Discovery of a proliferation essential gene signature and actin-like 6A as potential biomarkers for predicting prognosis and neoadjuvant chemotherapy response in triple-positive breast cancer

Unraveling the Role of Programmed Cell Death Gene Signature and THBS1 in Gastric Cancer Progression and Therapy Response

BACKGROUND

Programmed cell death (PCD) genes play crucial roles in cancer progression and response to therapies, yet their impact on gastric cancer remains inadequately elucidated. This study aimed to create a prognostic cell death signature (PCDs) for gastric cancer, providing insights into potential therapeutic targets and survival predictors.

METHODS

We utilized TCGA-STAD and five GEO datasets, representing thousands of gastric cancer samples, for a comprehensive analysis of PCD genes. Differential gene expression, functional enrichment, survival, and machine learning analyses were conducted to construct a PCD-based prognostic model.

RESULTS

A total of 249 differentially expressed PCD genes were identified between cancerous and noncancerous gastric tissues. Subsequently, a PCD signature based on seven genes was developed and cross-validated across multiple cohorts. The high-PCD subtype correlated with poorer survival outcomes, lower tumor mutational burden, higher infiltration of M2 macrophages, lower levels of immune checkpoint expression, and decreased response to immunotherapy. A nomogram incorporating the PCDs provided accurate survival rate predictions. Additionally, nine machine learning algorithms were implemented for recurrence prediction, with the random forest model displaying high effectiveness. In this model, thrombospondin 1 (THBS1) showed the highest weight, and its knockdown significantly reduced gastric cancer cell proliferation and invasion.

CONCLUSION

This study underscores the significance of PCD genes, particularly THBS1, in gastric cancer progression and highlights their value as potential therapeutic targets. The predictive models developed here can aid in assessing patient prognosis and tailoring personalized treatment strategies.

Deciphering a proliferation-essential gene signature based on CRISPR-Cas9 screening to predict prognosis and characterize the immune microenvironment in HNSCC

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with a poor prognosis. Identifying reliable prognostic biomarkers and therapeutic targets is crucial for improving patient outcomes. This study aimed to systematically identify proliferation-essential genes (PEGs) associated with HNSCC prognosis using CRISPR-Cas9 screening data.

METHODS

CRISPR-Cas9 screening data from the DepMap database were used to identify PEGs in HNSCC cells. A prognostic PEGs signature was constructed using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox regression analyses. The predictive accuracy of the signature was validated in internal and external datasets. Weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and immune infiltration analysis were used to investigate the underlying mechanism between high and low-risk patients. Random forest analysis and functional experiments were conducted to investigate the role of key proliferation essential genes in HNSCC progression.

RESULTS

A total of 1511 PEGs were identified. A seven-gene prognostic PEGs signature (MRPL33, NAT10, PSMC1, PSMD11, RPN2, TAF7, and ZNF335) was developed and validated, demonstrating robust prognostic performance in stratifying HNSCC patients by survival risk. WGCNA and GSEA analyses revealed a marked downregulation of immune-related pathways in high-risk patients. Immune infiltration analysis validated those high-risk patients had reduced immune scores, stromal scores, and ESTIMATE scores, as well as decreased infiltration of multiple immune cell types. Among the identified genes, PSMC1 was highlighted as a pivotal regulator of HNSCC proliferation and migration, as confirmed by functional experiments.

CONCLUSIONS

This study identifies a novel PEGs signature that effectively predicts HNSCC prognosis and stratifies patients by survival risk. PSMC1 was identified as a key gene promoting malignant progression, offering potential as a therapeutic target for HNSCC.

Predictive model using systemic inflammation markers to assess neoadjuvant chemotherapy efficacy in breast cancer

Background

Pathological complete response (pCR) is an important indicator for evaluating the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. The role of systemic inflammation markers in predicting pCR and the long-term prognosis of breast cancer patients undergoing NAC remains controversial. The purpose of this study was to explore the potential predictive and prognostic value of systemic inflammation markers (NLR, PLR, LMR, NMR) and clinicopathological characteristics in breast cancer patients receiving NAC and construct a pCR prediction model based on these indicators.

Methods

A retrospective analysis was conducted on 209 breast cancer patients who received NAC at Nanjing Drum Tower Hospital between January 2010 and March 2020. Independent sample t-tests, chi-square tests, and logistic regression models were used to evaluate the correlation between clinicopathological data, systemic inflammation markers, and pCR. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for NLR, PLR, and LMR. Survival analysis was performed using the Kaplan-Meier method and log-rank test. A predictive model for pCR was constructed using machine learning algorithms.

Results

Among the 209 breast cancer patients, 29 achieved pCR. During a median follow-up of 68 months, 74 patients experienced local or distant metastasis, and 56 patients died. Univariate logistic regression analysis showed that lymph node status, HER-2 status, NLR, PLR, and LMR were associated with pCR. ROC curve analysis revealed that the optimal cut-off values for NLR, PLR, and LMR were 1.525, 113.620, and 6.225, respectively. Multivariate logistic regression analysis indicated that lymph node status, NLR, and LMR were independent predictive factors for pCR. Survival analysis demonstrated that lymph node status, NLR, and LMR were associated with prognosis. Machine learning algorithm analysis identified the random forest (RF) model as the optimal predictive model for pCR.

Conclusion

This study demonstrated that lymph node status, NLR, and LMR had significant value in predicting pCR and prognosis in breast cancer patients. The RF model provides a simple and cost-effective tool for pCR prediction, offering strong support for clinical decision-making in breast cancer treatment and aiding in the optimization of individualized treatment strategies.

Triple-positive breast cancer: navigating heterogeneity and advancing multimodal therapies for improving patient outcomes

Triple-positive breast cancer (TPBC), a unique subtype of luminal breast cancer, is characterized by concurrent positivity for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Owing to the crosstalk between the ER and HER2 signaling pathways, the standard of care and drug resistance of this particular subtype are difficult challenges. Recent research and clinical trials have indicated a shift in the treatment paradigm for TPBC from single-target therapies to multi-pathway, multitarget strategies aiming to comprehensively modulate intricate signaling networks, thereby overcoming resistance and enhancing therapeutic outcomes. Among multiple strategies, triple-drug therapy has emerged as a promising treatment modality, demonstrating potential efficacy in patients with TPBC. Moving forward, there is a critical need to perform in-depth analyses of specific mechanisms of cancer pathogenesis and metastasis, decipher the complex interactions between different genes or proteins, and identify concrete molecular targets, thus paving the way for the development of tailored therapeutic strategies to combat TPBC effectively.

Breast cancer therapy in China: Introducing the Special Collection

This special issue indirectly reflects the medical level and research hotspots of breast cancer experts in China in recent years as well as the treatment level and disease outcome of patients with breast cancer in China, allowing readers to appreciate the achievements made in the field of breast cancer in China in recent years and to identify the gaps in the international field.