The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer

Conformation-sensitive targeting of CD18 depletes M2-like tumor-associated macrophages resulting in inhibition of solid tumor progression

BACKGROUND

Tumor-associated macrophages (TAMs) primarily exist in the M2-like phenotype in the tumor microenvironment (TME). M2-TAMs contribute to tumor progression by establishing an immunosuppressive environment. However, TAM targeting is hindered, mainly owing to a lack of specific biomarkers for M2-TAMs. Previously, we demonstrated that a novel peptide drug conjugate (TB511) consisting of a TAM-binding peptide and the apoptosis-promoting peptide targets M2-TAMs. This was achieved through M2-TAM targeting, although the target mechanism of action remained elusive. Herein, we elucidate the anticancer efficacy of TB511 by identifying new target proteins that preferentially bind to M2-TAMs and clarifying the apoptosis-inducing mechanism in these cells.

METHODS

We investigated the target proteins and binding site of TB511 using LC-MS/MS analyses, surface plasmon resonance and peptide-protein interaction 3D modeling. Activated CD18 expression in M2 TAMs was assessed using Quantibrite PE beads in PBMCs. The anticancer efficacy of TB511 was tested using colorectal cancer (CRC) and non-small cell lung cancer (NSCLC) mouse model. The immunotherapeutic effect of TB511 was investigated through spatial transcriptomics in human pancreatic ductal adenocarcinoma (PDAC) model.

RESULTS

Activated CD18 was highly expressed in human tumor tissues and was significantly higher in M2 TAMs than in other immune cells. TB511 showed high binding affinity to CD18 among the cell membrane proteins of M2 macrophages and appeared to bind to the cysteine-rich domain in the activated form. Moreover, TB511 specifically induced apoptosis in M2 TAMs, but its targeting ability to M2 macrophages was inhibited in CD18 blockade or knockout model. In mouse or humanized mouse models of solid tumors such as CRC, NSCLC, and PDAC, TB511 suppressed tumor growth by targeting M2-TAMs via CD18 and enhancing the presence of CD8+ T cells in the TME.

CONCLUSIONS

Collectively, our findings suggest that activated CD18 holds promise as a novel target protein for cancer therapy, and TB511 shows potential as a therapeutic agent for tumor treatment.

Metformin as an immunomodulatory agent in enhancing head and neck squamous cell carcinoma therapies

Head and neck squamous cell carcinoma (HNSCC) remains a significant clinical challenge due to its aggressive behavior and poor prognosis, making the development of novel therapeutics with enhanced efficacy and minimal side effects critical. Metformin, a widely used antidiabetic agent, has recently emerged as a potential adjunctive therapy for HNSCC, exhibiting both direct anti-tumor and immunomodulatory effects. This review comprehensively explores the multifaceted role of metformin in shaping the tumor immune microenvironment within HNSCC. We emphasize its pivotal role in modulating immune cell populations and its potential for synergistic action with immunotherapeutic strategies. Furthermore, we address the current challenges associated with optimizing dosing regimens, identifying predictive biomarkers, and integrating metformin with immunotherapy. By dissecting these aspects, this review aims to pave the way for the development of personalized HNSCC treatment strategies that fully exploit the therapeutic potential of metformin.

The aryl hydrocarbon receptor controls IFN-γ-induced immune checkpoints PD-L1 and IDO via the JAK/STAT pathway in lung adenocarcinoma

While immunotherapy has shown some efficacy in lung adenocarcinoma (LUAD) patients, many respond only partially or not at all. One limitation in improving outcomes is the lack of a complete understanding of immune checkpoint regulation. Here, we investigated a possible link between an environmental chemical receptor implicated in lung cancer and immune regulation, the AhR, a known but counterintuitive mediator of immunosuppression (interferon (IFN)-γ), and regulation of two immune checkpoints (PD-L1 and IDO). AhR gene-edited LUAD cell lines, a syngeneic LUAD mouse model, bulk and scRNA sequencing of LUADs and tumor-infiltrating T cells were used to map out a signaling pathway leading from IFN-γ through the AhR to JAK/STAT, PD-L1, IDO, and tumor-mediated immunosuppression. The data demonstrate that: (1) IFN-γ activation of the JAK/STAT pathway leading to PD-L1 and IDO1 up-regulation is mediated by the AhR in murine and human LUAD cells, (2) AhR-driven IDO1 induction results in the production of Kynurenine (Kyn), an AhR ligand, which likely mediates an AhR→IDO1→Kyn→AhR amplification loop, (3) transplantation of AhR-knockout LUAD cells results in long-term tumor immunity in most recipients. (4) The 23% of AhR-knockout tumors that do grow do so at a much slower pace than controls and exhibit higher densities of CD8+ T cells expressing markers of immunocompetence, increased activity, and increased cell-cell communication. The data definitively link the AhR to IFN-γ-induced JAK/STAT pathway and immune checkpoint-mediated immunosuppression and support the targeting of the AhR in the context of LUAD.

