Clinical significance of cyclin-dependent kinase inhibitor 2C expression in cancers: from small cell lung carcinoma to pan-cancers

An integrative analysis based on multiple cell death patterns identifies an immunosuppressive subtype and establishes a prognostic signature in lower-grade glioma

BACKGROUND

Cell death modulates the biological behaviors of tumors. However, the comprehensive role of the multiple forms of cell death in lower-grade glioma (LGG) is unknown.

METHODS

We collected the transcriptional data of LGG patients from public repositories to comprehensively examine six cell death patterns (autophagy, apoptosis, cuproptosis, necroptosis, ferroptosis, and pyroptosis) in LGG samples and systematically correlated these patterns with patient survival, underlying biological processes, and drug sensitivity using serial bioinformatics analysis, clinical sample validation and in vitro assays.

RESULTS

We identified and independently validated three reproducible cell death-based clusters associated with distinct clinical outcomes and tumor microenvironment characteristics. The Tumor Immune Dysfunction and Exclusion algorithm was applied for predicting how these three clusters would respond to immune checkpoint blockade (ICB) therapy; we found potential resistance of cluster B to ICB therapy. We also performed drug screening to identify cluster-specific drugs. Furthermore, a scoring system, designated as the CDPM score, was developed to estimate the cell death patterns of patients with LGG; this system could predict both LGG patients' prognosis and immunotherapy efficacy. By performing multiplex immunofluorescence staining, we validated the correlations of GNAL expression with the molecular and clinical features of LGG in an independent LGG cohort. Finally, in vitro assays showed that GNAL promoted apoptosis and inhibited the proliferation of LGG cells.

CONCLUSION

The new cell death-based subtype system indicates several features of LGG biology and reveals novel insights into the use of precision medicine for treating LGG. The CDPM score could be used to predict the immunotherapy response and prognosis of LGG patients; moreover, it could indicate a novel direction for improving LGG management.

1-L Transcription in Prion Diseases

Understanding the pathogenesis and mechanisms of prion diseases can significantly expand our knowledge in the field of neurodegenerative diseases. Prion biology is increasingly recognized as being relevant to the pathophysiology of Alzheimer's disease and Parkinson's disease, both of which affect millions of people each year. This bioinformatics study used a theoretical protein-RNA recognition code (1-L transcription) to reveal the post-transcriptional regulation of the prion protein (PrPC). The principle for this method is directly elucidated on PrPC, in which an octa-repeat can be 1-L transcribed into a GGA triplet repeat RNA aptamer known to reduce the misfolding of normal PrPC into abnormal PrPSc. The identified genes/proteins are associated with mitochondria, cancer, COVID-19 and ER-stress, and approximately half are directly or indirectly associated with prion diseases. For example, the octa-repeat supports CD44, and regions of the brain with astrocytic prion accumulation also display high levels of CD44.

CDCA3 is a potential biomarker for glioma malignancy and targeted therapy

CDCA3, a cell cycle regulator gene that plays a catalytic role in many tumors, was initially identified as a regulator of cell cycle progression, specifically facilitating the transition from the G2 phase to mitosis. However, its role in glioma remains unknown. In this study, bioinformatics analyses (TCGA, CGGA, Rembrandt) shed light on the upregulation and prognostic value of CDCA3 in gliomas. It can also be included in a column chart as a parameter predicting 3- and 5-year survival risk (C index = 0.86). According to Gene Set Enrichment Analysis and gene ontology analysis, the biological processes of CDCA3 are mainly concentrated in the biological activities related to cell cycle such as DNA replication and nuclear division. CDCA3 is closely associated with many classic glioma biomarkers (CDK4, CDK6), and inhibitors of CDK4 and CDK6 have been shown to be effective in tumor therapy. We have demonstrated that high expression of CDCA3 indicates a higher malignancy and poorer prognosis in gliomas.

Single nucleotide polymorphisms and Zn transport by ZIP11 shape functional phenotypes of HeLa cells

Zinc (Zn) is a vital micronutrient with essential roles in biological processes like enzyme function, gene expression, and cell signaling. Disruptions in the cellular regulation of Zn2+ ions often lead to pathological states. Mammalian Zn transporters, such as ZIP11, play a key role in homeostasis of this ion. ZIP11 resides predominately in the nucleus and Golgi apparatus. Our laboratory reported a function of ZIP11 in maintaining nuclear Zn levels in HeLa cervical cancer cells. Analyses of cervical and ovarian cancer patients' datasets identified four coding, single nucleotide polymorphisms (SNPs) in SLC39A11, the gene that encodes ZIP11, correlating with disease severity. We hypothesized that these SNPs might translate to functional changes in the ZIP11 protein by modifying access to substrate availability. We also proposed that a metal-binding site (MBS) in ZIP11 is crucial for transmembrane Zn2+ transport and required for maintenance of various pathogenic phenotypes observed in HeLa cells. Here, we investigated these claims by re-introducing single the SLC39A11 gene encoding for mutant residues associated with the SNPs, as well as MBS mutations into HeLa cells knocked down for the transporter. Some SNPs-encoding ZIP11 variants rescued Zn levels, proliferation, migration, and invasiveness of knockdown (KD) cells. Conversely, single MBS mutations mimicked the traits of KD cells, confirming the transporter's role in establishing and maintaining proliferative, migratory, and invasive traits. Overall, the intricate role of Zn in cellular dynamics and cancer progression underscores the significance of Zn transporters like ZIP11 in potential therapeutic interventions.

