Immune and stromal scoring system associated with tumor microenvironment and prognosis: a gene-based multi-cancer analysis

Construction of a lung cancer 3D culture model based on alginate/gelatin micro-beads for drug evaluation

Background

Lung cancer is one of the most common malignant tumors worldwide. Despite advances in lung cancer treatment, patients still face challenges related to drug resistance and recurrence. Current methods for evaluating anti-cancer drug activity are insufficient, as they rely on two-dimensional (2D) cell culture and animal models. Therefore, the development of an in vitro drug evaluation model capable of predicting individual sensitivity to anti-cancer drugs would greatly enhance the success rate of drug treatments for lung cancer patients. The purpose of this research is to utilise conditional reprogramming technology to cultivate patient-derived lung cancer cells and to construct an in vitro 3D culture model using sodium alginate (SA) and gelatin. The aim is to study the biological characteristics of cells in the 3D culture model and to further investigate the sensitivity of anti-cancer drugs based on the alginate-gelatin 3D culture model. This approach provides new means and insights for personalized precision anti-cancer therapy and the development of new anti-cancer drugs.

Methods

Conditional reprogramming technology was used to generate conditionally reprogrammed lung adenocarcinoma cells (CRLCs). Alginate-gelatin hydrogel micro-beads were created to explore their potential use in the assessment of anti-cancer drugs. Cell proliferation was also examined using the MTS assay method. Live/dead staining was performed to estimate cell distribution and viability using calcein acetoxymethyl ester/propidium iodide (calcein-AM/PI) double staining. Protein expression was assessed by Western blot.

Results

The cells grown in the three-dimensional (3D) culture were in a state of continuous proliferation, and there was an obvious phenomenon of cell mass growth. The drug sensitivity assay results demonstrated that compared with the 2D-grown cells, the CRLCs grown in the alginate-gelatin hydrogel micro-beads exhibited more resistance to anti-cancer drugs. The results also showed that the 3D-cultured CRLCs showed greater protein expression levels of stem cell hallmarks, such as Nanog Homeobox (NANOG), SRY-Box Transcription Factor 2 (SOX-2), and aldehyde dehydrogenase 1 family member A1 (ALDH1A1), than the 2D-grown cells.

Conclusions

These findings suggest that the 3D hydrogel cell culture models more closely mimicked the in vivo biological and clinical behavior of cells, and demonstrated higher innate resistance to anti-cancer drugs than the 2D cell culture models, and thus could serve as valuable tools for diagnosis, drug screening, and personalized medicine.

Targeting the lung tumour stroma: harnessing nanoparticles for effective therapeutic interventions

Lung cancer remains an influential global health concern, necessitating the development of innovative therapeutic strategies. The tumour stroma, which is known as tumour microenvironment (TME) has a central impact on tumour expansion and treatment resistance. The stroma of lung tumours consists of numerous cells and molecules that shape an environment for tumour expansion. This environment not only protects tumoral cells against immune system attacks but also enables tumour stroma to attenuate the action of antitumor drugs. This stroma consists of stromal cells like cancer-associated fibroblasts (CAFs), suppressive immune cells, and cytotoxic immune cells. Additionally, the presence of stem cells, endothelial cells and pericytes can facilitate tumour volume expansion. Nanoparticles are hopeful tools for targeted drug delivery because of their extraordinary properties and their capacity to devastate biological obstacles. This review article provides a comprehensive overview of contemporary advancements in targeting the lung tumour stroma using nanoparticles. Various nanoparticle-based approaches, including passive and active targeting, and stimuli-responsive systems, highlighting their potential to improve drug delivery efficiency. Additionally, the role of nanotechnology in modulating the tumour stroma by targeting key components such as immune cells, extracellular matrix (ECM), hypoxia, and suppressive elements in the lung tumour stroma.

