Development of an immune-related signature for predicting survival outcome and immunotherapy response in osteosarcoma

Wang, Qinghua; Zhang, Wenjing; Guo, Yuxian; Li, Yuting; Fu, Kaifeng

doi:10.18632/aging.203671

Cited by 3 publications

(4 citation statements)

References 34 publications

(42 reference statements)

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“…The immune, stromal and ESTIMATE scores were significantly abundant in the high-risk group by tumor immune infiltration analysis. Interestingly, this result is contrary to the conclusion that the enrichment of immune infiltrating cells in some tumors indicates a good prognosis 38 , 39 . However, it has been clarified that the immune system can both inhibit tumor growth and promote tumor development by changing tumor immunogenicity or promoting immunosuppressive state 40 .…”

Section: Discussioncontrasting

confidence: 94%

Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

Zhou

et al. 2023

Sci Rep

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In recent years, RNA methylation modification has been found to be related to a variety of tumor mechanisms, such as rectal cancer. Clear cell renal cell carcinoma (ccRCC) is most common in renal cell carcinoma. In this study, we get the RNA profiles of ccRCC patients from ArrayExpress and TCGA databases. The prognosis model of ccRCC was developed by the least absolute shrinkage and selection operator (LASSO) regression analysis, and the samples were stratified into low–high risk groups. In addition, our prognostic model was validated through the receiver operating characteristic curve (ROC). “pRRophetic” package screened five potential small molecule drugs. Protein interaction networks explore tumor driving factors and drug targeting factors. Finally, polymerase chain reaction (PCR) was used to verify the expression of the model in the ccRCC cell line. The mRNA matrix in ArrayExpress and TCGA databases was used to establish a prognostic model for ccRCC through LASSO regression analysis. Kaplan Meier analysis showed that the overall survival rate (OS) of the high-risk group was poor. ROC verifies the reliability of our model. Functional enrichment analysis showed that there was a obviously difference in immune status between the high-low risk groups. “pRRophetic” package screened five potential small molecule drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706). Protein interaction network shows that epidermal growth factor receptor [EGRF] and estrogen receptor 1 [ESR1] are tumor drivers and drug targeting factors. To further analyze the differential expression and pathway correlation of the prognosis risk model species. Finally, polymerase chain reaction (PCR) showed the expression of YTHN6-Methyladenosine RNA Binding Protein 1[YTHDF1], TRNA Methyltransferase 61B [TRMT61B], TRNA Methyltransferase 10C [TRMT10C] and AlkB Homolog 1[ALKBH1] in ccRCC cell lines. To sum up, the prognosis risk model we created not only has good predictive value, but also can provide guidance for accurately predicting the prognosis of ccRCC.

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Section: Discussioncontrasting

confidence: 94%

Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

Zhou

et al. 2023

Sci Rep

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“…Interdisciplinary analyses based on large amounts of sequencing data and diverse bioinformatics algorithms are increasingly being used for the identification of tumor subtypes, providing a valuable reference in the discovery of potential biomarkers [40][41][42][43]. In this study, a combined analysis of single-cells and bulk RNA sequencing was performed to construct a CAFRss comprising 11 CAFsrelated genes.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing

Bao,

Zhang,

Wang

et al. 2023

Aging

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Background: Cancer-associated fibroblasts (CAFs) regulate the malignant biological behaviour of hepatocellular carcinoma (HCC) as a significant component of the tumour immune microenvironment (TIME). This study aimed to develop a CAFs-based scoring system to predict the prognosis and TIME of patients with HCC. Methods: Data for the TCGA-LIHC and GSE14520 cohorts were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Single-cell RNA-sequencing data for HCC samples were retrieved from the GSE166635 cohort. The Least Absolute Shrinkage and Selection Operator algorithm was employed to develop a CAFs-related scoring system (CAFRss). The predictive value of the CAFRss was determined using Kaplan-Meier, Cox regression and Receiver Operating Characteristic curves. Additionally, the TIMER platform, single sample Gene Set Enrichment Analysis and the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data algorithms were performed to determine the TIME landscape. Finally, the pRRophic algorithm was utilised for drug sensitivity analysis. Results: The evaluation of the CAFRss system demonstrated its superior ability to predict the clinical outcome of patients with HCC. Additionally, CAFRss effectively distinguished HCC populations with distinct TIME landscapes. Furthermore, CAFRss-based risk stratification identified individuals with immune ‘hot tumours’ and predicted the survival of patients treated with ICBs. Conclusions: The developed CAFRss can serve as a predictive tool for determining the clinical outcome of HCC and differentiating populations with diverse TIME characteristics.

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“…Other treatments, like immunotherapy, show significantly improved response rates and survival outcomes for low‐risk patients. However, the efficacy of immunotherapy remains uncertain and is less effective for high‐risk, recurrent, and metastatic patients 14 . Additionally, research on targeted drug delivery using nanocarriers shows promise, but the specific tumor microenvironment and unclear targets pose challenges, hindering the use of emerging targeted therapies in OS treatment 15,16 .…”

Section: Conventional Treatment For Osmentioning

confidence: 99%

“…However, the efficacy of immunotherapy remains uncertain and is less effective for high-risk, recurrent, and metastatic patients. 14 Additionally, research on targeted drug delivery using nanocarriers shows promise, but the specific tumor microenvironment and unclear targets pose challenges, hindering the use of emerging targeted therapies in OS treatment. 15,16 Therefore, while conventional treatment methods have some effectiveness in treating OS, their inherent limitations necessitate the urgent development of alternative treatment approaches to overcome these limitations.…”

mentioning

confidence: 99%

Ferroptosis defense mechanisms: The future and hope for treating osteosarcoma

Ma,

Cong,

Shen

et al. 2024

Cell Biochemistry & Function

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Currently, challenges such as chemotherapy resistance, resulting from preoperative and postoperative chemotherapy, postoperative recurrence, and poor bone regeneration quality, are becoming increasingly prominent in osteosarcoma (OS) treatment. There is an urgent need to find more effective ways to address these issues. Ferroptosis is a novel form of iron‐dependent programmed cell death, distinct from other forms of cell death. In this paper, we summarize how, through the three major defense systems of ferroptosis, not only can substances from traditional Chinese medicine, antitumor drugs, and nano‐drug carriers induce ferroptosis in OS cells, but they can also be combined with immunotherapy, differentiation therapy, and other treatment modalities to significantly enhance chemotherapy sensitivity and inhibit tumor growth. Thus, ferroptosis holds great potential in treating OS, offering more choices and possibilities for future clinical interventions.

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Development of an immune-related signature for predicting survival outcome and immunotherapy response in osteosarcoma

Cited by 3 publications

References 34 publications

Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

Identification and validation of a cancer-associated fibroblasts-related scoring system to predict prognosis and immune landscape in hepatocellular carcinoma through integrated analysis of single-cell and bulk RNA-sequencing

Ferroptosis defense mechanisms: The future and hope for treating osteosarcoma

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