A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

Endocrine and cellular physiology and pathology of the insulin-like growth factor acid-labile subunit

The acid-labile subunit (ALS) of the insulin-like growth factor (IGF) binding protein (IGFBP) complex, encoded in humans by IGFALS, has a vital role in regulating the endocrine transport and bioavailability of IGF-1 and IGF-2. Accordingly, ALS has a considerable influence on postnatal growth and metabolism. ALS is a leucine-rich glycoprotein that forms high-affinity ternary complexes with IGFBP-3 or IGFBP-5 when they are occupied by either IGF-1 or IGF-2. These complexes constitute a stable reservoir of circulating IGFs, blocking the potentially hypoglycaemic activity of unbound IGFs. ALS is primarily synthesized by hepatocytes and its expression is lower in non-hepatic tissues. ALS synthesis is strongly induced by growth hormone and suppressed by IL-1β, thus potentially serving as a marker of growth hormone secretion and/or activity and of inflammation. IGFALS mutations in humans and Igfals deletion in mice cause modest growth retardation and pubertal delay, accompanied by decreased osteogenesis and enhanced adipogenesis. In hepatocellular carcinoma, IGFALS is described as a tumour suppressor; however, its contribution to other cancers is not well delineated. This Review addresses the endocrine physiology and pathology of ALS, discusses the latest cell and proteomic studies that suggest emerging cellular roles for ALS and outlines its involvement in other disease states.

Comprehensive analysis of mitophagy-related subtypes of breast cancer and the association with immune related characteristics

Breast cancer (BRCA) is a common neoplasm characterized by high levels of molecular heterogeneity. Previous studies have noted the importance of mitophagy for the progression and prognosis of BRCA. However, little was found in the similarity and difference of mitophagy-related gene expression patterns of BRCA. This study intended to investigate the differences in functional activation, somatic mutation, and immune-related characteristics among different subtypes of BRCA associated with mitophagy. Based on bioinformatics analysis, we systematically examined the heterogeneity of breast cancer concerning mitophagy and observed two distinct subtypes with different tumor microenvironments and prognoses. BRCA samples from TCGA database were divided into two subtypes based on the expression of 29 mitophagy-related genes by ConsensusClusterPlus algorithm. Two mitophagy-related subtypes with marked prognostic discrepancies were significantly correlated with race, intrinsic subtype grouped based on PAM50 subtype purity and BRCA Pathology. The results of GSVA and immune microenvironment analysis showed significant differences in cancer-related and immune-related features between the two subtypes. METABRIC datasets were extracted to validate the immune characteristics scoring and the expression of immune checkpoints between different subtypes based on the medium value of TCGA-Mitophagy score. It is noteworthy that the present study is the first to demonstrate a new classification based on the mitophagy of breast cancer, which comes up with a new perspective for the assessment and prognoses of BRCA.

Identifying mitophagy-related genes as prognostic biomarkers and therapeutic targets of gastric carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data

Gastric carcinoma (GC) is the fourth leading cause of cancer-related mortality worldwide. Patients with advanced GC tend to have poor prognoses and shortened survival. Finding novel predictive biomarkers for GC prognosis is an urgent need. Mitophagy is the selection degradation of damaged mitochondria to maintain cellular homeostasis, which has been shown to play both pro- and anti-tumor effects. This study combined single-cell sequencing data and transcriptomics to screen mitophagy-related genes (MRGs) associated with GC progression and analyze their clinical values. Reverse transcription-quantitative PCR (RT-qPCR) and immunochemistry (IHC) further verified gene expression profiles. A total of 18 DE-MRGs were identified after taking an intersection of single-cell sequencing data and MRGs. Cells with a higher MRG score were mainly distributed in the epithelial cell cluster. Cell-to-cell communications among epithelial cells with other cell types were significantly upregulated. We established and validated a reliable nomogram model based on DE-MRGs (GABARAPL2 and CDC37) and traditional clinicopathological parameters. GABARAPL2 and CDC37 displayed different immune infiltration states. Given the significant correlation between hub genes and immune checkpoints, targeting MRGs in GC may supplement more benefits to patients who received immunotherapy. In conclusion, GABARAPL2 and CDC37 may be prognostic biomarkers and candidate therapeutic targets of GC.