Lysine lactylation-based insight to understanding the characterization of cervical cancer

Lysine lactylation (Kla), a recently discovered post-translational modification (PTM), is not only present in histone proteins but also widely distributed among non-histone proteins in tumor cells and immunocytes. However, the precise characterization and functional implications of these non-histone Kla proteins remain to be explored. Herein, a comprehensive proteomic analysis of Kla was conducted in HeLa cells. As a result, a total of 3633 Kla sites on 1637 proteins were identified. Subsequently, the stable Kla substrates were obtained and sorted to investigate the characterization and function of Kla proteins. Moreover, we characterized the Kla-related features of cervical cancers through integrative analyses of multiple datasets with proteomes, transcriptomes and single-cell transcriptome profiling. Kla-related genes (KRGs) were used to stratify cervical cancers into two clusters (C1 and C2). C2 cluster display inhibition in glycosylation and increased oxidative phosphorylation activity with high survival rate. In addition, we constructed a prognostic model based on two lactate signature genes, namely ISY1 and PPP1R14B. Interestingly, our findings revealed a negative correlation between PPP1R14B expression and the infiltration of CD8+ T cells, as well as a lower survival rate. This observation was further validated at the single-cell resolution. Simultaneously, we found that K140R mutant of PPP1R14B resulted in the decrease of Kla level and enhanced the proliferation and migration capabilities of cervical cancer cell lines, suggesting PPP1R14B-K140la has an effect on tumor behaviors. Collectively, we provides a Kla-based insight to understanding the characterization of cervical cancer, offering a potential avenue for therapeutic approaches.

Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models

Background

Breast cancer (BC) constitutes a significant peril to global women's health. Contemporary research progressively suggests that mitochondrial dysfunction plays a pivotal role in both the inception and advancement of BC. However, investigations delving into the correlation between mitochondrial-related genes (MRGs) and the prognosis and metastasis of BC are still infrequent.

Methods

Utilizing data from the TCGA database, we employed the "limma" R package for differential expression analysis. Subsequently, both univariate and multivariate Cox regression analyses were executed, alongside LASSO Cox regression analysis, to pinpoint prognostic MRGs and to further develop the prognostic model. External validation (GSE88770 merged GSE425680) and internal validation were further conducted. Our investigation delved into a broad spectrum of analyses that included functional enrichment, metabolic and immune characteristics, immunotherapy response prediction, intratumor heterogeneity (ITH), mutation, tumor mutational burden (TMB), microsatellite instability (MSI), cellular stemness, single-cell, and drug sensitivity analysis. We validated the protein and mRNA expressions of prognostic MRGs in tissues and cell lines through immunohistochemistry and qRT-PCR. Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. The division between training and validation cohorts was set at 70% and 30%, respectively.

Results

A prognostic model was constructed by 9 prognostic MRGs, which were DCTPP1, FEZ1, KMO, NME3, CCR7, ISOC2, STAR, COMTD1, and ESR2. Patients within the high-risk group experienced more adverse outcomes than their counterparts in the low-risk group. The ROC curves and constructed nomogram showed that the model exhibited an excellent ability to predict overall survival (OS) for patients and the risk score was identified as an independent prognostic factor. The functional enrichment analysis showed a strong correlation between metabolic progression and MRGs. Additional research revealed that the discrepancies in outcomes between the two risk categories may be attributed to a variety of metabolic and immune characteristics, as well as differences in intratumor heterogeneity (ITH), tumor mutational burden (TMB), and cancer stemness indices. ITH, TIDE, and IPS analyses suggested that patients possessing a low-risk score may exhibit enhanced responsiveness to immunotherapy. Additionally, distant metastasis models were established by PDK4, NRF1, DCAF8, CHPT1, MARS2 and NAMPT. Among these, the XGBoost model showed the best predicting ability.

Conclusion

In conclusion, MRGs significantly influence the prognosis and metastasis of BC. The development of dual clinical prediction models offers crucial insights for tailored and precise therapeutic strategies, and paves the way for exploring new avenues in understanding the pathogenesis of BC.