Cuprotosis clusters predict prognosis and immunotherapy response in low-grade glioma

Cuprotosis, an emerging mode of cell death, has recently caught the attention of researchers worldwide. However, its impact on low-grade glioma (LGG) patients has not been fully explored. To gain a deeper insight into the relationship between cuprotosis and LGG patients' prognosis, we conducted this study in which LGG patients were divided into two clusters based on the expression of 18 cuprotosis-related genes. We found that LGG patients in cluster A had better prognosis than those in cluster B. The two clusters also differed in terms of immune cell infiltration and biological functions. Moreover, we identified differentially expressed genes (DEGs) between the two clusters and developed a cuprotosis-related prognostic signature through the least absolute shrinkage and selection operator (LASSO) analysis in the TCGA training cohort. This signature divided LGG patients into high- and low-risk groups, with the high-risk group having significantly shorter overall survival (OS) time than the low-risk group. Its predictive reliability for prognosis in LGG patients was confirmed by the TCGA internal validation cohort, CGGA325 cohort and CGGA693 cohort. Additionally, a nomogram was used to predict the 1-, 3-, and 5-year OS rates of each patient. The analysis of immune checkpoints and tumor mutation burden (TMB) has revealed that individuals belonging to high-risk groups have a greater chance of benefiting from immunotherapy. Functional experiments confirmed that interfering with the signature gene TNFRSF11B inhibited LGG cell proliferation and migration. Overall, this study shed light on the importance of cuprotosis in LGG patient prognosis. The cuprotosis-related prognostic signature is a reliable predictor for patient outcomes and immunotherapeutic response and can help to develop new therapies for LGG.

Characterization of tumor microenvironment infiltration and therapeutic responses of cell cycle-related genes' signature in breast cancer

BACKGROUND

It is unknown how the cell cycle plays a role in breast cancer (BC). This study aimed to establish a clinically applicable predictive model to predict the therapeutic responses and overall survival in BC patients.

MATERIALS AND METHODS

Cell cycle-related genes (CCGs) were identified within the Cancer Genome Atlas cohort (n is equal to 1001) and the Gene Expression Omnibus cohort (n is equal to 3265). An analysis of univariate and multivariate Cox was then conducted to develop a nomogram based on CCGs. After validating the nomogram, risk cohort stratification was established and the predictive value was examined. Finally, immune cell infiltration and therapeutic responses were analysed.

RESULTS

Based on 15 CCGs, 4 prognostic predictors were identified and entered into the nomogram. According to the curves of calibration, the estimated and observed value of the nomogram is in optimal agreement. Subsequently, stratification into two risk cohorts showed that the predictive value, immune cell infiltration and overall survival were better among patients with low risk. Immune checkpoint expression in patients with BC at higher risk was downregulated. Furthermore, the results of the study revealed that doxorubicin, paclitaxel, docetaxel, cisplatin and vinorelbine all had higher IC50 values in patients with high-risk BC.

CONCLUSION

The nomogram based on CCG could assess tumour immune micro-environment regulation and therapeutic responses of immunotherapy in BC. Moreover, it may provide novel information on the control of immune micro-environment infiltration in BC and aid in the development of targeted immunotherapy.

PAXIP1 and STAG2 converge to maintain 3D genome architecture and facilitate promoter/enhancer contacts to enable stress hormone-dependent transcription

How steroid hormone receptors (SHRs) regulate transcriptional activity remains partly understood. Upon activation, SHRs bind the genome together with a co-regulator repertoire, crucial to induce gene expression. However, it remains unknown which components of the SHR-recruited co-regulator complex are essential to drive transcription following hormonal stimuli. Through a FACS-based genome-wide CRISPR screen, we functionally dissected the Glucocorticoid Receptor (GR) complex. We describe a functional cross-talk between PAXIP1 and the cohesin subunit STAG2, critical for regulation of gene expression by GR. Without altering the GR cistrome, PAXIP1 and STAG2 depletion alter the GR transcriptome, by impairing the recruitment of 3D-genome organization proteins to the GR complex. Importantly, we demonstrate that PAXIP1 is required for stability of cohesin on chromatin, its localization to GR-occupied sites, and maintenance of enhancer-promoter interactions. In lung cancer, where GR acts as tumor suppressor, PAXIP1/STAG2 loss enhances GR-mediated tumor suppressor activity by modifying local chromatin interactions. All together, we introduce PAXIP1 and STAG2 as novel co-regulators of GR, required to maintain 3D-genome architecture and drive the GR transcriptional programme following hormonal stimuli.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.