PLCE1 is a poor prognostic marker and may promote immune escape from osteosarcoma by the CD70-CD27 signaling pathway

Development and validation of neutrophil extracellular traps-derived signature to predict the prognosis for osteosarcoma patients

Neutrophil extracellular traps (NETs) have been reported to be crucial in tumorigenesis and malignant progression. However, their prognostic significance, association with tumor immune microenvironment (TIME), and therapeutic response in osteosarcoma (OS) stills remain unclear. Hence, TARGET and GSE21257 cohorts were included for analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were conducted to extract NETs-derived genes. Subsequently, the NETs score (NETScore) model, consisting of 4 signature genes, was established and validated with the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. Our results indicated that NETScore has satisfactory predictability of the patient's overall survival, with AUC values at 1-, 3- and 5-year in the training cohort of 0.798, 0.792 and 0.804, respectively; similar prominent prediction performance was obtained in three validation cohorts. Further, real-time quantitative PCR (RT-qPCR) assay was conducted to determine the expression of signature genes in human osteoblasts and OS cells. Besides, NETScore and clinical factors (age, gender, metastatic status) were integrated to construct a nomogram. C-index and AUC values at 1-, 3-, and 5-year were above 0.800, displaying robust predictive performance. Patients with high and low NETScore had different immune statuses and drug sensitivity. Meanwhile, several positive regulatory immune function pathways, including T cell proliferation, activation and migration, were significantly suppressed among patients with high NETScore. Summarily, we established a novel NETScore that can accurately predict OS patients' prognosis, which correlated closely with the immune landscape and therapeutic response and might help to guide clinical decision-making.

Jun-APOE-LRP1 axis promotes tumor metastasis in colorectal cancer

Apolipoprotein E (apoE) has previously been reported to play vital roles in tumor progression. However, the impact of apoE on colorectal cancer (CRC) metastasis remains largely unexplored. This study aimed to investigate the role of apoE in CRC metastasis and to identify the transcription factor and receptor of apoE involved in regulation of CRC metastasis. Bioinformatic analyses were conducted to examine the expression pattern and prognosis of apolipoproteins. APOE-overexpressing cell lines were utilized to explore the effects of apoE on proliferation, migration and invasion of CRC cells. Additionally, the transcription factor and receptor of apoE were screened via bioinformatics, and further validated through knockdown experiments. We discovered that the mRNA levels of APOC1, APOC2, APOD and APOE were higher in lymphatic invasion group, and a higher apoE level indicated poorer overall survival and progression-free interval. In vitro studies demonstrated that APOE-overexpression did not affect proliferation but promoted the migration and invasion of CRC cells. We also reported that APOE-expression was modulated by the transcription factor Jun by activating the proximal promoter region of APOE, and APOE-overexpression reversed the metastasis suppression of JUN knockdown. Furthermore, bioinformatics analysis suggested an interaction between apoE and low-density lipoprotein receptor-related protein 1 (LRP1). LRP1 was highly expressed in both the lymphatic invasion group and the APOEHigh group. Additionally, we found that APOE-overexpression upregulated LRP1 protein levels, and LRP1 knockdown attenuated the metastasis-promoting function of APOE. Overall, our study suggests that the Jun-APOE-LRP1 axis contributes to tumor metastasis in CRC.

Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

OBJECTIVE

To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management.

METHODS

Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group.

RESULTS

We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4.

CONCLUSION

The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.

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.

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma. Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates. Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils. Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients.