Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

BACKGROUND

The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited.

METHODS

We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared.

RESULTS

The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group.

CONCLUSIONS

This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Background

Multiple myeloma (MM), an incurable plasma cell malignancy. The significance of the relationship between natural killer (NK) cell-related genes and clinical factors in MM remains unclear.

Methods

Initially, we extracted NK cell-related genes from peripheral blood mononuclear cells (PBMC) of healthy donors and MM samples by employing single-cell transcriptome data analysis in TISCH2. Subsequently, we screened NK cell-related genes with prognostic significance through univariate Cox regression analysis and protein-protein interaction (PPI) network analysis. Following the initial analyses, we developed potential subtypes and prognostic models for MM using consensus clustering and lasso regression analysis. Additionally, we conducted a correlation analysis to explore the relationship between clinical features and risk scores. Finally, we constructed a weighted gene co-expression network analysis (WGCNA) and identified differentially expressed genes (DEGs) within the MM cohort.

Results

We discovered that 153 NK cell-related genes were significantly associated with the prognosisof MM patients (P <0.05). Patients in NK cluster A exhibited poorer survival outcomes compared to those in cluster B. Furthermore, our NK cell-related genes risk model revealed that patients with a high risk score had significantly worse prognoses (P <0.05). Patients with a high risk score were more likely to exhibit adverse clinical markers. Additionally, the nomogram based on NK cell-related genes demonstrated strong prognostic performance. The enrichment analysis indicated that immune-related pathways were significantly correlated with both the NK subtypes and the NK cell-related genes risk model. Ultimately, through the combined use of WGCNA and DEGs analysis, and by employing Venn diagrams, we determined that ITM2C is an independent prognostic marker for MM patients.

Conclusion

In this study, we developed a novel model based on NK cell-related genes to stratify the prognosis of MM patients. Notably, higher expression levels of ITM2C were associated with more favorable survival outcomes in these patients.

Circadian rhythm-associated lncRNA RP11-414H17.5 as a key therapeutic target in osteosarcoma affects the tumor immune microenvironment and enhances malignancy

BACKGROUND

It has previously been proven that circadian rhythm disruption is associated with the incidence and deterioration of several tumors, which potentially leads to increased tumor susceptibility and a worse prognosis for tumor-bearing patients. However, their potential role in osteosarcoma has yet to be sufficiently investigated.

METHODS

Transcriptomic and clinical data of 84 osteosarcoma samples and 70 normal bone tissue samples were obtained from the TARGET and GTEx databases, circadian rhythm-related genes were obtained from Genecards, and circadian rhythm-related lncRNAs (CRLs) were obtained by Pearson correlation analysis, differential expression analysis, and protein-protein interaction (PPI) analysis. COX regression and LASSO regression were performed on the CRLs in order to construct a circadian rhythm-related prognostic prediction signature (CRPS). CRPS reliability was verified by Kaplan-Meier (KM), principal component analysis (PCA), nomogram, and receiver operating characteristic (ROC) curve. CRPS effects on the immune microenvironment of osteosarcoma were explored by enrichment analysis and immune infiltration analysis, and the effect of critical gene RP11-414H17.5 on osteosarcoma was experimentally verified.

RESULT

CRPS consisting of three CRLs was constructed and its area under the curve (AUC) values predicted that osteosarcoma prognosis reached 0.892 in the training group and 0.843 in the test group, with a p value of < 0.05 for the KM curve and stable performance across different clinical subgroups. PCA analysis found that CRPS could significantly distinguish between different risk subgroups, and exhibited excellent performance in the prediction of the immune microenvironment. The experiment verified that RP11-414H17.5 can promote metastasis and inhibit apoptosis of osteosarcoma cells.

CONCLUSION

The study revealed that circadian rhythm plays a crucial role in osteosarcoma progression and identified the impact of the key gene RP11-414H17.5 on osteosarcoma, which provides novel insights into osteosarcoma diagnosis and therapy.