The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma

The role of THBS1 and PDGFD in the immune microenvironment of Helicobacter pylori-associated gastric cancer

BACKGROUND AND STUDY AIMS

Immunotherapy has emerged as a hot topic in cancer treatment in recent years and has also shown potential in the treatment of Helicobacter pylori-associated gastric cancer. However, there is still a need to identify potential immunotherapy targets.

MATERIAL AND METHODS

We used the GSE116312 dataset of Helicobacter pylori-associated gastric cancer to identify differentially expressed genes, which were then overlapped with immune genes from the ImmPort database. The identified immune genes were used to classify gastric cancer samples and evaluate the relationship between classification and tumor mutations, as well as immune infiltration. An immune gene-based prognostic model was constructed, and the expression levels of the genes involved in constructing the model were explored in the tumor immune microenvironment.

RESULTS

We successfully identified 60 immune genes and classified gastric cancer samples into two subtypes, which showed differences in prognosis, tumor mutations, immune checkpoint expression, and immune cell infiltration. Subsequently, we constructed an immune prognostic model consisting of THBS1 and PDGFD, which showed significant associations with macrophages and fibroblasts.

CONCLUSION

We identified abnormal expression of THBS1 and PDGFD in cancer-associated fibroblasts (CAFs) within the tumor immune microenvironment, suggesting their potential as therapeutic targets.

Exploring the impact of PDGFD in osteosarcoma metastasis through single-cell sequencing analysis

PURPOSE

The overall survival rate for metastatic osteosarcoma hovers around 20%. Responses to second-line chemotherapy, targeted therapies, and immunotherapies have demonstrated limited efficacy in metastatic osteosarcoma. Our objective is to validate differentially expressed genes and signaling pathways between non-metastatic and metastatic osteosarcoma, employing single-cell RNA sequencing (scRNA-seq) and additional functional investigations. We aim to enhance comprehension of metastatic mechanisms and potentially unveil a therapeutic target.

METHODS

scRNA-seq was performed on two primary osteosarcoma lesions (1 non-metastatic and 1 metastatic). Seurat package facilitated dimensionality reduction and cluster identification. Copy number variation (CNV) was predicted using InferCNV. CellChat characterized ligand-receptor-based intercellular communication networks. Differentially expressed genes underwent GO function enrichment analysis and GSEA. Validation was achieved through the GSE152048 dataset, which identified PDGFD-PDGFRB as a common ligand-receptor pair with significant contribution. Immunohistochemistry assessed PDGFD and PDGFRB expression, while multicolor immunofluorescence and flow cytometry provided insight into spatial relationships and the tumor immune microenvironment. Kaplan-Meier survival analysis compared metastasis-free survival and overall survival between high and low levels of PDGFD and PDGFRB. Manipulation of PDGFD expression in primary osteosarcoma cells examined invasion abilities and related markers.

RESULTS

Ten clusters encompassing osteoblasts, osteoclasts, osteocytes, fibroblasts, pericytes, endothelial cells, myeloid cells, T cells, B cells, and proliferating cells were identified. Osteoblasts, osteoclasts, and osteocytes exhibited heightened CNV levels. Ligand-receptor-based communication networks exposed significant fibroblast crosstalk with other cell types, and the PDGF signaling pathway was activated in non-metastatic osteosarcoma primary lesion. These results were corroborated by the GSE152048 dataset, confirming the prominence of PDGFD-PDGFRB as a common ligand-receptor pair. Immunohistochemistry demonstrated considerably greater PDGFD expression in non-metastatic osteosarcoma tissues and organoids, correlating with extended metastasis-free and overall survival. PDGFRB expression showed no significant variation between non-metastatic and metastatic osteosarcoma, nor strong correlations with survival times. Multicolor immunofluorescence suggested co-localization of PDGFD with PDGFRB. Flow cytometry unveiled a highly immunosuppressive microenvironment in metastatic osteosarcoma. Manipulating PDGFD expression demonstrated altered invasive abilities and marker expressions in primary osteosarcoma cells from both non-metastatic and metastatic lesions.

CONCLUSIONS

scRNA-seq illuminated the activation of the PDGF signaling pathway in primary lesion of non-metastatic osteosarcoma. PDGFD displayed an inhibitory effect on osteosarcoma metastasis, likely through the suppression of the EMT signaling pathway.

Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram

Objective

To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma.

Methods

A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA).

Results

Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram.

Conclusion

The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.

Comprehensive bioinformatics analysis reveals the oncogenic role of FoxM1 and its impact on prognosis, immune microenvironment, and drug sensitivity in osteosarcoma

Osteosarcoma, a highly malignant bone tumor primarily affecting adolescents, presents a significant challenge in cancer therapy due to its resistance to chemotherapy. This study explores the multifaceted impact of the transcription factor FoxM1 on osteosarcoma, shedding light on its pivotal role in tumor progression, immune microenvironment modulation, and drug response. Utilizing publicly available datasets from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET) databases, we conducted an in-depth bioinformatics analysis. Our findings illuminate the far-reaching implications of FoxM1 in osteosarcoma, emphasizing its significance as a potential therapeutic target. Differential expression analysis and Gene Set Enrichment Analysis (GSEA) revealed FoxM1's influence on critical pathways related to apoptosis, cell cycle regulation, and DNA repair. Notably, FoxM1 expression correlated with poor clinical outcomes in osteosarcoma patients, highlighting its prognostic relevance. Additionally, FoxM1 was found to modulate the immune microenvironment within tumor tissues, impacting immune cell infiltration, immunomodulators, immune checkpoints, and chemokines. Furthermore, a prognostic model based on FoxM1-coexpressed genes demonstrated its effectiveness in predicting patient survival. Drug sensitivity analysis indicated FoxM1's association with drug response, potentially guiding personalized treatment approaches. Hub gene screening identified RAB23 as a key target regulated by FoxM1, with RAB23 shown to influence osteosarcoma cell growth. This study also confirmed FoxM1's overexpression in osteosarcoma tissues compared to normal tissues, and its association with clinicopathological characteristics, including clinical stage, pathological type, and lung metastasis. In conclusion, FoxM1 emerges as a central player in the pathogenesis of osteosarcoma, impacting gene expression, immune responses, and therapeutic outcomes. This comprehensive analysis deepens our understanding of FoxM1's role in osteosarcoma and offers potential avenues for improved diagnosis and treatment.

Overview of CD70 as a Potential Therapeutic Target for Osteosarcoma

Osteosarcoma is a primary malignant bone tumor. Effective chemotherapy regimens for refractory disease are scarce, accounting for no improvement in survival. Immune-based cell therapies have emerged as novel alternatives. However, advancements with these therapies have been seen mostly when immune cells are armed to target specific tumor Ags. Recent studies identified cluster of differentiation 70 (CD70) as a promising target to osteosarcoma particularly because CD70 is highly expressed in osteosarcoma lung metastases (Pahl et al. 2015. Cancer Cell Int. 15: 31), and its overexpression by tumors has been correlated with immune evasion and tumor proliferation (Yang et al. 2007. Blood 110: 2537-2544). However, the limited knowledge of the overall CD70 expression within normal tissues and the potential for off-target effect pose several challenges (Flieswasser et al. 2022. J. Exp. Clin. Cancer Res. 41: 12). Nonetheless, CD70-based clinical trials are currently ongoing and are preliminarily showing promising results for patients with osteosarcoma. The present review sheds light on the recent literature on CD70 as it relates to osteosarcoma and highlights the benefits and challenges of targeting this pathway.

Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes

Osteosarcoma is a relatively rare but aggressive cancer of the bones with a shortage of effective biomarkers. Although less common in humans, Osteosarcomas are fairly common in adult pet dogs and have been shown to share many similarities with their human analogs. In this work, we analyze bulk transcriptomic data of 213 primary and 100 metastatic Osteosarcoma samples from 210 pet dogs enrolled in nation-wide clinical trials to uncover three Tumor Microenvironment (TME)-based subtypes: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM) and Immune Desert (ID) with distinct cell type compositions, oncogenic pathway activity and chromosomal instability. Furthermore, leveraging bulk transcriptomic data of canine primary tumors and their matched metastases from different sites, we characterize how the Osteosarcoma TME evolves from primary to metastatic disease in a standard of care clinical setting and assess its overall impact on clinical outcomes of canines. Most importantly, we find that TME-based subtypes of canine Osteosarcomas are conserved in humans and predictive of progression free survival outcomes of human patients, independently of known prognostic biomarkers such as presence of metastatic disease at diagnosis and percent necrosis following chemotherapy. In summary, these results demonstrate the power of using canines to model the human Osteosarcoma TME and discover novel biomarkers for clinical translation.

Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma

Background

Soft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS.

Methods

The ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis.

Results

A novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells.

Conclusion

In sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

PDGF-D-induced immunoproteasome activation and cell-cell interactions

Platelet-derived growth factor-D (PDGF-D) is abundantly expressed in ocular diseases. Yet, it remains unknown whether and how PDGF-D affects ocular cells or cell-cell interactions in the eye. In this study, using single-cell RNA sequencing (scRNA-seq) and a mouse model of PDGF-D overexpression in retinal pigment epithelial (RPE) cells, we found that PDGF-D overexpression markedly upregulated the key immunoproteasome genes, leading to increased antigen processing/presentation capacity of RPE cells. Also, more than 6.5-fold ligand-receptor pairs were found in the PDGF-D overexpressing RPE-choroid tissues, suggesting markedly increased cell-cell interactions. Moreover, in the PDGF-D-overexpressing tissues, a unique cell population with a transcriptomic profile of both stromal cells and antigen-presenting RPE cells was detected, suggesting PDGF-D-induced epithelial-mesenchymal transition of RPE cells. Importantly, administration of ONX-0914, an immunoproteasome inhibitor, suppressed choroidal neovascularization (CNV) in a mouse CNV model in vivo. Together, we show that overexpression of PDGF-D increased pro-angiogenic immunoproteasome activities, and inhibiting immunoproteasome pathway may have therapeutic value for the treatment of neovascular diseases.

Strategies to Overcome Resistance to Immune-Based Therapies in Osteosarcoma

Improving the prognosis and cure rate of HGOSs (high-grade osteosarcomas) is an absolute need. Immune-based treatment approaches have been increasingly taken into consideration, in particular for metastatic, relapsed and refractory HGOS patients, to ameliorate the clinical results currently achieved. This review is intended to give an overview on the immunotherapeutic treatments targeting, counteracting or exploiting the different immune cell compartments that are present in the HGOS tumor microenvironment. The principle at the basis of these strategies and the possible mechanisms that HGOS cells may use to escape these treatments are presented and discussed. Finally, a list of the currently ongoing immune-based trials in HGOS is provided, together with the results that have been obtained in recently completed clinical studies. The different strategies that are presently under investigation, which are generally aimed at abrogating the immune evasion of HGOS cells, will hopefully help to indicate new treatment protocols, leading to an improvement in the prognosis of patients with this tumor.

Directionally Sensitive Fractal Radiomics Compatible With Irregularly Shaped Magnetic Resonance Tumor Regions of Interest: Association With Osteosarcoma Chemoresistance

BACKGROUND

Computational analysis of routinely acquired MRI has potential to improve the tumor chemoresistance prediction and to provide decision support in precision medicine, which may extend patient survival. Most radiomic analytical methods are compatible only with rectangular regions of interest (ROIs) and irregular tumor shape is therefore an important limitation. Furthermore, the currently used analytical methods are not directionally sensitive.

PURPOSE

To implement a tumor analysis that is directionally sensitive and compatible with irregularly shaped ROIs.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 54 patients with histopathologic diagnosis of primary osteosarcoma on tubular long bones and with prechemotherapy MRI.

FIELD STRENGTH/SEQUENCE

A 1.5 T, T2-weighted-short-tau-inversion-recovery-fast-spin-echo.

ASSESSMENT

A model to explore associations with osteosarcoma chemo-responsiveness included MRI data obtained before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Osteosarcoma morphology was analyzed in the MRI data by calculation of the nondirectional two-dimensional (2D) and directional and nondirectional one-dimensional (1D) Higuchi dimensions (Dh). MAP chemotherapy response was assessed by histopathological necrosis.

STATISTICAL TESTS

The area under the receiver operating characteristic (ROC) curve (AUC) evaluated the association of the calculated features with the actual chemoresponsiveness, using tumor histopathological necrosis (95%) as the endpoint. Least absolute shrinkage and selection operator (LASSO) machine learning and multivariable regression were used for feature selection. Significance was set at <0.05.