VDR is a potential prognostic biomarker and positively correlated with immune infiltration: a comprehensive pan-cancer analysis with experimental verification

The vitamin D receptor (VDR) is a transcription factor that mediates a variety of biological functions of 1,25-dihydroxyvitamin D3. Although there is growing evidence of cytological and animal studies supporting the suppressive role of VDR in cancers, the conclusion is still controversial in human cancers and no systematic pan-cancer analysis of VDR is available. We explored the relationships between VDR expression and prognosis, immune infiltration, tumor microenvironment, or gene set enrichment analysis (GSEA) in 33 types of human cancers based on multiple public databases and R software. Meanwhile, the expression and role of VDR were experimentally validated in papillary thyroid cancer (PTC). VDR expression decreased in 8 types and increased in 12 types of cancer compared with normal tissues. Increased expression of VDR was associated with either good or poor prognosis in 13 cancer types. VDR expression was positively correlated with the infiltration of cancer-associated fibroblasts, macrophages, or neutrophils in 20, 12, and 10 cancer types respectively and this correlation was experimentally validated in PTC. Increased VDR expression was associated with increased percentage of stromal or immune components in tumor microenvironment (TME) in 24 cancer types. VDR positively and negatively correlated genes were enriched in immune cell function and energy metabolism pathways, respectively, in the top 9 highly lethal tumors. Additionally, VDR expression was increased in PTC and inhibited cell proliferation and migration. In conclusion, VDR is a potential prognostic biomarker and positively correlated with immune infiltration as well as stromal or immune components in TME in multiple human cancers.

Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies

BACKGROUND

This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment.

METHODS

Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles.

RESULTS

Molecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

CONCLUSION

The established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

Non-coding RNAs: a promising target for early metastasis intervention

ABSTRACT

Metastases account for the overwhelming majority of cancer-associated deaths. The dissemination of cancer cells from the primary tumor to distant organs involves a complex process known as the invasion-metastasis cascade. The underlying biological mechanisms of metastasis, however, remain largely elusive. Recently, the discovery and characterization of non-coding RNAs (ncRNAs) have revealed the diversity of their regulatory roles, especially as key contributors throughout the metastatic cascade. Here, we review recent progress in how three major types of ncRNAs (microRNAs, long non-coding RNAs, and circular RNAs) are involved in the multistep procedure of metastasis. We further examine interactions among the three ncRNAs as well as current progress in their regulatory mechanisms. We also propose the prevention of metastasis in the early stages of cancer progression and discuss current translational studies using ncRNAs as targets for metastasis diagnosis and treatments. These studies provide insights into developing more effective strategies to target metastatic relapse.

Identification of the expression patterns and potential prognostic role of m6A-RNA methylation regulators in Wilms Tumor

BACKGROUND

To explore the potential role of m6A methylation modification in Wilms Tumor (WT) by m6A-RNA Methylation (m6A) regulators.

METHODOLOGY

The association of m6A modification patterns with immune and prognostic characteristics of tumors was systematically evaluated using 19 m6A regulators extracted from Wilms Tumor's samples in public databases. A comprehensive model of "m6Ascore" was constructed using principal component analysis, and its prognostic value was evaluated.

RESULTS

Almost all m6A regulators were differentially expressed between WT and normal tissues. Unsupervised clustering identified three distinct m6A clusters that differed in both immune cell infiltration and biological pathways. The m6Ascore was constructed to quantify m6A modifications in individual patients. Our analysis suggests that m6Ascore is an independent prognostic factor for WT and can be used as a novel predictor of WT prognosis.

CONCLUSIONS

This study comprehensively explored and systematically characterized m6A modifications in WT. m6A modification patterns play a critical role in the tumor immune microenvironment (TIME) and WT prognosis. m6Ascore provides a more comprehensive understanding of m6A modifications in WT and offers a practical tool for predicting WT prognosis. This study will help clinicians to identify valid indicators of WT to improve the poor prognosis of this disease.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at https://www.aliyundrive.com/drive/folder/64be739cd6956a741fb24670baeea53422be6024 .

