Single-cell RNA sequencing reveals the suppressive effect of PPP1R15A inhibitor Sephin1 in antitumor immunity

Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis

OBJECTIVES

To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value.

METHODS

Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts.

RESULTS

Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion.

CONCLUSION

A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

Mammalian integrated stress responses in stressed organelles and their functions

The integrated stress response (ISR) triggered in response to various cellular stress enables mammalian cells to effectively cope with diverse stressful conditions while maintaining their normal functions. Four kinases (PERK, PKR, GCN2, and HRI) of ISR regulate ISR signaling and intracellular protein translation via mediating the phosphorylation of eukaryotic translation initiation factor 2 α (eIF2α) at Ser51. Early ISR creates an opportunity for cells to repair themselves and restore homeostasis. This effect, however, is reversed in the late stages of ISR. Currently, some studies have shown the non-negligible impact of ISR on diseases such as ischemic diseases, cognitive impairment, metabolic syndrome, cancer, vanishing white matter, etc. Hence, artificial regulation of ISR and its signaling with ISR modulators becomes a promising therapeutic strategy for relieving disease symptoms and improving clinical outcomes. Here, we provide an overview of the essential mechanisms of ISR and describe the ISR-related pathways in organelles including mitochondria, endoplasmic reticulum, Golgi apparatus, and lysosomes. Meanwhile, the regulatory effects of ISR modulators and their potential application in various diseases are also enumerated.

A novel TCGA-validated programmed cell-death-related signature of ovarian cancer

BACKGROUND

Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC.

METHODS

By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores.

RESULTS

A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines.

CONCLUSION

Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Effect of radiotherapy on head and neck cancer tissues in patients receiving radiotherapy: a bioinformatics analysis-based study

Radiotherapy is pivotal in treating head and neck cancers including nasopharyngeal, tongue, hypopharyngeal, larynx, maxillary sinus, parotid gland, and oral cancers. It holds the potential for curative effects and finds application in conjunction with chemotherapy, either as a radical method to preserve organ function or as an adjuvant postoperative treatment. We used bioinformatics analysis to investigate the effects of radiotherapy on head and neck cancer tissues in patients who had received radiotherapy. In this study, the expression and mutation profiles of The Cancer Genome Atlas-Head-Neck Squamous Cell Carcinoma were downloaded from the UCSC-Xena database, categorizing patients into two groups-those receiving radiotherapy and those not receiving radiotherapy. Subsequently, differential expression analysis and gene set enrichment analysis (GSEA) were performed. Following this, single-sample GSEA (ssGSEA) scores related to glucose and lipid metabolism were compared between the two groups. Additionally, immune cell infiltration analysis and single-cell verification were performed. Finally, the mutation profiles of the two groups were compared. The analyses revealed that patients receiving radiotherapy exhibited prolonged survival, enhanced apoptosis in head and neck cancer tissue, and diminished keratinocyte proliferation and migration. A comparison of ssGSEA scores related to glucose and lipid metabolism between the two groups indicated a reduction in glycolysis, tricarboxylic acid cycle activity, and fat synthesis in tissues treated with radiotherapy, suggesting that radiotherapy can effectively inhibit tumour cell energy metabolism. Analyses of immune cell infiltration and single-cell verification suggested decreased infiltration of immune cells post-radiotherapy in head and neck cancer tissues. A comparison of mutation profiles revealed a higher frequency of TP53, TTN, and CDKN2A mutations in patients receiving radiotherapy for head and neck cancer. In conclusion, the bioinformatics analyses delved into the effect of radiotherapy on patients with head and neck carcinoma. This study provides a theoretical framework elucidating the molecular mechanisms underlying radiotherapy's efficacy in treating head and neck cancer and presents scientific recommendations for drug therapy following radiotherapy.

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value.

METHODS

OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs.

RESULTS

We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated.

CONCLUSIONS

Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.

The integrated stress response in cancer progression: a force for plasticity and resistance

During their quest for growth, adaptation, and survival, cancer cells create a favorable environment through the manipulation of normal cellular mechanisms. They increase anabolic processes, including protein synthesis, to facilitate uncontrolled proliferation and deplete the tumor microenvironment of resources. As a dynamic adaptation to the self-imposed oncogenic stress, cancer cells promptly hijack translational control to alter gene expression. Rewiring the cellular proteome shifts the phenotypic balance between growth and adaptation to promote therapeutic resistance and cancer cell survival. The integrated stress response (ISR) is a key translational program activated by oncogenic stress that is utilized to fine-tune protein synthesis and adjust to environmental barriers. Here, we focus on the role of ISR signaling for driving cancer progression. We highlight mechanisms of regulation for distinct mRNA translation downstream of the ISR, expand on oncogenic signaling utilizing the ISR in response to environmental stresses, and pinpoint the impact this has for cancer cell plasticity during resistance to therapy. There is an ongoing need for innovative drug targets in cancer treatment, and modulating ISR activity may provide a unique avenue for clinical benefit.