RESULTS

The nondirectional 1D Dh reached an AUC of 0.88 in association with the 95% tumor necrosis, while the directional 1D analysis along 180 radial lines significantly improved this association according to the Hanley/McNeil test, reaching an AUC of 0.95. The model defined by variable selection using LASSO reached an AUC of 0.98. The directional analysis showed an optimal predictive range between 90° and 97° and revealed structural osteosarcoma anisotropy manifested by its directionally dependent textural properties.

DATA CONCLUSION

Directionally sensitive radiomics had superior predictive performance in comparison to the standard nondirectional image analysis algorithms with AUCs reaching 0.95 and full compatibility with irregularly shaped ROIs.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

Integrated Analysis of Multi-Omics Data to Establish a Hypoxia-Related Prognostic Model in Osteosarcoma

Background:

Osteosarcoma (OS) is the most common malignant bone tumor in clinical practice, and currently, the ability to predict prognosis in the diagnosis of OS is limited. There is an urgent need to find new diagnostic methods and treatment strategies for OS.

Material and methods:

We downloaded the multi-omics data for OS from the TARGET database. Prognosis-associated methylation sites were used to identify clustered subtypes of OS, and OS was classified into 3 subtypes (C1, C2, C3). Survival analysis showed significant differences between the C3 subtype and the other subtypes. Subsequently, differentially expressed genes (DEGs) across subtypes were screened and subjected to pathway enrichment analysis.

Results:

A total of 249 DEGs were screened from C3 subtype to other subtypes. Metabolic pathway enrichment analysis showed that DEGs were significantly enriched to the hypoxic pathway. Based on univariate and multivariate COX regression analysis, 12 genes from the hypoxia pathway were further screened and used to construct hypoxia-related prognostic model (HRPM). External validation of the HRPM was performed on the GSE21257 dataset. Finally, differences in survival and immune infiltration between high and low risk score groups were compared.

Conclusion:

In summary, we proposed a hypoxia-associated risk model based on a 12-gene expression signature, which is potentially valuable for prognostic diagnosis of patients with OS.

An Immunity-Related Gene Model Predicts Prognosis in Cholangiocarcinoma

The prognosis of patients with cholangiocarcinoma (CCA) is closely related to both immune cell infiltration and mRNA expression. Therefore, we aimed at conducting multi-immune-related gene analyses to improve the prediction of CCA recurrence. Immune-related genes were selected from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Immunology Database and Analysis Portal (ImmPort). The least absolute shrinkage and selection operator (LASSO) regression model was used to establish the multi-gene model that was significantly correlated with the recurrence-free survival (RFS) in two test series. Furthermore, compared with single genes, clinical characteristics, tumor immune dysfunction and exclusion (TIDE), and tumor inflammation signature (TIS), the 8-immune-related differentially expressed genes (8-IRDEGs) signature had a better prediction value. Moreover, the high-risk subgroup had a lower density of B-cell, plasma, B-cell naïve, CD8+ T-cell, CD8+ T-cell naïve, and CD8+ T-cell memory infiltration, as well as more severe immunosuppression and higher mutation counts. In conclusion, the 8-IRDEGs signature was a promising biomarker for distinguishing the prognosis and the molecular and immune features of CCA, and could be beneficial to the individualized immunotherapy for CCA patients.

Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

Background

Chemoresistance is one of the leading causes that severely limits the success of osteosarcoma treatment. Evaluating chemoresistance before chemotherapy poses a new challenge for researchers. We established an effective chemoresistance risk scoring model for prechemotherapy osteosarcoma using single-cell sequencing.

Methods

We comprehensively analyzed osteosarcoma data from the bulk mRNA sequencing dataset TARGET-OS and the single-cell RNA sequencing (scRNA-seq) dataset GSE162454. Chemoresistant tumor clusters were identified using enrichment analysis and AUCell scoring. Its differentiated trajectory was achieved with inferCNV and pseudotime analysis. Ligand-receptor interactions were annotated with iTALK. Furthermore, we established a chemoresistance risk scoring model using LASSO regression based on scRNA-seq-based markers of chemoresistant tumor clusters. The TARGET-OS dataset was used as the training group, and the bulk mRNA array dataset GSE33382 was used as the validation group. Finally, the performance was verified for its discriminatory ability and calibration.

