE-150 cells and increased apoptosis (Fig. 3D, H), as demonstrated by the Celigo assay, flow cytometry, MTT assay, and cell cloning (Fig. 3C, E, F).

ESCC cells exhibit compromised penetration and migration abilities in the absence of LPXN

Transwell assays for migratory and invasive LPXN cells revealed that experimental groups 1 and 2 contained fewer cells than the control group did (Fig. 3G, H). The Celigo Scratch assay investigated the impact of LPXN knockdown on cell migration, revealing a significant decrease in migration speed compared to that of the control group (Fig. 3G, H).

PPI network construction

The PPI network contained a sizable number of nodes (21) and edges (57) (Fig. 4A). GeneMANIA revealed relationships between LPXN and other proteins, including ITGA4, LIMS1, VCL, PTK2, INPP5D, SRF, LYN, IMP3, and PLSCR4 (Fig. 4B). These relationships include cell-matrix adhesion, cell-substrate adhesion, response to transforming growth factor beta, cellular response to growth hormone stimuli, and cellular response to growth hormones.

Functional enrichment analysis of LPXN and its co-expressing genes in ESCC from TCGA dataset

We utilized the TCGA dataset (Spearman's correlation coefficient > 0.7 or 0.7, p 0.05) to investigate the co-expression of LPXN in ESCC. Eighty-eight co-expressed genes were included in the GO analysis of LPXN. LPXN was found to regulate immune receptor molecular function, the cellular compartment of the immunological synapse, and the biological process of T cell activation (Fig. 4C). The key immune-related pathways in the KEGG study were Th1 and Th2 cell differentiation, the T-cell receptor signaling pathway, and the expression of PD-L1 and PD-1 checkpoint pathways in cancer (Fig. 4D).

Immune status and tumor microenvironment analysis of LPXN in the ESCC from TCGA dataset

In human cancer, immune infiltrates vary from tumor-promoting to tumor-suppressing²⁶ and are classified as wound healing (C1), INF-g dominant (C2), inflammatory (C3), lymphocyte depleted (C4), immunologically quiet (C5), or TGF-b dominant (C6).²⁷ The immunological subtypes C3, C4, C5, and C6 were not included in this study because none of the patients in the sample had these subtypes. Figure 6A demonstrates that high levels of LPXN expression were associated with C2 (p = 0.002), whereas low levels of LPXN expression were associated with C1 (p = 0.002).

mRNA expression level was used to calculate the RNA stemness score (RNAss), whereas the DNA methylation pattern was used to calculate the DNA stemness score (DNAss).²⁸ The tumor immune microenvironment was determined using stromal and immunological scores. Tumor stem cells and their immunological microenvironments have been studied in relation to LPXN expression. Figure 5 demonstrates a correlation between LPXN expression and stromal score (p = 5.6 *10^− 6) and a correlation between LPXN expression and immunological score (p = 2.2 *10^− 16). Conversely, LPXN expression was negatively associated with DNAss (p = 0.031). However, there was no statistically significant correlation observed between LPXN expression and RNA levels (p = 0.057).

Relationships between LPXN and PD-1/PD-L1, drug resistance genes, and immune regulators

The PD-1/PD-L1 pathway is essential to block the ability of cancer cells to evade the immune system and spread. Personalized immunotherapy requires the measurement of immunological checkpoints, such as PD-1 and PD-L1. We found that PD-1 and PD-L1 expression levels were highly correlated with LPXN expression values obtained from TCGA database (Fig. 6C, D; p = 1.5*10 − ¹² and p = 0.021, respectively). Among the tumor drug resistance genes, MRP1 expression had a substantial negative correlation (p = 0.031) with LPXN expression (Fig. 6B). Figure 6E depicts the connections between LPXN and the 47 immune regulators in ESCC. Most of these regulators interacted positively and strongly with LPXN. Thus, LPXN can be used to predict whether ICIs are effective.

Immune cell infiltration of LPXN in patients with EC

Using the TIMER database, we observed a positive correlation between the expression of LPXN and the infiltration of various immune cell types in EC patients, except for CD8 + T cells (Fig. 7).

Verification of the association between LPXN and PD-L1 protein expression in ESCC cells

Compared to the cells transfected with the empty vector, the protein expression levels were reduced in the LPXN knockdown cell lines (Fig. 3B). The expression levels of PD-L1 were also decreased (Fig. 8D). These findings suggest a positive correlation between LPXN and PD-L1 expression, which aligns with the results obtained from TCGA dataset.

