A prognostic model based on cell-cycle control predicts outcome of breast cancer patients

DiPRO1 distinctly reprograms muscle and mesenchymal cancer cells

We have recently identified the uncharacterized ZNF555 protein as a component of a productive complex involved in the morbid function of the 4qA locus in facioscapulohumeral dystrophy. Subsequently named DiPRO1 (Death, Differentiation, and PROliferation related PROtein 1), our study provides substantial evidence of its role in the differentiation and proliferation of human myoblasts. DiPRO1 operates through the regulatory binding regions of SIX1, a master regulator of myogenesis. Its relevance extends to mesenchymal tumors, such as rhabdomyosarcoma (RMS) and Ewing sarcoma, where DiPRO1 acts as a repressor via the epigenetic regulators TIF1B and UHRF1, maintaining methylation of cis-regulatory elements and gene promoters. Loss of DiPRO1 mimics the host defense response to virus, awakening retrotransposable repeats and the ZNF/KZFP gene family. This enables the eradication of cancer cells, reprogramming the cellular decision balance towards inflammation and/or apoptosis by controlling TNF-α via NF-kappaB signaling. Finally, our results highlight the vulnerability of mesenchymal cancer tumors to si/shDiPRO1-based nanomedicines, positioning DiPRO1 as a potential therapeutic target.

Role of chromosomal cohesion and separation in aneuploidy and tumorigenesis

Cell division is a crucial process, and one of its essential steps involves copying the genetic material, which is organized into structures called chromosomes. Before a cell can divide into two, it needs to ensure that each newly copied chromosome is paired tightly with its identical twin. This pairing is maintained by a protein complex known as cohesin, which is conserved in various organisms, from single-celled ones to humans. Cohesin essentially encircles the DNA, creating a ring-like structure to handcuff, to keep the newly synthesized sister chromosomes together in pairs. Therefore, chromosomal cohesion and separation are fundamental processes governing the attachment and segregation of sister chromatids during cell division. Metaphase-to-anaphase transition requires dissolution of cohesins by the enzyme Separase. The tight regulation of these processes is vital for safeguarding genomic stability. Dysregulation in chromosomal cohesion and separation resulting in aneuploidy, a condition characterized by an abnormal chromosome count in a cell, is strongly associated with cancer. Aneuploidy is a recurring hallmark in many cancer types, and abnormalities in chromosomal cohesion and separation have been identified as significant contributors to various cancers, such as acute myeloid leukemia, myelodysplastic syndrome, colorectal, bladder, and other solid cancers. Mutations within the cohesin complex have been associated with these cancers, as they interfere with chromosomal segregation, genome organization, and gene expression, promoting aneuploidy and contributing to the initiation of malignancy. In summary, chromosomal cohesion and separation processes play a pivotal role in preserving genomic stability, and aberrations in these mechanisms can lead to aneuploidy and cancer. Gaining a deeper understanding of the molecular intricacies of chromosomal cohesion and separation offers promising prospects for the development of innovative therapeutic approaches in the battle against cancer.

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer

BACKGROUND

The Golgi apparatus plays a pivotal role in various aspects of cancer. This study aims to investigate the predictive value of Golgi apparatus-related genes (GARGs) in breast cancer prognosis and immunotherapy response evaluation.

METHODS

Transcriptional and clinical data from the TCGA-BRCA cohort and GSE96058 cohort were utilized to construct and validate a prognostic model for breast cancer using Cox regression analysis. Differences in immune landscape, somatic mutations, gene expression, drug sensitivity, and immunotherapy response between different risk groups were assessed. A prognostic nomogram for breast cancer was further developed and evaluated. qPCR and single-cell sequencing analyses were performed to validate the expression of GARGs.

RESULTS

A total of 394 GARGs significantly associated with breast cancer prognosis were identified, leading to the construction of a prognostic risk feature comprising 10 GARGs. This feature effectively stratified breast cancer patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse prognosis. Meanwhile, significant differences in clinicopathological features, immune infiltration, drug sensitivity, and immunotherapy response were observed between the high- and low-risk groups. The constructed nomogram incorporating these factors showed superior performance in prognostic assessment for breast cancer patients. Ultimately, the utilization of qPCR and single-cell sequencing techniques substantiated the disparate expression patterns of these prognostic genes in breast cancer.

CONCLUSION

Our findings demonstrate that a prognostic risk feature derived from GARGs holds promising application potential for predicting prognosis and evaluating immunotherapy response in breast cancer patients.

