UTRN inhibits melanoma growth by suppressing p38 and JNK/c-Jun signaling pathways

A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes.

RESULTS

Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group.

CONCLUSIONS

The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.

UTRN as a potential biomarker in breast cancer: a comprehensive bioinformatics and in vitro study

Utrophin (UTRN), known as a tumor suppressor, potentially regulates tumor development and the immune microenvironment. However, its impact on breast cancer's development and treatment remains unstudied. We conducted a thorough examination of UTRN using both bioinformatic and in vitro experiments in this study. We discovered UTRN expression decreased in breast cancer compared to standard samples. High UTRN expression correlated with better prognosis. Drug sensitivity tests and RT-qPCR assays revealed UTRN's pivotal role in tamoxifen resistance. Furthermore, the Kruskal-Wallis rank test indicated UTRN's potential as a valuable diagnostic biomarker for breast cancer and its utility in detecting T stage of breast cancer. Additionally, our results demonstrated UTRN's close association with immune cells, inhibitors, stimulators, receptors, and chemokines in breast cancer (BRCA). This research provides a novel perspective on UTRN's role in breast cancer's prognostic and therapeutic value. Low UTRN expression may contribute to tamoxifen resistance and a poor prognosis. Specifically, UTRN can improve clinical decision-making and raise the diagnosis accuracy of breast cancer.

The Essential Oil from Conyza bonariensis (L.) Cronquist (Asteraceae) Exerts an In Vitro Antimelanoma Effect by Inducing Apoptosis and Modulating the MAPKs, NF-κB, and PKB/AKT Signaling Pathways

The characterization and cytotoxicity of the essential oil from Conyza bonariensis (L.) aerial parts (CBEO) were previously conducted. The major compound was (Z)-2-lachnophyllum ester (EZ), and CBEO exhibited significant ROS-dependent cytotoxicity in the melanoma cell line SK-MEL-28. Herein, we employed the Molegro Virtual Docker v.6.0.1 software to investigate the interactions between the EZ and Mitogen-Activated Protein Kinases (MAPKs), the Nuclear Factor kappa B (NF-κB), and the Protein Kinase B (PKB/AKT). Additionally, in vitro assays were performed in SK-MEL-28 cells to assess the effect of CBEO on the cell cycle, apoptosis, and these signaling pathways by flow cytometry and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay using MAPKs inhibitors. CBEO induced a significant increase in the sub-G1 peak, as well as biochemical and morphological changes characteristic of apoptosis. The in-silico results indicated that EZ interacts with Extracellular Signal-Regulated Kinase 1 (ERK1), c-Jun N-terminal Kinase 1 (JNK1), p38α MAPK, NF-κB, and PKB/AKT. Moreover, CBEO modulated the ERK1/2, JNK, p38 MAPK, NF-κB, and PKB/AKT activities in SK-MEL-28 cells. Furthermore, CBEO's cytotoxicity against SK-MEL-28 cells was significantly altered in the presence of MAPKs inhibitors. These findings support the in vitro antimelanoma effect of CBEO through apoptosis induction, and the modulation of ERK, JNK, p38 MAPK, NF-κB, and PKB/AKT activities.

Identification and verification of ferroptosis-related genes in diabetic foot using bioinformatics analysis

Ferroptosis is a novel form of cell death that plays a key role in several diseases, including inflammation and tumours; however, the role of ferroptosis-related genes in diabetic foot remains unclear. Herein, diabetic foot-related genes were downloaded from the Gene Expression Omnibus and the ferroptosis database (FerrDb). The least absolute shrinkage and selection operator regression algorithm was used to construct a related risk model, and differentially expressed genes were analysed through immune infiltration. Finally, we identified relevant core genes through a protein-protein interaction network, subsequently verified using immunohistochemistry. Comprehensive analysis showed 198 genes that were differentially expressed during ferroptosis. Based on functional enrichment analysis, these genes were primarily involved in cell response, chemical stimulation, and autophagy. Using the CIBERSORT algorithm, we calculated the immune infiltration of 22 different types of immune cells in diabetic foot and normal tissues. The protein-protein interaction network identified the hub gene TP53, and according to immunohistochemistry, the expression of TP53 was high in diabetic foot tissues but low in normal tissues. Accordingly, we identified the ferroptosis-related gene TP53 in the diabetic foot, which may play a key role in the pathogenesis of diabetic foot and could be used as a potential biomarker.