An Immune-Related Gene Signature for Predicting Survival and Immunotherapy Efficacy in Esophageal Adenocarcinoma

BACKGROUND Immune checkpoint inhibitor (ICI) therapy has attracted wide attention in the treatment of malignant tumors. This study was designed to build a prognostic model based on immune-related genes for esophageal adenocarcinoma (EAC). MATERIAL AND METHODS The expression of immune-related differentially-expressed genes (IRDEGs) between EAC and normal samples from The Cancer Genome Atlas database was analyzed. Univariate and multivariate Cox regressions were used to identify the prognostic IRDEGs and construct an immune-related gene signature (IRGS) to predict the overall survival (OS) of EAC patients. Then, the molecular mechanisms and immune characteristics were comprehensively analyzed. RESULTS A total of 111 IRDEGs were obtained from the weighted gene co-expression network analysis. Univariate Cox regression analysis showed that 12 IRDEGs (P<0.05 for all) were linked with OS in the EAC patients. Four genes were used to construct the IRGS based on the multivariate Cox regression analysis. Patients in the high-risk group showed worse OS than those in the low-risk group (P<0.001). A high-risk score was related to DNA replication relevant pathways, an increase in mutation rate, and an increase in activated mast cell infiltration. Patients with high-risk scores had lower tumor immune dysfunction and exclusion scores (P<0.001). CONCLUSIONS IRDEGs may be involved in the progression of EAC. The high-risk group is more suitable for immunotherapy, which may provide a reference value for the treatment of clinical EAC patients. Therefore, it is possible to identify the patients who are better suited for ICI therapy.

Tumor purity-associated genes influence hepatocellular carcinoma prognosis and tumor microenvironment

Introduction

Tumor purity takes on critical significance to the progression of solid tumors. The aim of this study was at exploring potential prognostic genes correlated with tumor purity in hepatocellular carcinoma (HCC) by bioinformatics analysis.

Methods

The ESTIMATE algorithm was applied for determining the tumor purity of HCC samples from The Cancer Genome Atlas (TCGA). The tumor purity-associated genes with differential expression (DEGs) were identified based on overlap analysis, weighted gene co-expression network analysis (WGCNA), and differential expression analysis. The prognostic genes were identified in terms of the prognostic model construction based on the Kaplan-Meier (K-M) survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. The expression of the above-described genes was further validated by the GSE105130 dataset from the Gene Expression Omnibus (GEO) database. We also characterized the clinical and immunophenotypes of prognostic genes. Gene set enrichment analysis (GSEA) was carried out for exploring the biological signaling pathway.

Results

A total of 26 tumor purity-associated DEGs were identified, which were involved in biological processes such as immune/inflammatory responses and fatty acid elongation. Ultimately, we identified ADCK3, HK3, and PPT1 as the prognostic genes for HCC. Moreover, HCC patients exhibiting higher ADCK3 expression and lower HK3 and PPT1 expressions had a better prognosis. Furthermore, high HK3 and PPT1 expressions and low ADCK3 expression resulted in high tumor purity, high immune score, high stromal score, and high ESTIMATE score. GSEA showed that the abovementioned prognostic genes showed a significant correlation with immune-inflammatory response, tumor growth, and fatty acid production/degradation.

Discussion

In conclusion, this study identified novel predictive biomarkers (ADCK3, HK3, and PPT1) and studied the underlying molecular mechanisms of HCC pathology initially.

High Expression of Triggering Receptor Expressed on Myeloid Cells 1 Predicts Poor Prognosis in Glioblastoma

Background

Glioblastoma (GBM) is a highly malignant tumor with poor prognosis, and new treatment strategies are urgently needed. Currently, the role of triggering receptor expressed on myeloid cells 1 (TREM-1) in tumors has been studied, but the role of TREM-1 in GBM remains unclear.