Development and validation of an inflammatory response-related signature in triple negative breast cancer for predicting prognosis and immunotherapy

Background

Inflammation is one of the most important characteristics of tumor tissue. Signatures based on inflammatory response-related genes (IRGs) can predict prognosis and treatment response in a variety of tumors. However, the clear function of IRGs in the triple negative breast cancer (TNBC) still needs to be explored.

Methods

IRGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO). Verification analyses were conducted to show the robustness of the signature. The expression of risk genes was identified by RT-qPCR. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The IRGs signature, comprised of four genes, was developed and was shown to be highly correlated with the prognoses of TNBC patients. In contrast with the performance of the other individual predictors, we discovered that the IRGs signature was remarkably superior. Also, the ImmuneScores were elevated in the low-risk group. The immune cell infiltration showed significant difference between the two groups, as did the expression of immune checkpoints.

Conclusion

The IRGs signature could act as a biomarker and provide a momentous reference for individual therapy of TNBC.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics

Background

Recently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagnostic and therapeutic tools available for the disease are insufficient, necessitating the screening of potential biomarkers to predict renal fibrosis.

Methods

Using the Gene Expression Omnibus (GEO) database, we obtained two gene array datasets (GSE76882 and GSE22459) from patients with renal fibrosis and healthy individuals. We identified differentially expressed genes (DEGs) between renal fibrosis and normal tissues and analyzed possible diagnostic biomarkers using machine learning. The diagnostic effect of the candidate markers was evaluated using receiver operating characteristic (ROC) curves and verified their expression using Reverse transcription quantitative polymerase chain reaction (RT-qPCR). The CIBERSORT algorithm was used to determine the proportions of 22 types of immune cells in patients with renal fibrosis, and the correlation between biomarker expression and the proportion of immune cells was studied. Finally, we developed an artificial neural network model of renal fibrosis.

Results

Four candidate genes namely DOCK2, SLC1A3, SOX9 and TARP were identified as biomarkers of renal fibrosis, with the area under the ROC curve (AUC) values higher than 0.75. Next, we verified the expression of these genes by RT-qPCR. Subsequently, we revealed the potential disorder of immune cells in the renal fibrosis group through CIBERSORT analysis and found that immune cells were highly correlated with the expression of candidate markers.

Conclusion

DOCK2, SLC1A3, SOX9, and TARP were identified as potential diagnostic genes for renal fibrosis, and the most relevant immune cells were identified. Our findings provide potential biomarkers for the diagnosis of renal fibrosis.

Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer's disease and their immune cell infiltration correlation

Background

Alzheimer's disease (AD), a neurodegenerative disorder with progressive symptoms, seriously endangers human health worldwide. AD diagnosis and treatment are challenging, but molecular biomarkers show diagnostic potential. This study aimed to investigate AD biomarkers in the peripheral blood.

Method

Utilizing three microarray datasets, we systematically analyzed the differences in expression and predictive value of mitophagy-related hub genes (MRHGs) in the peripheral blood mononuclear cells of patients with AD to identify potential diagnostic biomarkers. Subsequently, a protein-protein interaction network was constructed to identify hub genes, and functional enrichment analyses were performed. Using consistent clustering analysis, AD subtypes with significant differences were determined. Finally, infiltration patterns of immune cells in AD subtypes and the relationship between MRHGs and immune cells were investigated by two algorithms, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).

Results

Our study identified 53 AD- and mitophagy-related differentially expressed genes and six MRHGs, which may be potential biomarkers for diagnosing AD. Functional analysis revealed that six MRHGs significantly affected biologically relevant functions and signaling pathways such as IL-4 Signaling Pathway, RUNX3 Regulates Notch Signaling Pathway, IL-1 and Megakaryocytes in Obesity Pathway, and Overview of Leukocyteintrinsic Hippo Pathway. Furthermore, CIBERSORT and ssGSEA algorithms were used for all AD samples to analyze the abundance of infiltrating immune cells in the two disease subtypes. The results showed that these subtypes were significantly related to immune cell types such as activated mast cells, regulatory T cells, M0 macrophages, and neutrophils. Moreover, specific MRHGs were significantly correlated with immune cell levels.