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

Breast cancer (BC) is one of the most common malignancies. However, the existing pathological grading system cannot accurately and effectively predict the survival rate and immune checkpoint treatment response of BC patients. In this study, based on The Cancer Genome Atlas (TCGA) database, a total of 7 immune-related genes (IRGs) were screened out to construct a prognostic model. Subsequently, the clinical prognosis, pathological characteristics, cancer-immunity cycle, tumour immune dysfunction and exclusion (TIDE) score, and immune checkpoint inhibitor (ICI) response were compared between the high- and low-risk groups. In addition, we determined the potential regulatory effect of NPR3 on BC cell proliferation, migration, and apoptosis. The model consisting of 7 IRGs was an independent prognostic factor. Patients with lower risk scores exhibited longer survival times. Moreover, the expression of NPR3 was increased but the expression of PD-1, PD-L1, and CTLA-4 was decreased in the high-risk group compared to the low-risk group. In addition, compared with si-NC, si-NPR3 suppressed proliferation and migration but promoted apoptosis in both MDA-MB-231 and MCF-7 cells. This study presents a model for predicting survival outcomes and provides a strategy to guide effective personalized immunotherapy in BC patients.

Evaluation of the Prognostic Capacity of a Novel Survival Marker in Patients with Sinonasal Squamous Cell Carcinoma

Sinonasal squamous cell carcinoma (SNSCC) is a malignant tumor associated with poor survival, and easily obtainable prognostic markers are of high interest. Therefore, we aimed to assess the prognostic value of a novel survival index (SI) combining prognostic values of clinical (T and N classifications and invasion across Ohngren’s line), inflammatory (neutrophil-to-lymphocyte ratio), and nutritional (albumin and body-mass index) markers. All patients with primarily treated SNSCC between 2002 and 2020 (n = 51) were included. Each of the six SI components was stratified into a low- (0) and high-risk (1) categories. Subsequently, the cohort was stratified into low- (SI of 0–2) and high-risk SI groups (SI of 3–6). Overall survival (OS) and disease-free survival (DFS) were compared between patients with low- and high-risk SI. The log-rank test was used to test for statistical significance. Overall, the mortality rate was 41.2% (n = 21), and the recurrence rate was 43.1% (n = 22). We observed significantly better OS in patients with low-risk SI (n = 24/51, 47.1%, mean OS: 7.9 years, 95% confidence interval (CI): 6.3–9.6 years) than in high-risk SI (n = 27/51, 52.9%, mean OS: 3.4 years, 95% CI: 2.2–4.5 years; p = 0.013). Moreover, we also showed that patients with low-risk SI had a longer DFS than patients with high-risk SI (mean DFS: 6.4, 95% CI: 4.8–8.0 vs. mean DFS: 2.4 years, 95% CI 1.3–3.5, p = 0.012). The SI combines the prognostic capacity of well-established clinical, radiologic, inflammatory, and nutritional prognosticators and showed prognostic potential in our cohort of SNSCC patients.

A Novel Risk Model Based on Autophagy-Related LncRNAs Predicts Prognosis and Indicates Immune Infiltration Landscape of Patients With Cutaneous Melanoma

Cutaneous melanoma (CM) is a malignant tumor with a high incidence rate and poor prognosis. Autophagy plays an essential role in the development of CM; however, the role of autophagy-related long noncoding RNAs (lncRNAs) in this process remains unknown. Human autophagy-related genes were extracted from the Human Autophagy Gene Database and screened for autophagy-related lncRNAs using Pearson correlation. Multivariate Cox regression analysis was implemented to identify ten autophagy-related lncRNAs associated with prognosis, and a risk model was constructed. The Kaplan-Meier survival curve showed that the survival probability of the high-risk group was lower than that of the low-risk group. A novel predictive model was constructed to investigate the independent prognostic value of the risk model. The nomogram results showed that the risk score was an independent prognostic signature that distinguished it from other clinical characteristics. The immune infiltration landscape of the low-risk and high-risk groups was further investigated. The low-risk groups displayed higher immune, stromal, and ESTIMATE scores and lower tumor purity. The CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms indicated a notable gap in immune cells between the low- and high-risk groups. Ten autophagy-related lncRNAs were significantly correlated with immune cells. Finally, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) results demonstrated that autophagy-related lncRNA-mediated and immune-related signaling pathways are crucial factors in regulating CM. Altogether, these data suggest that constructing a risk model based on ten autophagy-related lncRNAs can accurately predict prognosis and indicate the tumor microenvironment of patients with CM. Thus, our study provides a new perspective for the future clinical treatment of CM.

M2-phenotype tumour-associated macrophages upregulate the expression of prognostic predictors MMP14 and INHBA in pancreatic cancer

