Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer

The role of Phafin proteins in cell signaling pathways and diseases

Membrane-associated proteins are important membrane readers that mediate and facilitate the signaling and trafficking pathways in eukaryotic membrane-bound compartments. The protein members in the Phafin family are membrane readers containing two phosphoinositide recognition domains: the Pleckstrin Homology domain and the FYVE (Fab1, YOTB, Vac1, and early endosome antigen 1) domain. Phafin proteins, categorized into two subfamilies, Phafin1 and Phafin2, associate with cellular membranes through interactions involving membrane-embedded phosphoinositides and phosphoinositide-binding domains. These membrane-associated Phafin proteins play pivotal roles by recruiting binding partners and forming complexes, which contribute significantly to apoptotic, autophagic, and macropinocytotic pathways. Elevated expression levels of Phafin1 and Phafin2 are observed in various cancers. A recent study highlights a significant increase in Phafin1 protein levels in the lungs of idiopathic pulmonary fibrosis patients compared to normal subjects, suggesting a crucial role for Phafin1 in the pathogenesis of pulmonary fibrosis. Additionally, phosphatidylinositol-3-phosphate-binding 2 (Pib2), a close relative of the Phafin1 protein, functions as an amino acid sensor activating the TOCR1 pathway in yeasts. This review focuses on delineating the involvement of Phafin proteins in cellular signaling and their implications in diseases and briefly discusses the latest research findings concerning Pib2.

Local administration of liposomal-based Plekhf1 gene therapy attenuates pulmonary fibrosis by modulating macrophage polarization

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease with limited therapeutic options. Macrophages, particularly alternatively activated macrophages (M2), have been recognized to contribute to the pathogenesis of pulmonary fibrosis. Therefore, targeting macrophages might be a viable therapeutic strategy for IPF. Herein, we report a potential nanomedicine-based gene therapy for IPF by modulating macrophage M2 activation. In this study, we illustrated that the levels of pleckstrin homology and FYVE domain containing 1 (Plekhf1) were increased in the lungs originating from IPF patients and PF mice. Further functionality studies identified the pivotal role of Plekhf1 in macrophage M2 activation. Mechanistically, Plekhf1 was upregulated by IL-4/IL-13 stimulation, after which Plekhf1 enhanced PI3K/Akt signaling to promote the macrophage M2 program and exacerbate pulmonary fibrosis. Therefore, intratracheal administration of Plekhf1 siRNA-loaded liposomes could effectively suppress the expression of Plekhf1 in the lungs and notably protect mice against BLM-induced lung injury and fibrosis, concomitant with a significant reduction in M2 macrophage accumulation in the lungs. In conclusion, Plekhf1 may play a crucial role in the pathogenesis of pulmonary fibrosis, and Plekhf1 siRNA-loaded liposomes might be a promising therapeutic approach against pulmonary fibrosis.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

Molecular profiling of osteoprogenitor cells reveals FOS as a master regulator of bone non-union

Despite the advances in bone fracture treatment, a significant fraction of fracture patients will develop non-union. Most non-unions are treated with surgery since identifying the molecular causes of these defects is exceptionally challenging. In this study, compared with marrow bone, we generated a transcriptional atlas of human osteoprogenitor cells derived from healing callus and non-union fractures. Detailed comparison among the three conditions revealed a substantial similarity of callus and nonunion at the gene expression level. Nevertheless, when assayed functionally, they showed different osteogenic potential. Utilizing longitudinal transcriptional profiling of the osteoprogenitor cells, we identified FOS as a putative master regulator of non-union fractures. We validated FOS activity by profiling a validation cohort of 31 tissue samples. Our work identified new molecular targets for non-union classification and treatment while providing a valuable resource to better understand human bone healing biology.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic triple-negative breast cancer

BACKGROUND

Conditional survival (CS) is defined as the possibility of further survival after patients have survived for several years since diagnosis. This may be highly valuable for real-time prognostic monitoring, especially when considering individualized factors. Such prediction tools were lacking for non-metastatic triple-negative breast cancer (TNBC). Therefore, this study estimated CS and developed a novel CS-nomogram for real-time prediction of 10-year survival.

METHODS

We recruited 32,836 non-metastatic TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019), who were divided into training and validation groups according to a 7:3 ratio. The Kaplan-Meier method estimated overall survival (OS), and the CS was calculated using the formula CS(y|x) =OS(y+x)/OS(x), where OS(x) and OS(y+x) were the survival of x- and (x+y)-years, respectively. The least absolute shrinkage and selection operator (LASSO) regression identified predictors to develop the CS-nomogram.

RESULTS

CS analysis reported gradual improvement in real-time survival over time since diagnosis, with 10-year OS updated annually from an initial 69.9% to 72.8%, 78.1%, 83.0%, 87.0%, 90.3%, 93.0%, 95.0%, 97.0%, and 98.9% (after 1-9 years of survival, respectively). The LASSO regression identified age, marriage, race, T status, N status, chemotherapy, surgery, and radiotherapy as predictors of CS-nomogram development. This model had a satisfactory predictive performance with a stable 10-year time-dependent area under the curves (AUCs) between 0.75 and 0.86.

CONCLUSIONS

Survival of non-metastatic TNBC survivors improved dynamically and non-linearly with survival time. The study developed a CS-nomogram that provided more accurate prognostic data than traditional nomograms, aiding clinical decision-making and reducing patient anxiety.

Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer

Purpose

Necroptosis is a mode of programmed cell death that overcomes apoptotic resistance. We aimed to construct a steady necroptosis-related signature and identify subtypes for prognostic and immunotherapy sensitivity prediction.

Methods

Necroptosis-related prognostic lncRNAs were selected by co-expression analysis, and were used to construct a linear stepwise regression model via univariate and multivariate Cox regression, along with least absolute shrinkage and selection operator (LASSO). Quantitative reverse transcription polymerase chain reaction (RT-PCR) was used to measure the gene expression levels of lncRNAs included in the model. Based on the riskScore calculated, we separated patients into high- and low-risk groups. Afterwards, we performed CIBERSORT and the single-sample gene set enrichment analysis (ssGSEA) method to explore immune infiltration status. Furthermore, we investigated the relationships between the signature and immune landscape, genomic integrity, clinical characteristics, drug sensitivity, and immunotherapy efficacy.

Results

We constructed a robust necroptosis-related 22-lncRNA model, serving as an independent prognostic factor for breast cancer (BRCA). The low-risk group seemed to be the immune-activated type. Meanwhile, it showed that the higher the tumor mutation burden (TMB), the higher the riskScore. PD-L1-CTLA4 combined immunotherapy seemed to be a promising treatment strategy. Lastly, patients were assigned to 4 clusters to better discern the heterogeneity among patients.

Conclusions

The necroptosis-related lncRNA signature and molecular clusters indicated superior predictive performance in prognosis and the immune microenvironment, which may also provide guidance to drug regimens for immunotherapy and provide novel insights into precision medicine.

Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis

BACKGROUND

Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients. However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients.

METHODS

The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs). Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model ("glm" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed.

RESULTS

Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells.

CONCLUSIONS

This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.