Methods

Immunohistochemical staining for TREM-1 was performed in 91 patients diagnosed with GBM. Clinicopathological characteristics and survival times were recorded. TREM-1 expression and its effect on prognosis were analyzed using online Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA) databases. The expression profile of TCGA-GBM cohort was used to perform functional enrichment analysis. The CIBERSORT method and Tumor Immune Estimation Resource (TIMER) database were used to estimate the tumor-infiltrating immune cells (TIICs). The ESTIMATE algorithm was used to estimate the immune-stromal scores. Finally, the relationships of TREM-1 with TIICs, immune-stromal score, and immune checkpoint genes (ICGs) were analyzed.

Results

The expression of TREM-1 was upregulated in GBM, and high TREM-1 expression predicted a poor prognosis. TREM-1, surgical resection, postoperative radiotherapy, and temozolomide (TMZ) chemotherapy were associated with the survival time of patients with GBM, but only surgical resection and TREM-1 expression were independent prognostic factors. GBM with high TREM-1 expression exhibited increased neutrophil and macrophage infiltration. TREM-1 was positively associated with the immune-stromal score and multiple ICGs, and most of which were involved in immunosuppressive responses.

Conclusion

The present study revealed that high expression of TREM-1 in GBM is an independent poor prognosis factor and that TREM-1 is associated with the immunosuppressive microenvironment. Thus, blocking TREM-1 may be a strategy for enhancing the GBM immune response.

Prognostic value of preoperative immune-nutritional scoring systems in remnant gastric cancer patients undergoing surgery

BACKGROUND

Remnant gastric cancer (GC) is defined as GC that occurs five years or more after gastrectomy. Systematically evaluating the preoperative immune and nutritional status of patients and analyzing its prognostic impact on postoperative remnant gastric cancer (RGC) patients are crucial. A simple scoring system that combines multiple immune or nutritional indicators to identify nutritional or immune status before surgery is necessary.

AIM

To evaluate the value of preoperative immune-nutritional scoring systems in predicting the prognosis of patients with RGC.

METHODS

The clinical data of 54 patients with RGC were collected and analyzed retrospectively. Prognostic nutritional index (PNI), controlled nutritional status (CONUT), and Naples prognostic score (NPS) were calculated by preoperative blood indicators, including absolute lymphocyte count, lymphocyte to monocyte ratio, neutrophil to lymphocyte ratio, serum albumin, and serum total cholesterol. Patients with RGC were divided into groups according to the immune-nutritional risk. The relationship between the three preoperative immune-nutritional scores and clinical characteristics was analyzed. Cox regression and Kaplan–Meier analysis was performed to analyze the difference in overall survival (OS) rate between various immune-nutritional score groups.

RESULTS

The median age of this cohort was 70.5 years (ranging from 39 to 87 years). No significant correlation was found between most pathological features and immune-nutritional status (P > 0.05). Patients with a PNI score < 45, CONUT score or NPS score ≥ 3 were considered to be at high immune-nutritional risk. The areas under the receiver operating characteristic curves of PNI, CONUT, and NPS systems for predicting postoperative survival were 0.611 [95% confidence interval (CI): 0.460–0.763; P = 0.161], 0.635 (95%CI: 0.485–0.784; P = 0.090), and 0.707 (95%CI: 0.566–0.848; P = 0.009), respectively. Cox regression analysis showed that the three immune-nutritional scoring systems were significantly correlated with OS (PNI: P = 0.002; CONUT: P = 0.039; NPS: P < 0.001). Survival analysis revealed a significant difference in OS between different immune-nutritional groups (PNI: 75 mo vs 42 mo, P = 0.001; CONUT: 69 mo vs 48 mo, P = 0.033; NPS: 77 mo vs 40 mo, P < 0.001).

CONCLUSION

These preoperative immune-nutritional scores are reliable multidimensional prognostic scoring systems for predicting the prognosis of patients with RGC, in which the NPS system has relatively effective predictive performance.