Pancreatic cancer is one of the most lethal gastrointestinal tumours, the most common pathological type is pancreatic adenocarcinoma (PAAD). In recent year, immune imbalanced in tumour microenvironment has been shown to play an important role in the evolution of tumours progression, and the efficacy of immunotherapy has been gradually demonstrated in clinical practice. In this study, we propose to construct an immune-related prognostic risk model based on immune-related genes MMP14 and INHBA expression that can assess the prognosis of pancreatic cancer patients and identify potential therapeutic targets for pancreatic cancer, to provide new ideas for the treatment of pancreatic cancer. We also investigate the correlation between macrophage infiltration and MMP14 and INHBA expression. First, the gene expression data of pancreatic cancer and normal pancreatic tissue were obtained from The Cancer Genome Atlas Program (TCGA) and The Genotype-Tissue Expression public database (GTEx). The differentially expressed immune-related genes between pancreatic cancer samples and normal sample were screened by R software. Secondly, univariate Cox regression analysis were used to evaluate the relationship between immune-related genes and the prognosis of pancreatic cancer patients. A polygenic risk score model was constructed by Cox regression analysis. The prognostic nomogram was constructed, and its performance was evaluated comprehensively by internal calibration curve and C-index. Using the risk model, each patient gets a risk score, and was divided into high- or low- risk groups. The proportion of 22 types of immune cells infiltration in pancreatic cancer samples was inferred by CIBERSOFT algorithm, correlation analysis (Pearson method) was used to analyse the correlation between the immune-related genes and immunes cells. Then, we applied macrophage conditioned medium to culture pancreatic cancer cell line PANC1, detected the expression of MMP14 and INHBA by qRT-PCR and Western blot methods. Knock-down MMP14 and INHBA in PANC1 cells by transfected with shRNA lentiviruses. Detection of migration ability of pancreatic cells was done by trans-well cell migration assay. A subcutaneous xenograft tumour model of human pancreatic cancer in nude mice was constructed. In conclusion, an immune-related gene prognostic model was constructed, patients with high-risk scores have poorer survival status, M2-phenotype tumour-associated macrophages (TAMs) up-regulate two immune-related genes, MMP14 and INHBA, which were used to establish the prognostic model. Knock-down of MMP14 and INHBA inhibited invasion of pancreatic cancer.

Bioinformatics Analysis Reveals the Related Role of miR-511-5p in the Progression of Breast Cancer

Breast cancer remains a dangerous disease, and delving the molecular mechanism of breast cancer is still necessary. To illustrate the role of miR-511-5p, TCGA database was used to excavate the abundance of miR-511-5p, and the miR-511-5p level was measured in the pathological tissues and tumor cell lines. Moreover, the targets of miR-511-5p were identified with miRDIP and GEPIA and then were used for functional enrichment analysis. Besides, the targets of miR-511-5p were analyzed with the protein-protein interaction (PPI) network for the hub nodes, and then the expression levels of the hub nodes were visualized with the GEPIA database. The results showed that miR-511-5p was significantly downregulated in multiple types of tumor samples in the online database, and the downregulated miR-511-5p was also found in pathological tissues and tumor cell lines. Moreover, 48 genes were identified as the potential targets of miR-511-5p by miRDIP and GEPIA databases and enriched in cell cycle, PI3K/AKT, and P53 pathways. Besides, seven genes including BRCA1, FN1, CCNE1, CCND1, CHEK1, BUB3, and CDC25A were identified as the hub nodes by the PPI network, and CCNE1 and CHEK1 were confirmed to be related with the prognostic survival of the patients with breast cancer. In conclusion, the proofs in this study suggest that reduced miR-511-5p was a biomarker event for breast cancer, and CCNE1 and CHEK1 served as potential targets of miR-511-5p to involve the progression of breast cancer.

Varying outcomes of triple-negative breast cancer in different age groups-prognostic value of clinical features and proliferation

PURPOSE

Triple-negative breast cancer (TNBC) is an aggressive disease lacking specific biomarkers to guide treatment decisions. We evaluated the combined prognostic impact of clinical features and novel biomarkers of cell cycle-progression in age-dependent subgroups of TNBC patients.

METHODS

One hundred forty seven TNBC patients with complete clinical data and up to 18 year follow-up were collected from Turku University Hospital, Finland. Eight biomarkers for cell division were immunohistochemically detected to evaluate their clinical applicability in relation to patient and tumor characteristics.

RESULTS

Age at diagnosis was the decisive factor predicting disease-specific mortality in TNBC (p = 0.002). The established prognostic features, nodal status and Ki-67, predicted survival only when combined with age. The outcome and prognostic features differed significantly between age groups, middle-aged patients showing the most favorable outcome. Among young patients, only lack of basal differentiation predicted disease outcome, indicating 4.5-fold mortality risk (p = 0.03). Among patients aged > 57, the established prognostic features predicted disease outcome with up to 3.0-fold mortality risk for tumor size ≥ 2 cm (p = 0.001). Concerning cell proliferation, Ki-67 alone was a significant prognosticator among patients aged > 57 years (p = 0.009). Among the studied cell cycle-specific biomarkers, only geminin predicted disease outcome, indicating up to 6.2-fold increased risk of mortality for tumor size < 2 cm (p = 0.03).

CONCLUSION

Traditional clinical features do not provide optimal prognostic characterization for all TNBC patients. Young age should be considered as an additional adverse prognostic feature in therapeutic considerations. Increased proliferation, as evaluated using Ki-67 or geminin immunohistochemistry, showed potential in detecting survival differences in subgroups of TNBC.