A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

USP7-mediated JUND suppresses RCAN2 transcription and elevates NFATC1 to enhance stem cell property in colorectal cancer

Cancer stem cells (CSCs) encompass a subset of highly aggressive tumor cells that are involved in tumor initiation and progression. This study investigates the function of regulator of calcineurin 2 (RCAN2) in the stem cell property in colorectal cancer (CRC). By analyzing four GEO datasets, we obtained RCAN2 as a stemness-related gene in CRC. RCAN2 was poorly expressed in CRC tissues and cells, especially in CSCs. RCAN2 restoration reduced calcineurin activity and promoted phosphorylation and degradation of nuclear factor of activated T cells 1 (NFATC1) protein, leading to reduced stemness of CSCs. JunD proto-oncogene (JUND), whose protein level was increased in CRC samples and CRC stem cells, bound to RCAN2 and suppressed its transcription. The abundant ubiquitin specific peptidase 7 (USP7) in CSCs enhanced JUND protein stability through deubiquitination modification. Lentivirus-mediated knockdown of USP7 or JUND also blocked the calcineurin-NFATC1 signaling and reduced the protein levels of stemness-related proteins. Moreover, the USP7 knockdown weakened the colony/sphere formation ability as well as the tumorigenicity of CSCs, and it reduced the CSC content in xenograft tumors. However, further restoration of JUND rescued the stemness of the CSCs. Overall, this study demonstrates that USP7-mediated JUND suppresses RCAN2 transcription and activates NFATC1 to enhance stem cell property in CRC. 1. RCAN2 is poorly expressed in CRC tissues and cells and especially in CSCs. 2. RCAN2 reduces stemness of CSCs by blocking calcineurin-NFATC1 signal transduction. 3. JUND binds to RCAN2 promoter to suppresses RCAN2 transcription. 4. USP7 enhances JUND protein stability via deubiquitination modification. 5. Downregulation of USP7 or JUND restores RCAN2 level and suppresses stemness of CSCs.

Stemness genes and miR-1247-3p expression associate with clinicopathological parameters and prognosis in lung adenocarcinoma

Lung cancer makes up one-fourth of all cancer-related mortality with the highest mortality rate among all cancers. Despite recent scientific advancements in cancer therapeutics, the 5-year survival rate of lung adenocarcinoma (LUAD) cancer patients remains below 15 percent. It has been suggested that the high mortality rate of LUAD is linked to the acquisition of progenitor-like cells with stem-like characteristics that assist the whole tumor in regulating immune cell infiltration. To examine this hypothesis further, this study mined several databases to explore the presence of stemness-related genes and miRNAs in LUAD cancers. We examine their association with immune and accessory cell infiltration rates and patient survival. We found 3 stem cell-related genes, ORC1L, KIF20A, and DLGAP5, present in LUAD that also correlate with changes in immune infiltration rates and reduced patient survival rates. Additionally, the modulation in myeloid-derived suppressor cell (MDSC) infiltration and miRNA hsa-mir-1247-3p mediated targeting of tumor suppressor SLC24A4 and oncogenes RAB3B and HJURP appears to primarily regulate LUAD patient survival. Given these findings, hsa-mir-1247-3p and/or its associated gene targets may offer a promising avenue to enhance patient survivability.

New gene signature from the dominant infiltration immune cell type in osteosarcoma predicts overall survival

The immune microenvironment of osteosarcoma (OS) has been reported to play an important role in disease progression and prognosis. However, owing to tumor heterogeneity, it is not ideal to predict OS prognosis by examining only infiltrating immune cells. This work aimed to build a prognostic gene signature based on similarities in the immune microenvironments of OS patients. Public datasets were used to examine the correlated genes, and the most consistent dominant infiltrating immune cell type was identified. The LASSO Cox regression model was used to establish a multiple-gene risk prediction signature. A nine-gene prognostic signature was generated from the correlated genes for M0 macrophages and then proven to be effective and reliable in validation cohorts. Signature comparison indicated the priority of the signature. Multivariate Cox regression models indicated that the signature risk score is an independent prognostic factor for OS patients regardless of the Huvos grade in all datasets. In addition, the results of the association between the signature risk score and chemotherapy sensitivity also showed that there was no significant difference in the sensitivity of any drugs between the low- and high-risk groups. A GSEA of GO and KEGG pathways found that antigen processing- and presentation-related biological functions and olfactory transduction receptor signaling pathways have important roles in signature functioning. Our findings showed that M0 macrophages were the dominant infiltrating immune cell type in OS and that the new gene signature is a promising prognostic model for OS patients.

Ferroptosis in osteosarcoma: A promising future

The incidence of osteosarcoma (OS) is increasing year by year, and the prognosis of patients with advanced OS is extremely poor due to the tendency of recurrence and chemotherapy resistance after surgery. Ferroptosis is a novel form of programmed cell death (PCD) that kills cells through iron-dependent lipid peroxidation. Current studies have shown that ferroptosis is closely related to OS and could reduce chemotherapy resistance to a certain extent, which has great therapeutic potential. In this paper, we review the regulatory mechanism of ferroptosis and its research progress in OS, hoping to provide new help for the clinical treatment of OS.

Osteosarcoma subtypes based on platelet-related genes and tumor microenvironment characteristics

Background

Osteosarcoma is a common metastatic tumor in children and adolescents. Because of its easy metastasis, patients often show a poor prognosis. Recently, researchers have found that platelets are closely related to metastasis of a variety of malignant tumors, but the role of platelets related characteristics in osteosarcoma is still unknown. The purpose of this study is to explore the characteristics of platelet-related subtypes and cell infiltration in tumor microenvironment.

Methods

We collected osteosarcoma cohorts from TCGA and GEO databases, and explored the molecular subtypes mediated by platelet-related genes and the related TME cell infiltration according to the expression of platelet-related genes in osteosarcoma. In addition, we also explored the differentially expressed genes (DEGs) among different molecular subtypes and established a protein-protein interaction network (PPI). Then we constructed a platelet scoring model by Univariate cox regression and least absolute shrinkage and selection operator (Lasso) cox regression model to quantify the characteristics of platelet in a single tumor. RT-PCR was used to investigate the expression of six candidate genes in osteosarcoma cell lines and normal osteoblast lines. Finally, we also predicted potential drugs with therapeutic effects on platelet-related subtypes.

Results

We found that platelet-related genes (PRGs) can distinguish osteosarcoma into two different platelet-related subtypes, C1 and C2. And the prognosis of the C2 subtype was significantly worse than that of C1 subtype. The results of ESTIMATE analysis and GO/KEGG enrichment showed that the differences between different subtypes were mainly concentrated in immune response pathways, and the immune response of C2 was inhibited relative to C1. We further studied the relationship between platelet-related subtypes and immune cell infiltration. We found that the distribution of most immune cells in C1 subtype was higher than that in C2 subtype, and there was a correlation between C1 subtype and more immune cells. Finally, we screened the PRGs related to the prognosis of osteosarcoma through Univariate Cox regression, established independent prognostic platelet characteristics consisting of six genes to predict the prognosis of patients with OS, and predicted the drugs that may be used in the treatment of osteosarcoma. RT-PCR was used to verify the expression of candidate genes in osteosarcoma cells.

Conclusion

Platelet scoring model is a significant biomarker, which is of great significance to determine the prognosis, molecular subtypes, characteristics of TME cell infiltration and therapy in patients with OS.

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.