Computed cancer interactome explains the effects of somatic mutations in cancers

Protein-protein interactions (PPIs) are involved in almost all essential cellular processes. Perturbation of PPI networks plays critical roles in tumorigenesis, cancer progression, and metastasis. While numerous high-throughput experiments have produced a vast amount of data for PPIs, these data sets suffer from high false positive rates and exhibit a high degree of discrepancy. Coevolution of amino acid positions between protein pairs has proven to be useful in identifying interacting proteins and providing structural details of the interaction interfaces with the help of deep learning methods like AlphaFold (AF). In this study, we applied AF to investigate the cancer protein-protein interactome. We predicted 1,798 PPIs for cancer driver proteins involved in diverse cellular processes such as transcription regulation, signal transduction, DNA repair, and cell cycle. We modeled the spatial structures for the predicted binary protein complexes, 1,087 of which lacked previous 3D structure information. Our predictions offer novel structural insight into many cancer-related processes such as the MAP kinase cascade and Fanconi anemia pathway. We further investigated the cancer mutation landscape by mapping somatic missense mutations (SMMs) in cancer to the predicted PPI interfaces and performing enrichment and depletion analyses. Interfaces enriched or depleted with SMMs exhibit different preferences for functional categories. Interfaces enriched in mutations tend to function in pathways that are deregulated in cancers and they may help explain the molecular mechanisms of cancers in patients; interfaces lacking mutations appear to be essential for the survival of cancer cells and thus may be future targets for PPI modulating drugs.

Proteins implicated in muscular dystrophy and cancer are functional constituents of the centrosome

This study demonstrates that the muscular dystrophy-associated proteins dystrophin, utrophin, dysferlin, and calpain-3 localize to the centrosome and that their absence leads to excess centrosomes, compromised nuclear morphology, impaired centrosome orientation, and defective microtubule nucleation.

Aberrant expression of dystrophin, utrophin, dysferlin, or calpain-3 was originally identified in muscular dystrophies (MDs). Increasing evidence now indicates that these proteins might act as tumor suppressors in myogenic and non-myogenic cancers. As DNA damage and somatic aneuploidy, hallmarks of cancer, are early pathological signs in MDs, we hypothesized that a common pathway might involve the centrosome. Here, we show that dystrophin, utrophin, dysferlin, and calpain-3 are functional constituents of the centrosome. In myoblasts, lack of any of these proteins caused excess centrosomes, centrosome misorientation, nuclear abnormalities, and impaired microtubule nucleation. In dystrophin double-mutants, these defects were significantly aggravated. Moreover, we demonstrate that also in non-myogenic cells, all four MD-related proteins localize to the centrosome, including the muscle-specific full-length dystrophin isoform. Therefore, MD-related proteins might share a convergent function at the centrosome in addition to their diverse, well-established muscle-specific functions. Thus, our findings support the notion that cancer-like centrosome-related defects underlie MDs and establish a novel concept linking MDs to cancer.

Genome-Wide Population Structure and Selection Signatures of Yunling Goat Based on RAD-seq

Simple Summary

Goats are important domestic animals that provide meat, milk, fur, and other products for humans. The demand for these products has increased in recent years. Disease resistance among goat breeds is different, but the genetic basis of the differences in resistance to diseases is still unclear and needs to be further studied. In this study, many genes and pathways related to immunity and diseases were identified to be under positive selection between Yunling and Nubian goats using RAD-seq technology. This study on the selection signatures of Yunling goats provides the scientific basis and technical support for the breeding of domestic goats for disease resistance, which has important social and economic significance.