Results

Using bulk RNA data, we found that osteogenic expression was upregulated in chemoresistant osteosarcoma as compared to chemosensitive osteosarcoma. Then, we transferred the bulk RNA findings to scRNA-seq and noticed osteosarcoma tumor clusters C14 and C25 showing osteogenic cancer stem cell expression patterns, which fit chemoresistant characteristics. C14 and C25 possessed bridge roles in interactions with other clusters. On the one hand, they received various growth factor stimulators and could potentially transform into a proliferative state. On the other hand, they promote local tumor angiogenesis, bone remodeling and immunosuppression. Next, we identified a ten-gene signature from the C14 and C25 markers and constructed a chemoresistant risk scoring model using LASSO regression model. Finally, we found that chemoresistant osteosarcoma had higher chemoresistance risk score and that the model showed good discriminatory ability and calibration in both the training and validation groups (AUCtrain = 0.82; AUCvalid = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment (AUCvalid-scRNA = 0.84; AUCvalid-bulk DEGs = 0.54).

Conclusions

Our work provides insights into understanding chemoresistant osteosarcoma tumor cells and using single-cell sequencing to establish a chemoresistance risk scoring model. The model showed good discriminatory ability and calibration and provided us with a feasible way to evaluate chemoresistance in prechemotherapy osteosarcoma.

Identification of a Solute Carrier Family-Based Signature for Predicting Overall Survival in Osteosarcoma

Given the important role of SLC family in essential physiological processes including nutrient uptake, ion transport, and waste removal, and that their dysregulation was found in distinct forms of cancer, here we identified a novel gene signature of SLC family for patient risk stratification in osteosarcoma. Gene expression data and relevant clinical materials of osteosarcoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Prognosis-related SLC genes were identified by performing univariate Cox regression analysis and were utilized to construct a four-SLC gene signature in osteosarcoma. It allowed patients to be classified into high- and low-risk groups, and Kaplan-Meier survival analysis in the training, testing, entire, and external GSE21257 cohorts suggested that the overall survival of patients in high-risk group was consistently worse than that in low-risk group, suggesting the promising accuracy and generalizability of the SLC-based signature in predicting the prognosis of patients with osteosarcoma. Moreover, univariate and multivariate Cox regression analyses indicated that the derived risk score was the only independent prognostic factor for osteosarcoma patients in TCGA and GSE21257 cohorts. Besides, a prognostic nomogram comprising the derived risk score and clinical features including gender and age was developed for clinical decision-making. Functional enrichment analyses of the differentially expressed genes between high- and low-risk group revealed that immune-related biological processes and pathways were significantly enriched. Estimation of tumor immune microenvironment using ESTIMATE algorithm revealed that patients with lower risk score had higher stromal, immune, and ESTIMATE score, and lower tumor purity. ssGSEA analyses indicated that the scores of various immune subpopulations including CD8+ T cells, DCs, and TIL were lower in high-risk group than these in low-risk group in both cohorts. As for the related immune functions, the scores of APC co-inhibition, CCR, check-point, T cell co-stimulation, and Type II IFN response were lower in high-risk group than these in low-risk group in both cohorts. In all, we identified a novel prognostic signature based on four SLC family genes that accurately predicted overall survival in osteosarcoma patients. Furthermore, the signature is linked to differences in immunological status and immune cell infiltrations in the tumor microenvironment.

Identification of Immune-Related Breast Cancer Chemotherapy Resistance Genes via Bioinformatics Approaches

Chemotherapy resistance in breast cancer is an important factor affecting the prognosis of breast cancer patients. We computationally analyzed the differences in gene expression before and after chemotherapy in breast cancer patients, drug-sensitive groups, and drug-resistant groups. Through functional enrichment analysis, immune microenvironment analysis, and other computational analysis methods, we identified PRC1, GGTLC1, and IRS1 as genes that may mediate breast cancer chemoresistance through the immune pathway. After validation of certain other clinical datasets and in vitro cellular assays, we found that the above three genes influenced drug resistance in breast cancer patients and were closely related to the tumor immune microenvironment. Our finding that chemoresistance in breast cancer could be influenced by the mediation of tumor immunity expanded our knowledge of how to address this problem and could guide future research involving chemoresistance.

miR-138 Reduces the Dysfunction of T Follicular Helper Cells in Osteosarcoma via the PI3K/Akt/mTOR Pathway by Targeting PDK1

OBJECTIVE

To study the effect of miR-138 on the function of osteosarcoma (OS) T follicular helper cells (Tfh cells) and its mechanism.