Role of ITGB2 protein structure and molecular mechanism in precancerous lesions of gastric cancer: Influencing the occurrence and development of cancer through the CXCL1-CXCR2 axis

Gastric cancer is a prevalent gastrointestinal tumor. In the classical cascade of gastric cancer development, the gradual progression from non-atrophic gastritis, atrophic gastritis, intestinal metaplasia, to intraepithelial neoplasia eventually leads to early gastric cancer. We investigated the proteomic characteristics of chronic gastritis (CG), low-grade intraepithelial neoplasia (low-grade LGIN), and early gastric cancer (EGC). Additionally, we utilized transcriptomic databases to explore the expression patterns of ITGB2 across different stages of gastric tissue and its correlation with the prognosis of gastric cancer. The expression of ITGB2 was confirmed in cytological experiments, revealing that ITGB2 can influence the onset and progression of gastric cancer via the CXCL1-CXCR2 axis. This finding suggests that ITGB2 represents a novel biomarker for gastric cancer, making it a potential target for accurate diagnosis and treatment.

Significance of CD8+T cells related gene ITGB2 in prognosis and tumor microenvironment of small cell lung cancer

The CD8 + T cells could enhance response of antitumor immune in cancers. Therefore, we aimed to analyze the CD8 + T cells related genes in small cell lung cancer (SCLC) patients. The data of SCLC samples were collected from the Gene Expression Omnibus database. The hub genes were screened by weighted gene co-expression network analysis, protein-protein interaction network and survival analyses. Moreover, the relative proportions of the 22 immune cells in the samples were calculated using CIBERSORT software. The relationship between target gene and immunotherapy was analyzed in IMvigor210 cohort. We identified 10 genes (PTPRC, RPS27A, UBA52, CD8A, ITGB2, GNB2L1, TYROBP, CD86, TLR4, and FCGR3A) that were correlated with CD8 + T cells in SCLC. Among them, ITGB2 was positively correlated with CD8 + T cells in SCLC. ITGB2 was down-regulated in SCLC. SCLC patients with low ITGB2 expression exhibited a poor prognosis, and ITGB2 was an independent prognostic factor for survival rate of SCLC patients. The differentially expressed genes between ITGB2high and ITGB2low groups were significantly enriched in 143 signaling pathways. We also discovered that the ImmuneScore, StromalScore, and the expression of immune checkpoints (PD-1 (PDCD1), CTLA4, PDL-1 (CD274), TIGIT, IFNG, GZMA, TBX2, and IDO1) were significantly increased in ITGB2high group. Moreover, SCLC patients with high ITGB2 expression had lower tumor immune dysfunction and exclusion scores, and the proportion of urothelial cancer patients with complete response/partial response was observably decreased in ITGB2high group. Finally, we found that ITGB2 was correlated with IC50 of cancer drugs. In conclusion, SCLC patients with low ITGB2 expression exhibited worse prognosis. ITGB2 might be correlated with immunotherapy response of SCLC patients.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

CD2 glycoprotein and CD44 structure and prevention of diabetes nephropathy: Central characteristics of related genes based on WGCNA and PPI

Diabetic nephropathy (DN) is a prevalent complication of diabetes mellitus, characterized by complex pathogenesis that involves numerous molecules and signaling pathways. Among these, CD2 glycoprotein and CD44 play pivotal roles in cell adhesion, signal transduction, and inflammatory responses, potentially contributing significantly to the onset and progression of DN. This study aimed to investigate the central features of CD2 glycoprotein and CD44 in preventing diabetic nephropathy. To achieve this, kidney tissue sample data from DN patients were sourced from a public gene expression database. The roles of CD2 glycoprotein and CD44 within the PPI network were then analyzed, focusing on their interactions with other related genes. WGCNA analysis identified several significant gene modules associated with DN, including CD2 glycoprotein and CD44. PPI network analysis showed that these two proteins had a high degree of connectivity in the network, suggesting that they may be central regulatory molecules of DN. Further functional enrichment analysis revealed the potentially important role of CD2 glycoprotein and CD44 in diabetic nephropathy.

