TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis

Multifaceted role of TRIM28 in health and disease

The TRIM (tripartite motif) family, with TRIM28 as a key member, plays a vital role in regulating health and disease. TRIM28 contains various functional domains essential for transcriptional regulation, primarily through its interaction with KRAB-ZNF proteins, which influence chromatin remodeling and gene expression. Despite extensive research, the precise mechanisms by which TRIM28 impacts health and disease remain elusive. This review delves into TRIM28's multifaceted roles in maintaining health, contributing to a variety of diseases, and influencing cancer progression. In cancers, TRIM28 exhibits a dual nature, functioning as both a tumor promoter and suppressor depending on the cellular context and cancer type. The review also explores its critical involvement in processes such as DNA repair, cell cycle regulation, epithelial-to-mesenchymal transition, and the maintenance of stem cell properties. By uncovering TRIM28's complex roles across different cancers and other diseases, this review underscores its potential as a therapeutic target. The significance of TRIM28 as a versatile regulator opens the door to innovative therapeutic strategies, particularly in cancer treatment and the management of other diseases. Ongoing research into TRIM28 may yield key insights into disease progression and novel treatment options.

Preclinical evidence using synthetic compounds and natural products indicates that AMPK represents a potential pharmacological target for the therapy of pulmonary diseases

Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) is a highly conserved eukaryotic enzyme discovered as a key regulator of cellular energy homeostasis, with anti-inflammation, antioxidative stress, anticancer, and antifibrosis beneficial effects. AMPK is dysregulated in human pulmonary diseases such as acute lung injury, nonsmall cell lung cancer, pulmonary fibrosis, chronic obstructive pulmonary disease, and asthma. This review provides an overview of the beneficial role of natural, synthetic, and Chinese traditional medicines AMPK modulators in pulmonary diseases, and highlights the role of the AMPK signaling pathway in the lung, emphasizing the importance of finding lead compounds and drugs that can target and modulate AMPK to treat the lung diseases.

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.

Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing

BACKGROUND

Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood.

OBJECTIVE

To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas.

METHODS

ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level.

RESULTS

A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells.

CONCLUSION

We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.

Identification and validation of ubiquitination-related signature and subgroups in immune microenvironment of tuberculosis

BACKGROUND

Mycobacterium tuberculosis (Mtb) is the bacterial pathogen responsible for causing tuberculosis (TB), a severe public health concern that results in numerous deaths worldwide. Ubiquitination (Ub) is an essential physiological process that aids in maintaining homeostasis and contributes to the development of TB. Therefore, the main objective of our study was to investigate the potential role of Ub-related genes in TB.

METHODS

Our research entailed utilizing single sample gene set enrichment analysis (ssGSEA) in combination with several machine learning techniques to discern the Ub-related signature of TB and identify potential diagnostic markers that distinguish TB from healthy controls (HC).

RESULTS

In summary, we used the ssGSEA algorithm to determine the score of Ub families (E1, E2, E3, DUB, UBD, and ULD). Notably, the score of E1, E3, and UBD were lower in TB patients than in HC individuals, and we identified 96 Ub-related differentially expressed genes (UbDEGs). Employing machine learning algorithms, we identified 11 Ub-related hub genes and defined two distinct Ub-related subclusters. Notably, through GSVA and functional analysis, it was determined that these subclusters were implicated in numerous immune-related processes. We further investigated these Ub-related hub genes in four TB-related diseases and found that TRIM68 exhibited higher correlations with various immune cells in different conditions, indicating that it may play a crucial role in the immune process of these diseases.

CONCLUSION

The observed enrichment of Ub-related gene expression in TB patients emphasizes the potential involvement of ubiquitination in the progression of TB. These significant findings establish a basis for future investigations to elucidate the molecular mechanisms associated with TB, select suitable diagnostic biomarkers, and design innovative therapeutic interventions for combating this fatal infectious disease.

Machine learning and integrative analysis identify the common pathogenesis of azoospermia complicated with COVID-19

Background

Although more recent evidence has indicated COVID-19 is prone to azoospermia, the common molecular mechanism of its occurrence remains to be elucidated. The aim of the present study is to further investigate the mechanism of this complication.

