A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma

Harnessing multi‑omics to revolutionize understanding and management of osteosarcoma: A pathway to precision medicine (Review)

Osteosarcoma, the most prevalent primary bone malignancy in children and adolescents, poses significant challenges due to its aggressive nature and propensity for metastasis. Despite advances in treatment, survival rates for high‑risk patients remain unsatisfactory, underscoring the urgent need for innovative approaches. This review explores the vital role of multi‑omics‑integrating genomics, transcriptomics, proteomics and metabolomics‑in unraveling the complex biological landscapes of osteosarcoma. By providing comprehensive insights into tumor heterogeneity, signaling pathways and metabolic reprogramming, multi‑omics facilitates the identification of novel biomarkers and therapeutic targets. The objective of the present study was to highlight the transformative potential of multi‑omics in enhancing the understanding and management of osteosarcoma, ultimately paving the way for personalized treatment strategies and improved patient outcomes. Through this synthesis, the study calls for a concerted effort to integrate multi‑omics into clinical practice, fostering a more precise approach to osteosarcoma care.

Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constructs

Background

Hepatocellular carcinoma (HCC) is the leading cause of tumor-related mortality worldwide. There is an urgent need for predictive biomarkers to guide treatment decisions. This study aimed to identify robust prognostic genes for HCC and to establish a theoretical foundation for clinical interventions.

Methods

The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. Immune infiltration and drug sensitivity analyses were conducted to assess the impact of these genes and to explore potential chemotherapeutic agents for HCC. Additionally, single-cell analysis was employed to identify key cellular players and their interactions within the tumor microenvironment. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was utilized to validate the roles of these prognostic genes in HCC.

Results

A total of eight prognostic genes were identified (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4). The risk model, constructed based on these genes, was effective in predicting survival outcomes for HCC patients. CEP55 exhibited the strongest positive correlation with activated CD4 T cells. The top 10 drugs showed increased sensitivity in the low-risk group. B cells were identified as key cellular components with the highest interaction numbers and strengths with macrophages in both HCC and control groups. Prognostic genes were more highly expressed in the initial state of B cell differentiation. RT-qPCR confirmed significant upregulation of MCM10, KIF18A, CDC45, and PLK4 in HCC tissues (p< 0.05).

Conclusion

This study successfully identified eight prognostic genes (MCM10, CEP55, KIF18A, ORC6, KIF23, CDC45, CDT1, and PLK4), which provided new directions for exploring the potential pathogenesis and clinical treatment research of HCC.

Multi-cohort validation based on a novel prognostic signature of anoikis for predicting prognosis and immunotherapy response of esophageal squamous cell carcinoma

Immunotherapy is recognized as an effective and promising treatment modality that offers a new approach to cancer treatment. However, identifying responsive patients remains challenging. Anoikis, a distinct form of programmed cell death, plays a crucial role in cancer progression and metastasis. Thus, we aimed to investigate prognostic biomarkers based on anoikis and their role in guiding immunotherapy decisions for esophageal squamous cell carcinoma (ESCC). By consensus clustering, the GSE53624 cohort of ESCC patients was divided into two subgroups based on prognostic anoikis-related genes (ARGs), with significant differences in survival outcomes between the two subgroups. Subsequently, we constructed an ARGs signature with four genes, and its reliability and accuracy were validated both internally and externally. Additional, different risk groups showed notable variances in terms of immunotherapy response, tumor infiltration, functional enrichment, immune function, and tumor mutation burden. Notably, the effectiveness of the signature in predicting immunotherapy response was confirmed across multiple cohorts, including GSE53624, GSE53625, TCGA-ESCC, and IMvigor210, highlighting its potential utility in predicting immunotherapy response. In conclusion, the ARGs signature has the potential to serve as an innovative and dependable prognostic biomarker for ESCC, facilitating personalized treatment strategies in this field, and may represent a valuable new tool for guiding ESCC immunotherapy decision-making.