Proteo-genomic analyses in relatively lean Chinese adults identify proteins and pathways that affect general and central adiposity levels

Adiposity is an established risk factor for multiple diseases, but the causal relationships of different adiposity types with circulating protein biomarkers have not been systematically investigated. We examine the causal associations of general and central adiposity with 2923 plasma proteins among 3977 Chinese adults (mean BMI = 23.9 kg/m²). Genetically-predicted body mass index (BMI), body fat percentage (BF%), waist circumference (WC), and waist-to-hip ratio (WHR) are significantly (FDR < 0.05) associated with 399, 239, 436, and 283 proteins, respectively, with 80 proteins associated with all four and 275 with only one adiposity trait. WHR is associated with the most proteins (n = 90) after adjusting for other adiposity traits. These associations are largely replicated in Europeans (mean BMI = 27.4 kg/m²). Two-sample Mendelian randomisation (MR) analyses in East Asians using cis-protein quantitative trait locus (cis-pQTLs) identified in GWAS find 30/2 proteins significantly affect levels of BMI/WC, respectively, with 10 showing evidence of colocalisation, and seven (inter-alpha-trypsin inhibitor heavy chain H3, complement factor B, EGF-containing fibulin-like extracellular matrix protein 1, thioredoxin domain-containing protein 15, alpha-2-antiplasmin, fibronectin, mimecan) are replicated in separate MR using different cis-pQTLs identified in Europeans. These findings identified potential novel mechanisms and targets, to our knowledge, for improved treatment and prevention of obesity and associated diseases.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

MATERIALS AND METHODS

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

RESULTS

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

CONCLUSION

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Transcriptional switches in melanoma T Cells: Facilitating polarizing into regulatory T cells

Melanoma is a malignant skin tumor with a high mortality rate. Regulatory T cells (Tregs) are immune cells with immunosuppressive roles, however, the precise mechanisms governing Treg involvement in melanoma remain enigmatic. Experimental findings unveiled different transcription factor switches between normal and tumor T cell, with heightened FOXP3 and BATF in the latter. These factors induced immunosuppressive molecules and Treg maintenance genes, polarizing tumor T cells into Tregs. Spatial transcriptomics illuminated the preferential settlement of Tregs at the melanoma periphery. Within this context, FOXP3 in Tregs facilitated direct enhancement of specific ligand gene expression, fostering communication with neighboring cells. Novel functional molecules bound to FOXP3 or BATF in Tregs, such as SPOCK2, SH2D2A, and ligand molecules ITGB2, LTA, CLEC2C, CLEC2D, were discovered, which had not been previously reported in melanoma Treg studies. Furthermore, we validated our findings in a large number of clinical samples and identified the Melanoma Treg-Specific Regulatory Tag Set (Mel TregS). ELISA analysis showed that the protein levels of Mel TregS in melanoma Tregs were higher than in normal Tregs. We then utilized SERS technology to measure the signal values of Mel TregS in exosome, and successfully discriminated between healthy individuals and melanoma patients, as well as early and late-stage patients. This approach significantly enhanced detection sensitivity. In sum, our research elucidated fresh insights into the mechanisms governing Treg self-maintenance and communication with surrounding cells in melanoma. We also introduced an innovative method for clinical disease monitoring through SERS technology.

The Aryl Hydrocarbon Receptor Controls IFNγ-Induced Immune Checkpoints PD-L1 and IDO via the JAK/STAT Pathway in Lung Adenocarcinoma