Verification of the LPXN immunotherapy prediction in ESCC

The clinical information for ESCC samples with various levels of immunotherapy response is reported in Supplementary Table 3, and these data were provided by Chongqing University Cancer Hospital. Contrary to the samples in the control group, immunotherapy-responsive ESCC samples overexpressed LPXN and PD-L1 at both the mRNA and protein levels (Fig. 8A–C). This demonstrates that LPXN and PD-L1 are co-expressed in ESCC and are positively associated with immunotherapeutic efficacy, allowing for the indirect prediction of immunotherapy efficacy in ESCC and the inference of PD-L1 expression from LPXN expression.

LPXN expression and cancer cell sensitivity to chemotherapy

In the NCI-60 cell line, we investigated the association between LPXN expression and drug sensitivity (Supplementary Fig. 1); LPXN is related to sensitivity to chemotherapeutic treatment (p < 0.05). Increased resistance to several targeted medicines, including irofulven, dacomitinib, ibrutinib, afatinib, and sonidegib, were observed in cancer cells with high LPXN expression levels. However, some chemotherapeutic drugs, such as vemurafenib, dabrafenib, encorafenib, calusterone, dacarbazine, selumetinib, LEE011, carmustine, panobinostat, and ABT199, are more effective against cancer cells with increased LPXN expression.

Traditional treatments for EC, such as surgery, radiation therapy, and chemotherapy, have a significant impact on patients’ quality of life and are associated with low effectiveness rates,²⁹ warranting, new effective therapeutic approaches. Immune checkpoint inhibition and CAR T-cell therapy have improved cancer immunotherapy,³⁰ providing patients with hope for a possible cure. Owing to the heterogeneity of each patient’s tumor-suppressing microenvironment, immunotherapy is only effective against a subset of tumors, and only a small percentage of patients with cancer successfully respond to immunotherapy.³¹ However, the use of biomarkers to predict immunotherapy responses accurately can enable personalization of immunotherapy for each patient.³² To this end, LPXN may be a dependent indicator of immunotherapy efficacy and a potent prognostic biomarker of ESCC.

We evaluated LPXN expression in EC/ESCC using data from GEPIA and UALCAN and found that ESCC dramatically increased LPXN expression. In vitro molecular studies were performed to confirm the results because of uncertainty regarding the accuracy of the bioinformatics analyses. q-PCR and IHC results were supported by the gene expression profiles of LPXN in EC/ESCC from the GEPIA and UALCAN databases. In patients with EC/ESCC, high levels of LPXN gene expression were linked to poor prognosis, indicating that LPXN may be a risk factor for this condition. The relationship between OS and pathological staging supports this conclusion. Comprehensive cell tests were performed to confirm this finding, wherein LPXN knockdown resulted in fewer active ESCC cells, increased apoptosis, and impaired migration and invasion capacities compared with cells transfected with an empty vector. Overall, our results show that LPXN, which functions as both an oncogene and a potential prognostic biomarker, has a critical role in ESCC initiation, development, and progression. However, information on whether LPXN is connected to ESCC is limited. In determining the prognostic and indicative value of LPXN in ESCC, our study is the first to combine bioinformatics analysis, RNA sequencing, cell experiments, and IHC. The findings of this study establish a connection between LPXN gene expression levels, prognostic indicators, and the effectiveness of immunotherapy.

LPXN has been a potential biomarker for various cancers.^{12,14,33–36}. Some of these studies have focused on PCa. LPXN functions in PCa invasion and development by interacting with the androgen receptor (AR). Khan et al.³⁷ studied the AR-LPXN coupling pathway in PCa. Using artificial intelligence to analyze gene expression data, Carreras et al.³⁸ suggested that LPXN could prevent the development of diffuse large B-cell lymphoma. Conversely, inhibiting LPXN expression blocked MMP-2 and MMP-9 release, activated the cJun NH2-terminal kinase pathway (JNK) and mitogen-activated protein kinase signaling pathway (p38 MAPK), and halted the malignant growth and transmembrane invasion of human acute monocytic leukemia SHI-1 cells. LPXN overexpression may be involved in the proliferation, adhesion, and invasion of human acute monocytic leukemia THP-1 cells.³⁹ New insights into potential therapeutic targets for bladder cancer were provided by Hou et al.,⁴⁰ who discovered that LPXN may enhance bladder cancer growth by upregulating the expression of S100P via phosphatidylinositol 3kinase/protein kinase B signaling. The results for cervical, ovarian, uterine, endometrial, bone, kidney, and lung cancers were all comparable.^41–45 These results suggest that LPXN functions in oncogenesis.