Methods

To discover the common differentially expressed genes (DEGs) and pathways of azoospermia and COVID-19, integrated weighted co-expression network (WGCNA), multiple machine learning analyses, and single-cell RNA-sequencing (scRNA-seq) were performed.

Results

Therefore, we screened two key network modules in the obstructive azoospermia (OA) and non-obstructive azoospermia (NOA) samples. The differentially expressed genes were mainly related to the immune system and infectious virus diseases. We then used multiple machine learning methods to detect biomarkers that differentiated OA from NOA. Enrichment analysis showed that azoospermia patients and COVID-19 patients shared a common IL-17 signaling pathway. In addition, GLO1, GPR135, DYNLL2, and EPB41L3 were identified as significant hub genes in these two diseases. Screening of two different molecular subtypes revealed that azoospermia-related genes were associated with clinicopathological characteristics of age, hospital-free-days, ventilator-free-days, charlson score, and d-dimer of patients with COVID-19 (P < 0.05). Finally, we used the Xsum method to predict potential drugs and single-cell sequencing data to further characterize whether azoospermia-related genes could validate the biological patterns of impaired spermatogenesis in cryptozoospermia patients.

Conclusion

Our study performs a comprehensive and integrated bioinformatics analysis of azoospermia and COVID-19. These hub genes and common pathways may provide new insights for further mechanism research.

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma

Background

Despite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment.

Methods

Transcriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment.

Results

A prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells.

Conclusions

The prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.

Epithelial-mesenchymal transition interaction with CD8+ T cell, dendritic cell and immune checkpoints in the development of melanoma

BACKGROUND

Melanoma is fatal cancer originating from melanocytes, whose high metastatic potential leads to an extremely poor prognosis.

OBJECTIVE

This study aimed to reveal the relationship among EMT, TIICs, and immune checkpoints in melanoma.

METHODS

Gene expression data and clinical data of melanoma were downloaded from TCGA, UCSC Xena and GEO databases. EMT-related DEGs were detected for risk score calculation. "ESTIMATE" and "xCell" were used for estimating TIICs and obtaining 64 immune cell subtypes, respectively. Moreover, we evaluated the relationship between the risk score and immune cell subtypes and immune checkpoints.

RESULTS

Seven EMT-related genes were selected to establish a risk scoring system because of their integrated prognostic relevance. The results of GSEA revealed that most of the gene sets focused on immune-related pathways in the low-risk score group. The risk score was significantly correlated with the xCell score of some TIICs, which significantly affected the prognosis of melanoma. Patients with a low-risk score may be associated with a better response to ICI therapy.

CONCLUSION

The individualized risk score could effectively conduct risk stratification, overall survival prediction, ICI therapy prediction, and TME judgment for patients with melanoma, which would be conducive to patients' precise treatment.

Ferroptosis-related gene SOCS1, a marker for tuberculosis diagnosis and treatment, involves in macrophage polarization and facilitates bone destruction in tuberculosis

This study was aimed to reveal the role of ferroptosis in tuberculosis infection. To elucidate the ferroptosis-related DEGs, GEO datasets associated with tuberculosis infection were downloaded. The two external validation GEO datasets were exploited for subsequent verification of the ferroptosis-related DEGs. We further evaluated the correlation among the ferroptosis-related DEGs, therapeutic effects, and drug resistance. Finally, we tried to reveal the engagement of the ferroptosis-related DEGs in bone destruction during TB infection. The present study identified SOCS1 as the only ferroptosis-related DEGs. Compared to the non-TB patients, up-regulation of SOCS1 was evident in the TB patients. After receiving standard anti-TB treatment, significant down-regulation of SOCS1 confirmed its acceptance as the marker for therapeutic efficacy. The involvement of SOCS1 has also been suggested in the regulation of the micro immune environment in TB. Furthermore, SOCS1 might play an important role in causing bone destruction during TB infection. FRGs-SOCS1 may be the key gene involved in the pathogenesis and progression of TB infection.

TYROBP, TLR4 and ITGAM regulated macrophages polarization and immune checkpoints expression in osteosarcoma

We established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. “Estimate” was used for immune infiltrating estimation and “xCell” was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.

Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

Introduction

Gastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.

Materials and Methods

Gene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.

Results

After five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.

Conclusion

CGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.