While immunotherapy has shown efficacy in lung adenocarcinoma (LUAD) patients, many respond only partially or not at all. One limitation in improving outcomes is the lack of a complete understanding of immune checkpoint regulation. Here, we investigated a possible link between an environmental chemical receptor implicated in lung cancer and immune regulation, (the aryl hydrocarbon receptor/AhR), a known but counterintuitive mediator of immunosuppression (IFNγ), and regulation of two immune checkpoints (PD-L1 and IDO). AhR gene-edited LUAD cell lines, a syngeneic LUAD mouse model, bulk- and scRNA sequencing of LUADs and tumor-infiltrating leukocytes were used to map out a signaling pathway leading from IFNγ through the AhR to JAK/STAT, PD-L1, IDO, and tumor-mediated immunosuppression. The data demonstrate that: 1) IFNγ activation of the JAK/STAT pathway leading to PD-L1 and IDO1 upregulation is mediated by the AhR in murine and human LUAD cells, 2) AhR-driven IDO1 induction results in the production of Kynurenine (Kyn), an AhR ligand, which likely mediates an AhR→IDO1→Kyn→AhR amplification loop, 3) transplantation of AhR-knockout LUAD cells results in long-term tumor immunity in most recipients. 4) The 23% of AhR-knockout tumors that do grow do so at a much slower pace than controls and exhibit higher densities of CD8+ T cells expressing markers of immunocompetence, increased activity, and increased cell-cell communication. The data definitively link the AhR to IFNγ-induced JAK/STAT pathway and immune checkpoint-mediated immunosuppression and support the targeting of the AhR in the context of LUAD.

Transcriptome Profiling of Mouse Embryonic Fibroblast Spontaneous Immortalization: A Comparative Analysis

Cell immortalization, a hallmark of cancer development, is a process that cells can undergo on their path to carcinogenesis. Spontaneously immortalized mouse embryonic fibroblasts (MEFs) have been used for decades; however, changes in the global transcriptome during this process have been poorly described. In our research, we characterized the poly-A RNA transcriptome changes after spontaneous immortalization. To this end, differentially expressed genes (DEGs) were screened using DESeq2 and characterized by gene ontology enrichment analysis and protein-protein interaction (PPI) network analysis to identify the potential hub genes. In our study, we identified changes in the expression of genes involved in proliferation regulation, cell adhesion, immune response and transcriptional regulation in immortalized MEFs. In addition, we performed a comparative analysis with previously reported MEF immortalization data, where we propose a predicted gene regulatory network model in immortalized MEFs based on the altered expression of Mapk11, Cdh1, Chl1, Zic1, Hoxd10 and the novel hub genes Il6 and Itgb2.

Identification of potential core genes in pseudoexfoliation using bioinformatics analysis and retrospective analysis of 84 patients with this disease

BACKGROUND

Pseudoexfoliation (XFS) is a common cause of glaucoma in nowadays. Because of XFS causing irreversible blindness secondary to glaucoma (XFG), this study aims to identify the current prevalence of XFS among Xinjiang Province of China, and identify the hub genes involved in XFS.

METHODS

A retrospective chart review was conducted from 2007 to 2019 for patients aged 50 and older. All patients with XFS or XFG diagnosed by slit lamp exam were identified through chart review.

RESULTS

Of the 84 patient charts available for review, 50% of the patients identified as male, with a mean age of 67 years. The top ten genes evaluated by connectivity degree in the PPI network were identified. The results showed that Tyrobp was the most outstanding gene, followed by Ptprc, Fcgr3, Itgb2, Emr1, Cd68, Syk, Fcerlg, Hck, and Lyz2. All of these hub genes were downregulated in XFS.

CONCLUSION

Our findings show a considerably biomarkers of XFS for diagnosis and treatment.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

Characterization of mRNA Signature in Milk Small Extracellular Vesicles from Cattle Infected with Bovine Leukemia Virus

This study aimed to characterize the mRNA signature of milk small extracellular vesicles (sEVs) from BLV-infected cattle. A total of 23 mRNAs, which showed greater abundance in milk sEVs from BLV-infected cattle compared to those from BLV-uninfected (control) cattle, were identified through microarray analyses conducted in our previous study. To assess the significance of these differences in mRNA abundance, milk was collected from six control cattle and twenty-six cattle infected with BLV. The infected cattle were categorized into two distinct groups based on their proviral loads: a group of eight cattle with low proviral loads (LPVL), characterized by <10,000 copies per 105 white blood cells (WBC), and a group of eighteen cattle with high proviral loads (HPVL), marked by ≥10,000 copies per 105 WBC. The qPCR analysis quantified 7 out of 23 mRNAs, including BoLA, CALB1, IL33, ITGB2, MYOF, TGFBR1, and TMEM156, in the milk sEVs from control cattle, LPVL cattle, and HPVL cattle. Significantly, the average relative expression of CALB1 mRNA in milk sEVs was higher in LPVL cattle compared to HPVL cattle and control cattle (p < 0.05), while it was relatively lower in HPVL cattle compared to LPVL cattle and control cattle (p > 0.05). Likewise, the average relative expression of TMEM156 mRNA in milk sEVs was significantly higher in LPVL cattle compared to HPVL cattle (p < 0.05), and relatively lower in HPVL cattle compared to LPVL cattle and control cattle (p > 0.05). The results indicate distinct patterns of CALB1 and TMEM156 mRNA levels in milk sEVs, with higher levels observed in LPVL cattle and lower levels in HPVL cattle. The current study could provide essential information to comprehend the complexities during the progression of BLV infection and direct the exploration of mRNA biomarkers for monitoring the clinical stage of BLV infection.