METHODS

Peripheral blood mononuclear cells (PBMCs) were isolated from patients with osteosarcoma (OS group) and healthy volunteers (control group). CD4+CXCR5+ Tfh cells and CD9+ B cells were sorted by flow cytometry. qRT-PCR was used to detect the expression of miR-138 and PDK1 in the peripheral blood and CD4+CXCR5+ Tfh cells. Flow cytometry was employed to detect the proportion of CD4+CXCR5+ Tfh cells in CD4+ T cells, the level of CD40L in CD4+CXCR5+ Tfh cells, and the expression of CD27 and CD38 in B cells. Western blot was used to determine the protein expression of PDK1, PI3K, p-Akt, Akt, p-mTOR, and mTOR. In addition, dual-luciferase reporter assay was performed to verify the relationship between miR-138 and PDK1. ELISA method was used to determine the levels of IgM, IgG, IL-10, and IL-21.

RESULTS

Compared with that of the control group, the expression of miR-138 in PBMC and CD4+CXCR5+ Tfh cells of the OS group was lower; overexpression of miR-138 could promote the maturation of Tfh cells and immature B cells. The results of the dual-luciferase report experiment showed that miR-138 can target and negatively regulate PDK1, and PDK1 can reverse the effect of miR-138 on the function of Tfh cells and immature B cells.

CONCLUSION

miR-138 inhibits the PI3K/Akt/mTOR pathway by targeting and negatively regulating PDK1 to alleviate the dysfunction of T follicular helper cells in OS.

Tumor-Infiltrating Lymphocytes and Cancer Markers in Osteosarcoma: Influence on Patient Survival

Simple Summary

Osteosarcoma (OST) is the most common type of high-grade primary bone tumor, which mainly affects young adults. Despite the efforts that have been made to address the importance of immune-related factors in OST, there is still a lot to understand. The purpose of the current study was to evaluate the tumor-infiltrating lymphocytes (TIL), the expression of proteins involved in tumor biology, and their impact on the clinical outcome of OST patients. Our results suggest that the presence of tumor-infiltrating CD4+ cells provides protection to patients, and that CD8+ cells have a significant impact on the patient’s overall survival (OS) and progression-free survival (PFS). In addition, a strong association of tumor-infiltrating CD4+ cells and the presence of CD44s expression in tumor samples was observed. These findings reinforce the idea that TIL and the expression of tumor markers should be taken into consideration in order to improve OST treatment and management.

Abstract

Osteosarcoma (OST) is the most common type of high-grade primary bone tumor, which mainly affects young adults. The current standard of care for OST combines surgical resection with chemotherapy. The clinical outcomes and the current options to treat OST patients are unsatisfactory and novel treatment strategies are needed. The crosstalk between tumor cells and immune cells is essential to the OST microenvironment. Despite the efforts that have been made to address the importance of immune-related factors in OST, there is still a lot to understand. The purpose of the current study was to evaluate the tumor-infiltrating lymphocytes (TIL), the expression of proteins involved in tumor biology, and their impact on the clinical outcome of OST patients. We studied 93 samples of OST patients using immunohistochemistry and histomorphometry. We looked for the infiltration of CD3+, CD4+, CD8+, TIA1+ and CD20+ cells and for the expression of CD44 standard (CD44s) and variant 6 (CD44v6), CD95/Fas, Fas-L, p53 and p-glycoprotein. All the parameters were analyzed for the influence on the occurrence of death and metastasis, plus patient overall survival (OS) and progression-free survival (PFS). The effect of sex, age, tumor location (distal femur or proximal tibia) and the combination with neoadjuvant chemotherapy was also assessed. Our results suggest that the presence of tumor-infiltrating CD4+ cells provides protection to OST patients, and that CD8+ cells have a significant impact on the patient’s overall survival (OS) and progression-free survival (PFS), which is more evident in male patients. In addition, a strong association between tumor-infiltrating CD4+ cells and the presence of CD44s expression in tumor samples was observed. Analysis of TIL and tumor markers related to tumor biology could be useful to stratify patients and monitor the response to therapy, as well as to assist with the development of immunotherapy strategies to improve the effects of cytotoxic TIL to eradicate the tumor cells.