Abstract

Animal diseases impose a huge burden on the countries where diseases are endemic. Conventional control strategies of vaccines and veterinary drugs are to control diseases from a pharmaceutical perspective. Another alternative approach is using pre-existing genetic disease resistance or tolerance. We know that the Yunling goat is an excellent local breed from Yunnan, southwestern China, which has characteristics of strong disease resistance and remarkable adaptability. However, genetic information about the selection signatures of Yunling goats is limited. We reasoned that the genes underlying the observed difference in disease resistance might be identified by investigating selection signatures between two different goat breeds. Herein, we selected the Nubian goat as the reference group to perform the population structure and selection signature analysis by using RAD-seq technology. The results showed that two goat breeds were divided into two clusters, but there also existed gene flow. We used Fst (F-statistics) and π (pi/θπ) methods to carry out selection signature analysis. Eight selected regions and 91 candidate genes were identified, in which some genes such as DOK2, TIMM17A, MAVS, and DOCK8 related to disease and immunity and some genes such as SPEFI, CDC25B, and MIR103 were associated with reproduction. Four GO (Gene Ontology) terms (GO:0010591, GO:001601, GO:0038023, and GO:0017166) were associated with cell migration, signal transduction, and immune responses. The KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathways were mainly associated with immune responses, inflammatory responses, and stress reactions. This study preliminarily revealed the genetic basis of strong disease resistance and adaptability of Yunling goats. It provides a theoretical basis for the subsequent genetic breeding of disease resistance of goats.

Bioinformatic Analysis of Prognostic Value of SNTG2 with Immune Implications in Lung Adenocarcinoma

Background

Lung cancer is the most morbid and fatal cancer in the world, and nearly 85% of lung cancer is non-small cell lung cancer (NSCLC). Besides traditional chemotherapies, molecular targeted therapies and immunotherapies are increasing rapidly, but the treatment is still unsatisfactory. The study is to identify a new diagnostic and prognostic biomarker.

Methods

Data including mRNA expression and clinical information of lung adenocarcinoma (LUAD) patients were downloaded from The Cancer Genome Atlas (TCGA) database. Receiver operating characteristic (ROC) curve was constructed to assess the diagnostic value of syntrophin-γ2 (SNTG2) expression and Kaplan-Meier (KM) survival curves were used to compare the survival disparities. The protein-protein interaction (PPI) network was analysed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the connections between SNTG2 and immune cell infiltration were found with Tumor Immunoassay Resource (TIMER). Genetic mutation in SNTG2 and its association with overall survival (OS) were evaluated by cBioPortal. The relationship between SNTG2 and methylation and its association with overall survival were evaluated by MethSurv. Chi square and Mann-Whitney tests were used for statistical analyses. Xiantao Academic Online Website was used for online analysis.

Results

Our results revealed that SNTG2 mRNA expression was lower in LUAD tissues than in both adjacent and non-adjacent normal tissues and low SNTG2 mRNA expression was verified to be correlated with histological grade, clinical stage, first therapy outcome and poor overall survival of LUAD. Next, ROC curve revealed diagnostic and prognostic value of SNTG2 for LUAD patients. Moreover, SNTG2 presented correlation with immune cell infiltration and immune checkpoints. Then, we revealed CC chemokine ligand 14 (CCL14), a co-expression gene with SNTG2, which has consistent influence with SNTG2. Furthermore, hypomethylation was found to be associated with high SNTG2 expression.

Conclusion

We revealed a potential diagnostic and prognostic indicator in LUAD and analyzed its influence on immunotherapy.

A Genomic Signature Reflecting Fibroblast Infiltration Into Gastric Cancer Is Associated With Prognosis and Treatment Outcomes of Immune Checkpoint Inhibitors