Several clinical trials, including CheckMate-577,⁴⁶ CheckMate-648,⁴⁷ ESOPEC,⁴⁸ TOPGEAR⁴⁹, Keynote-181,⁵⁰ Keynote-590,⁵¹ RATIONALE-306,⁵² ASTRUM-007,⁵³ and JUPITER-06,⁵⁴ have demonstrated that efficient targeted immunotherapies may significantly improve EC patient survival outcomes. To ascertain the likely mechanism by which LPXN affects survival outcomes and build a framework for future molecular investigations, GO and KEGG enrichment analyses were performed.

Cancer cells are now understood to be embedded in connected stromal cells, which involve vascular cells, fibroblasts, and inflammatory immune cells forming the tumor microenvironment.⁵⁵ This definition is a considerable shift from the “cancer cell-centered” perspective that previously dominated the field. A link between LPXN, stromal score, and immunological score was identified using the ESTIMATE method. LPXN is probably secreted by stromal cells or participates in stromal processes because of its association with the stromal score. The strong connection between LPXN and the immunological score suggests that tumor tissues with high LPXN expression may be invaded by the immune system.

Cancer stem cell-like cells⁵⁶ originate from progenitor cells, non-stem cell dedifferentiation, and long-lived stem cells. They are responsible for treatment-induced drug resistance⁵⁷ owing to their propensity to self-renew and infiltrate, thereby promoting tumor development. DNAss exhibited a strong correlation between LPXN expression and tumor stemness, suggesting that LPXN has a role in tumorigenesis.

Increased PD-1 and PD-L1 expression is useful in treating ESCC⁵⁷ by reducing the antitumor immune response of T cells. Here, the expression of LPXN was positively correlated with that of PD-1 and PD-L1. Moreover, western blotting determined a positive correlation between LPXN and PD-L1 protein expression in ESCC cell lines.

To directly validate the link between LPXN and the success of immunotherapy in ESCC, we collected tumor samples from 22 patients who underwent immunotherapy at the Chongqing University Cancer Hospital. Based on various treatment responses, q-PCR and IHC staining revealed that LPXN was expressed in tumor samples from the treatment response group compared with those from the poor response group. Thus, LPXN expression can predict the levels of immunological checkpoint expression and influence therapeutic choices.

Multiple types of immune cells, such as macrophages, CD8⁺ T cells, and Th17 cells, are linked to cancer outcomes.^58–60 Our analysis of data from the TIMER database demonstrated a connection between LPXN expression and B cell, CD4 + T cell, dendritic cell, macrophage, and neutrophil counts, suggesting the importance of LPXN in the tumor immune microenvironment. Many cancer types have experienced a dramatic shift in the clinical therapy landscape as the use of ICIs has become increasingly important in poor prognosis pathways.^61,62 Therefore, we examined the relationship between LPXN and ICI gene expression and discovered that LPXN was associated with several ICI genes.

Based on the information on NCI-60 cell lines, elevated LPXN expression was associated with resistance to therapy with many chemotherapeutic agents with FDA approval. LPXN was linked to increased drug sensitivity to some medications, including vemurafenib, dabrafenib, encorafenib, calusterone, dacarbazine, selumetinib, LEE-011, carmustine, panobinostat, and ABT-199.

Among 13 members of the multidrug resistance protein (MRP) family, MRP1–MRP9 are the primary transporters contributing to multidrug resistance by obstructing chemotherapy uptake by cancer cells.⁵⁶ Because of its relationship with MRP1, LPXN may serve as a therapeutic target for drug resistance and enhance drug sensitivity.

Our investigation remains subject to certain limitations. As a preliminary exploration of the correlation between LPXN and ESCC, this study suffers from a scarcity of related reference publications. Moreover, China presents a high prevalence of ESCC, principally ESCC, while Western countries observe a higher incidence of esophagogastric junction adenocarcinoma. Consequently, current leading databases provide few ESCC cases, and some instances lack comprehensive clinical data, impeding our ability to gather adequate cases for data scrutiny and reciprocal verification. For instance, when employing the GEPIA database to validate LPXN's differential expression in ESCC, we included several esophageal adenocarcinoma cases. Additionally, a dearth of clinical information precluded disease-free survival analysis from our survival examination. To address these shortcomings, we executed numerous experimental authentications of LPXN's biological role in ESCC using clinical specimens. Of note is the absence of any relevant mechanisms pertaining to LPXN's predicted function in ESCC within our study. Future investigations should aim to further classify LPXN's mechanism and cellular functions in ESCC. The relationship between ESCC, immune cell infiltration, and immune checkpoints offers novel insights into additional potential roles for LPXN in ESCC treatment, albeit necessitating further experimental verification. The factor of controversy surrounding the selection of suitable biological matrices for potential biomarker exploration, considering both tissue-based and liquid biopsy samples, presents as diffuse and broadly agreed-upon biological specimens for routine practice. Notwithstanding, a significant strength lies in the technical methodologies employed in identifying molecular alterations of LPXN. This study underscores LPXN's potential part in fostering cancer and its mechanism in ESCC development. Nevertheless, the prospect of utilizing LPXN as a tumor marker to forecast the disease remains uncertain. Its specificity, sensitivity, and the need for clinical detection kits mandate additional research.