Integrated transcriptomic analysis systematically reveals the heterogeneity and molecular characterization of cancer-associated fibroblasts in osteosarcoma

BACKGROUND

Osteosarcoma (OS), with a peak incidence during the adolescent growth spurt, is correlated with poor prognosis for its high malignancy. The tumor microenvironment (TME) is highly complicated, with frequent interactions between tumor and stromal cells. The cancer-associated fibroblasts (CAFs) in the TME have been considered to actively involve in the progression, metastasis, and drug resistance of OS. This study aimed to characterize cellular heterogeneity and molecular characterization in CAFs subtypes and explore the potential targeting therapeutic strategies to improve the prognosis of OS patients.

METHODS

The single-cell atlas of human OS tumor lesions were constructed from the GEO database. Then significant marker genes and potential biological functions for each CAFs subtype were identified and explored using the Seurat R package. Next, by performing the survival analyses and constructing the risk scores for CAFs subtypes, we aimed to identify and characterize the prognostic values of specific marker genes and different CAFs subtypes. Furthermore, we explored the therapeutic targets and innovative drugs targeting different CAFs subtypes based on the GDSC database. Finally, prognoses related CAFs subtypes were further validated through immunohistochemistry (IHC) on clinical OS specimens.

RESULTS

Overall, nine main cell clusters and five subtypes of CAFs were identified. The differentially expressed marker genes for each CAFs clusters were then identified. Moreover, through Gene Ontology (GO) enrichment analysis, we defined the CAFs_2 (upregulated CXCL14 and C3), which was closely related to leukocyte migration and chemotaxis, as inflammatory CAFs (iCAFs). Likewise, we defined the CAFs_4 (upregulated CD74, HLA-DRA and HLA-DRB1), which was closely related to antigen process and presentation, as antigen-presenting CAFs (apCAFs). Furthermore, Kaplan-Meier analyses showed that CAFs_2 and CAFs_4 were correlated with poor clinical prognosis of OS patients. Meanwhile, therapeutic drugs targeting CAFs_2 and CAFs_4, such as 17-AAG/Docetaxel/Bleomycin and PHA-793887/NG-25/KIN001-102, were also explored, respectively. Finally, IHC assay confirmed the abundant CAFs_2 and CAFs_4 subtypes infiltration in the OS microenvironment compared with adjacent tissues.

CONCLUSION

Our study revealed the diversity, complexity, and heterogeneity of CAFs in OS, and complemented the single-cell atlas in OS TME.

Development of an anoikis-related gene signature and prognostic model for predicting the tumor microenvironment and response to immunotherapy in colorectal cancer

The effect of anoikis-related genes (ARGs) on clinicopathological characteristics and tumor microenvironment remains unclear. We comprehensively analyzed anoikis-associated gene signatures of 1057 colorectal cancer (CRC) samples based on 18 ARGs. Anoikis-related molecular subtypes and gene features were identified through consensus clustering analysis. The biological functions and immune cell infiltration were assessed using the GSVA and ssGSEA algorithms. Prognostic risk score was constructed using multivariate Cox regression analysis. The immunological features of high-risk and low-risk groups were compared. Finally, DAPK2-overexpressing plasmid was transfected to measure its effect on tumor proliferation and metastasis in vitro and in vivo. We identified 18 prognostic ARGs. Three different subtypes of anoikis were identified and demonstrated to be linked to distinct biological processes and prognosis. Then, a risk score model was constructed and identified as an independent prognostic factor. Compared to the high-risk group, patients in the low-risk group exhibited longer survival, higher enrichment of checkpoint function, increased expression of CTLA4 and PD-L1, higher IPS scores, and a higher proportion of MSI-H. The results of RT-PCR indicated that the expression of DAPK2 mRNA was significantly downregulated in CRC tissues compared to normal tissues. Increased DAPK2 expression significantly suppressed cell proliferation, promoted apoptosis, and inhibited migration and invasion. The nude mice xenograft tumor model confirmed that high expression of DAPK2 inhibited tumor growth. Collectively, we discovered an innovative anoikis-related gene signature associated with prognosis and TME. Besides, our study indicated that DAPK2 can serve as a promising therapeutic target for inhibiting the growth and metastasis of CRC.