Conclusion

Our findings suggest that MRHGs may contribute to the development and prognosis of AD. The six identified MRHGs could be used as valuable diagnostic biomarkers for further research on AD. This study may provide new promising diagnostic and therapeutic targets in the peripheral blood of patients with AD.

From past to future: Bibliometric analysis of global research productivity on nomogram (2000-2021)

Background

Nomogram, a visual clinical predictive model, provides a scientific basis for clinical decision making. Herein, we investigated 20 years of nomogram research responses, focusing on current and future trends and analytical challenges.

Methods

We mined data of scientific literature from the Core Collection of Web of Science, searching for the original articles with title "Nomogram^*/Parton Table^*/Parton Nomogram^*", published within January 1st, 2000 to December 30th, 2021. Data records were validated using HistCite Version and analyzed with a transformable statistical method, the Bibliometrix 3.0 package of R Studio.

Results

In total, 4,176 original articles written by 19,158 authors were included from 915 sources. Annually, Nomogram publications are continually produced, which have rapidly grown since 2018. China published the most articles; however, its total citations ranked second after the United States. Both total citations and average article citations in the United States rank first globally, and a high degree of cooperation exists between countries. Frontiers in Oncology published the most papers (238); this number has grown rapidly since 2019. Journal of Urology had the highest H-index, with an average increase in publications over the past 20 years. Most research topics were tumor-related, among which tumor risk prediction and prognostic evaluation were the main contents. Research on prognostic assessment is more published and advanced, while risk prediction and diagnosis have good developmental prospects. Furthermore, nomogram of the urinary system has been highly developed. Following advancements in nomogram modeling, it has recently been applied to non-oncological subjects.

Conclusion

This bibliometric analysis provides a comprehensive overview of the current nomogram status, which could enable better understanding of its development over the years, and provide global researchers a comprehensive analysis and structured information to help identify hot spots and gaps in future research.

Based on different immune responses under the glucose metabolizing type of papillary thyroid cancer and the response to anti-PD-1 therapy

Glucose metabolism-related genes play an important role in the development and immunotherapy of many tumours, but their role in thyroid cancer is ambiguous. To investigate the role of glucose metabolism-related genes in the development of papillary thyroid cancer (PTC) and their correlation with the clinical outcome of PTC, we collected transcriptomic data from 501 PTC patients in the Cancer Genome Atlas (TCGA). We performed nonnegative matrix decomposition clustering of 2752 glucose metabolism-related genes from transcriptome data and classified PTC patients into three subgroups (C1 for high activation of glucose metabolism, C2 for low activation of glucose metabolism and C3 for moderate activation of glucose metabolism) based on the activation of different glucose metabolism-related genes in 10 glucose metabolism-related pathways. We found a positive correlation between the activation level of glucose metabolism and the tumour mutation burden (TMB), neoantigen number, mRNA stemness index (mRNAsi), age, and tumour stage in PTC patients. Next, we constructed a prognostic prediction model for PTC using six glucose metabolism-related genes (PGBD5, TPO, IGFBPL1, TMEM171, SOD3, TDRD9) and constructed a nomogram based on the risk score and clinical parameters of PTC patients. Both the prognostic risk prediction model and nomogram had high stability and accuracy for predicting the progression-free interval (PFI) in PTC patients. Patients were then divided into high-risk and low-risk groups by risk score. The high-risk group was sensitive to paclitaxel and anti-PD-1 treatment, and the low-risk group was sensitive to sorafenib treatment. We found that the high-risk group was enriched in inflammatory response pathways and associated with high level of immune cell infiltration. To verify the accuracy of the prognostic prediction model, we knocked down PGBD5 in PTC cells and found that the proliferation ability of PTC cells was significantly reduced. This suggests that PGBD5 may be a relatively important oncogene in PTC. Our study constructed a prognostic prediction model and classification of PTC by glucose metabolism-related genes, which provides a new perspective on the role of glucose metabolism in the development and immune microenvironment of PTC and in guiding chemotherapy, targeted therapy and immune checkpoint blockade therapy of PTC.