Background: The immunotherapy efficacy in gastric cancer (GC) is limited. Cancer-associated fibroblasts (CAFs) induce primary resistance to immunotherapy. However, CAF infiltration in tumors is difficult to evaluate due to the lack of validated and standardized quantified methods. This study aimed to investigate the impact of infiltrating CAFs alternatively using fibroblast-associated mutation scoring (FAMscore). Methods: In a GC cohort from Affiliated Hospital of Jiangsu University (AHJU), whole exon sequencing of genomic mutations, whole transcriptome sequencing of mRNA expression profiles, and immunofluorescence staining of tumor-infiltrating immune cells were performed. GC data from The Cancer Genome Atlas were used to identify genetic mutations which were associated with overall survival (OS) and impacted infiltrating CAF abundance determined by transcriptome-based estimation. FAMscore was then constructed through a least absolute shrinkage and selection operator Cox regression model and further validated in AHJU. The predictive role of FAMscore for immunotherapy outcomes was tested in 1 GC, one melanoma, and two non-small-cell lung cancer (NSCLC-1 and -2) cohorts wherein participants were treated by immune checkpoint inhibitors. Results: FAMscore was calculated based on a mutation signature consisting of 16 genes. In both TCGA and AHJU, a high FAMscore was an independent predictor for poor OS of GC patients. FAMscore was associated with immune-associated genome biomarkers, immune cell infiltration, and signaling pathways of abnormal immunity. Importantly, patients with high FAMscore presented inferiority in the objective response rate of immunotherapy compared to those with low FAMscore, with 14.6% vs. 66.7% (p<0.001) in GC, 19.6% vs. 68.2% (p<0.001) in NSCLC-1, 23.1% vs 75% (p = 0.007) in NSCLC-2, and 40.9% vs 75% (p = 0.037) in melanoma. For available survival data, a high FAMscore was also an independent predictor of poor progression-free survival in NSCLC-1 (HR = 2.55, 95% CI: 1.16-5.62, p = 0.02) and NSCLC-2 (HR = 5.0, 95% CI: 1.13-22.19, p = 0.034) and poor OS in melanoma (HR = 3.48, 95% CI: 1.27-9.55, p = 0.015). Conclusions: Alternative evaluation of CAF infiltration in GC by determining the FAMscore could independently predict prognosis and immunotherapy outcomes. The FAMscore may be used to optimize patient selection for immunotherapy.

Identification of Novel Biomarkers With Diagnostic Value and Immune Infiltration in Burn Injury

Burn injury is an intractable problem in the field of surgery where screening relevant target genes and exploring pathological mechanisms through bioinformatic methods has become a necessity. Herein, we integrated three burn injury mRNA microarray datasets from the Gene Expression Omnibus database to analyze the hub differentially expressed genes (DEGs) between burn injury patient samples and healthy human samples; we conducted multiple functional enrichment analyses and constructed the protein-protein interaction (PPI) network. Finally, we evaluated the immune infiltration in the burn injury microenvironment. A total of 84 intersection DEGs (32 upregulated and 52 downregulated) were screened in burn injury patients via integrated analyses. Upregulated genes were primarily enriched in regulation of T cell activation, regulation of response to DNA damage stimulus, positive regulation of innate immune response, positive regulation of defense response. We also identified 10 hub genes from the PPI network (CCNB2, MYO10, TTK, POLQ, VASP, TIMP1, CDK16, MMP1, ZYX, and PKMYT1). Next, we found that 22 immune cells were substantially changed during the burn injury by CIBERSORT. In addition, we verified that VASP and POLQ are two novel diagnostic markers in burn processes with high diagnostic efficacy via immunohistochemistry. In summary, we identified several key genes involved in burn injury and provided a favorable basis for elucidating the molecular mechanisms of burn injury through comprehensive bioinformatic analysis.

Senescence-associated tumor growth is promoted by 12-Lipoxygenase

Radiation therapy is a commonly used treatment modality for cancer. Although effective in providing local tumor control, radiation causes oxidative stress, inflammation, immunomodulatory and mitogenic cytokine production, extracellular matrix production, and premature senescence in lung parenchyma. The senescence associated secretory phenotype (SASP) can promote inflammation and stimulate alterations in the surrounding tissue. Therefore, we hypothesized that radiation-induced senescent parenchymal cells in irradiated lung would enhance tumor growth. Using a murine syngeneic tumor model of melanoma and non-small cell lung cancer lung metastasis, we demonstrate that radiation causes a significant increase in markers of premature senescence in lung parenchyma within 4 to 8 weeks. Further, injection of B16F0 (melanoma) or Lewis Lung carcinoma (epidermoid lung cancer) cells at these time points after radiation results in an increase in the number and size of pulmonary tumor nodules relative to unirradiated mice. Treatment of irradiated mice with a senolytic agent (ABT-737) or agents that prevent senescence (rapamycin, INK-128) was sufficient to reduce radiation-induced lung parenchymal senescence and to mitigate radiation-enhanced tumor growth. These agents abrogated radiation-induced expression of 12-Lipoxygenase (12-LOX), a molecule implicated in several deleterious effects of senescence. Deficiency of 12-LOX prevented radiation-enhanced tumor growth. Together, these data demonstrate the pro-tumorigenic role of radiation-induced senescence, introduces the dual TORC inhibitor INK-128 as an effective agent for prevention of radiation-induced normal tissue senescence, and identifies senescence-associated 12-LOX activity as an important component of the pro-tumorigenic irradiated tissue microenvironment. These studies suggest that combining senotherapeutic agents with radiotherapy may decrease post-therapy tumor growth.