The application of patient-derived organoid in the research of lung cancer

Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. However, mechanisms of its progression remained unclear and new treatments against this disease are rapidly emerging. As a novel preclinical model, patient-derived organoid (PDO) can also be established from the patient's tumor tissue and cultured in the laboratory, which preserves the key biological characteristics of the original tumor. Compared to the patient-derived xenograft (PDX) model of lung cancer, the culture success rate is improved, and the time and cost of model establishment are largely reduced. PDO is also expected to provide a more individual model to predict the efficacy of anti-cancer treatment in vitro. This paper summarizes the current application of PDO in the translational research of lung cancer.

Core immune cell infiltration signatures identify molecular subtypes and promote precise checkpoint immunotherapy in cutaneous melanoma

Yutao Wang, China Medical University, ChinaThe tumor microenvironment (TME) has been shown to impact the prognosis of tumors in patients including cutaneous melanoma (CM); however, not all components of TME are important. Given the aforementioned situation, the functional immune cell contents correlated with CM patient prognosis are needed to optimize present predictive models and reflect the overall situation of TME. We developed a novel risk score named core tumor-infiltrating immune cell score (cTICscore), which showed certain advantages over existing biomarkers or TME-related signatures in predicting the prognosis of CM patients. Furthermore, we explored a new gene signature named cTILscore−related module gene score (cTMGs), based on four identified TME-associated genes (GCH1, GZMA, PSMB8, and PLAAT4) showing a close correlation with the cTICscore, which was generated by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis to facilitate clinical application. Patients with low cTMGs had significantly better overall survival (OS, P = 0.002,< 0.001, = 0.002, and = 0.03, respectively) in the training and validating CM datasets. In addition, the area under the curve values used to predict the immune response in four CM cohorts were 0.723, 0.723, 0.754, and 0.792, respectively, and that in one gastric cohort was 0.764. Therefore, the four-gene signature, based on cTICscore, might improve prognostic information, serving as a predictive tool for CM patients receiving immunotherapy.cutaneous melanoma, tumor microenvironment, prognosis, immunotherapy, cTICscore

A systemic pan-cancer analysis of MPZL3 as a potential prognostic biomarker and its correlation with immune infiltration and drug sensitivity in breast cancer

Background

This study aimed to analyze the role of myelin protein zero-like 3 (MPZL3), a single membrane glycoprotein, in prognosis, tumor immune infiltration, and drug susceptibility in human cancers.

Methods

Data regarding MPZL3 were extracted from the TCGA, GTEx, CellMiner, CCLE, TIMER, GSEA, and USCS Xena databases. The expression difference, survival outcomes, DNA methylation, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), tumor microenvironment (TME), immune cell infiltration, and drug sensitivity of MPZL3 were analyzed by R language software. Cell proliferation and drug sensitivity tests were applied to analyze the biological role of MPZL3 and drug sensitivities in breast cancer.

Results

MPZL3 was highly expressed in most cancer types and correlated with unfavorable survival outcomes in several cancers. TMB, MSI, MMR, DNA methylation, and RNA modification played a significant role in mediating MPZL3 dysregulation in cancers, and MPZL3 was closely linked to CD8+ T cells and CD4+ T immune infiltration. The MPML3 mRNA level was associated with protein secretion, the Notch signaling pathway, and heme metabolism. In addition, drug sensitivity analysis and validation also indicated that MPZL3 expression influenced the sensitivity of therapeutics targeting EGFR, ABL, FGFR, etc. Additionally, MPZL3 overexpression contributed to proliferation and drug sensitivity in different subtypes of breast cancer.

Conclusions

This study provides a comprehensive analysis and understanding of the oncogenic roles of the pan-cancer gene MPZL3 across different tumors, including breast cancer. MPZL3 could be a potential prognostic biomarker and therapeutic target for breast cancer.