Through bioinformatics, molecular, and cellular analyses, we demonstrated the involvement of LPXN in the growth and progression of ESCC cells. LPXN may serve as a unique prognostic indicator for patients with ESCC, and it shows promise as a tool for predicting responses to immunotherapy. Immune regulators, immune cell infiltration, tumor microenvironment, immunological subtypes, drug resistance genes, and chemosensitivity in ESCC are correlated with increased LPXN expression.

Acknowledgements

We thank all the individuals who assisted with this article.

Authors’ contributions

Conception and design of the research: Yue-Quan Jiang, Fei Teng, and Zhi-Qiang Wang. Acquisition of data: Fei Teng, Yang Chen, and Fei Liao. Data analysis and interpretation: Fei Teng, Yang Chen, Yingzhao Wang, and Fei Liao. Statistical analysis: Fei Teng, Yingzhao Wang, and Yang Chen. Obtaining financing: , Yang Chen, and Zhi-Qiang Wang. Manuscript writing: Fei Teng and Fei Liao. Critical revision of the manuscript for intellectual content: Zhi-Qiang Wang and Fei Teng. All the authors have read and approved the final draft of the manuscript. All the authors contributed to the manuscript and approved the submitted version.

Availability of data and materials

The datasets analyzed in the current study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki (revised in 2013). The Ethics Committee of Chongqing University Cancer Hospital approved the study, and all patients and participants provided written informed consent to participate.

Consent for publication

Written informed consent was obtained from all individuals included in this study, and they agreed to the publication of their research findings.

Funding

This work was supported by the Postdoctoral Science Foundation of China (No. 2023M730435) and the Natural Science Foundation of Chongqing, China (CSTB2022NSCQ-MSX1143). The Rapid Service Fee was funded by the authors.

Competing Interests Statement

The authors declare no competing financial interests.

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No competing interests reported.

SupplementaryTable1.xlsx
Supplementary Table 1: The 263 chemotherapy drugs that are either FDA approved or on clinical trials.
SupplementaryTable2.xlsx
Supplementary Table 2: The information of primary antibodies used in IHC.
SupplementaryTable3.doc
Supplementary Table 3: Clinical information of the 32 patients that provided thetissue samples used in this study.
Supplementaryfigure1.png
Supplementary Fig. 1: Scatterplot of the relationship between LPXN expression and drug sensitivity (p-values in each image indicate statistical significance).

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Elucidating the Correlation between Leupaxin as a Prognostic Biomarker and Immunotherapeutic Efficacy in Esophageal Squamous Cell Carcinoma

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Abstract

Figures

Introduction

Materials and methods

Gene Expression Profiling Interactive Analysis

UALCAN Omnibus

GSE53625 from the GEO database

STRING database

GeneMANIA and TCGA databases

TIMER interface

Chemotherapy sensitivity analysis

Quantitative polymerase chain reaction

Immunohistochemistry staining, assessment, and scoring

Small hairpin RNAs (shRNAs) and lentiviruses

Western blotting

Cell culture and transfection

Celigo image cytometry assay

Flow cytometry analysis of apoptosis and cell cycle

Colony-forming assay

3-[4,5-Dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assay

Celigo scratch assay

Cell invasion and migration assays

Statistical analysis

Results

Characterization of differentially expressed LPXN in patients with EC/ESCC

Verification of LPXN expression in ESCC tissues and adjacent non-tumorous tissues

Association of LPXN with tumor pathological stage and patient survival in EC or ESCC

LPXN knockout inhibits ESCC cell proliferation

ESCC cells exhibit compromised penetration and migration abilities in the absence of LPXN

PPI network construction

Functional enrichment analysis of LPXN and its co-expressing genes in ESCC from TCGA dataset

Immune status and tumor microenvironment analysis of LPXN in the ESCC from TCGA dataset

Relationships between LPXN and PD-1/PD-L1, drug resistance genes, and immune regulators

Immune cell infiltration of LPXN in patients with EC

Verification of the association between LPXN and PD-L1 protein expression in ESCC cells

Verification of the LPXN immunotherapy prediction in ESCC

LPXN expression and cancer cell sensitivity to chemotherapy

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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