Identification and Verification of Five Potential Biomarkers Related to Skin and Thermal Injury Using Weighted Gene Co-Expression Network Analysis

Background: Burn injury is a life-threatening disease that does not have ideal biomarkers. Therefore, this study first applied weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening methods to identify pivotal genes and diagnostic biomarkers associated with the skin burn process.

Methods: After obtaining transcriptomic datasets of burn patient skin and normal skin from Gene Expression Omnibus (GEO) and performing differential analysis and functional enrichment, WGCNA was used to identify hub gene modules associated with burn skin processes in the burn patient peripheral blood sample dataset and determine the correlation between modules and clinical features. Enrichment analysis was performed to identify the functions and pathways of key module genes. Differential analysis, WGCNA, protein-protein interaction analysis, and enrichment analysis were utilized to screen for hub genes. Hub genes were validated in two other GEO datasets, tested by immunohistochemistry for hub gene expression in burn patients, and receiver operating characteristic curve analysis was performed. Finally, we constructed the specific drug activity, transcription factors, and microRNA regulatory network of the five hub genes.

Results: A total of 1,373 DEGs in GSE8056 were obtained, and the top 5 upregulated genes were S100A12, CXCL8, CXCL5, MMP3, and MMP1, whereas the top 5 downregulated genes were SCGB1D2, SCGB2A2, DCD, TSPAN8, and KRT25. DEGs were significantly enriched in the immunity, epidermal development, and skin development processes. In WGCNA, the yellow module was identified as the most closely associated module with tissue damage during the burn process, and the five hub genes (ANXA3, MCEMP1, MMP9, S100A12, and TCN1) were identified as the key genes for burn injury status, which consistently showed high expression in burn patient blood samples in the GSE37069 and GSE13902 datasets. Furthermore, we verified using immunohistochemistry that these five novel hub genes were also significantly elevated in burn patient skin. In addition, MCEMP1, MMP9, and S100A12 showed perfect diagnostic performance in the receiver operating characteristic analysis.

Conclusion: In conclusion, we analyzed the changes in genetic processes in the skin during burns and used them to identify five potential novel diagnostic markers in blood samples from burn patients, which are important for burn patient diagnosis. In particular, MCEMP1, MMP9, and S100A12 are three key blood biomarkers that can be used to identify skin damage in burn patients.

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.

The Identification of the Metabolism Subtypes of Skin Cutaneous Melanoma Associated With the Tumor Microenvironment and the Immunotherapy

Skin cutaneous melanoma (SKCM) is a highly aggressive and resistant cancer with immense metabolic heterogeneity. Here, we performed a comprehensive examination of the diverse metabolic signatures of SKCM based on non-negative matrix factorization (NMF) categorization, clustering SKCM into three distinct metabolic subtypes (C1, C2, and C3). Next, we evaluated the metadata sets of the metabolic signatures, prognostic values, transcriptomic features, tumor microenvironment signatures, immune infiltration, clinical features, drug sensitivity, and immunotherapy response of the subtypes and compared them with those of prior publications for classification. Subtype C1 was associated with high metabolic activity, low immune scores, and poor prognosis. Subtype C2 displayed low metabolic activity, high immune infiltration, high stromal score, and high expression of immune checkpoints, demonstrating the drug sensitivity to PD-1 inhibitors. The C3 subtype manifested moderate metabolic activity, high enrichment in carcinogenesis-relevant pathways, high levels of CpG island methylator phenotype (CIMP), and poor prognosis. Eventually, a 90-gene classifier was produced to implement the SKCM taxonomy and execute a consistency test in different cohorts to validate its reliability. Preliminary validation was performed to ascertain the role of SLC7A4 in SKCM. These results indicated that the 90-gene signature can be replicated to stably identify the metabolic classification of SKCM. In this study, a novel SKCM classification approach based on metabolic gene expression profiles was established to further understand the metabolic diversity of SKCM and provide guidance on precisely targeted therapy to patients with the disease.