Identification of useful biomolecular markers in kidney renal clear cell carcinoma: an in silico and in vitro analysis-based study

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is the most prevalent type of renal cell carcinoma (RCC), with a high incidence and mortality rate. There is a lack of sensitive biomarkers. Therefore, the discovery of accurate biomarkers for KIRC patients is critical to improve prognosis.

METHODS

We determined hub genes and their associated pathways involved in the pathogenesis of KIRC from the GSE66272 dataset consisting of KIRC (n = 26) and corresponding control (n = 26) samples and later validated the expression and methylation level of the identified hub genes on The Cancer Genomic Atlas (TCGA) datasets and Human RCC 786-O and normal HK-2 cell lines through RNA sequencing (RNA-seq), Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and targeted bisulfite sequencing (bisulfite-seq) analyses.

RESULTS

The identified up-regulated four hub genes include TYROBP (Transmembrane Immune Signaling Adaptor TYROBP), PTPRC (Protein tyrosine phosphatase, receptor type, C), LCP2 (Lymphocyte cytosolic protein 2), and ITGB2 (Integrin Subunit Beta 2). Moreover, the higher expression of TYROBP, PTPRC, LCP2, and ITGB2 in KIRC patients insignificantly correlates with a poor prognosis in KIRC patients. In addition, hub genes were involved in the "Fc epsilon RI signaling pathway, asthma, natural cell killer mediated cytotoxicity, T cell receptor signaling pathway, primary immunodeficiency, Fc gamma R-mediated phagocytosis, malaria, leukocyte transendothelial migration, and legionellosis" pathways and associated with the infiltration level of CD8+ T, CD4+ T, and macrophage cells.

CONCLUSION

Our integrated in silico and in vitro analysis identified important hub genes (TYROBP, PTPRC, LCP2, and ITGB2) involved in the pathogenesis of KIRC as possible diagnostic biomarkers.

Amorphous Calcium Carbonate Shows Anti-Cancer Properties That are Attributed to Its Buffering Capacity

AIM

Amorphous calcium carbonate (ACC) is a non-crystalline form of calcium carbonate, and it is composed of aggregated nano-size primary particles. Here, we evaluated its anti-cancer effect postulated relative to its buffering capabilities in lung cancer.

METHODS

Tumors were evaluated in vivo using the Lewis lung carcinoma (LLC) mouse cell line and A549 human lung cancer carcinoma cell line. LLC and A549 cells were injected subcutaneously into the right hind leg of mice. Treatments (ACC, cisplatin, vehicle, and ACC with cisplatin, all given via daily IP injections) started once tumors reached a measurable size. Treatments were carried out for 14 days in the LLC model and for 22 and 24 days in the xenograft model (two experiments). LLC tumors were resected from ACC at the end of the study, and vehicle groups were evaluated for cathepsin B activity. Differential gene expression was carried out on A549 cells following 8 weeks of in vitro culture in the presence or absence of ACC in a culture medium.

RESULTS

The ACC treatment decelerated tumor growth rates in both models. When tumor volumes were compared on the last day of each study, the ACC-treated animal tumor volume was reduced by 44.83% compared to vehicle-treated animals in the LLC model. In the xenograft model, the tumor volume was reduced by 51.6% in ACC-treated animals compared to vehicle-treated animals. A more substantial reduction of 74.75% occurred in the combined treatment of ACC and cisplatin compared to the vehicle (carried out only in the LLC model). Cathepsin B activity was significantly reduced in ACC-treated LLC tumors compared to control tumors. Differential gene expression results showed a shift towards anti-tumorigenic pathways in the ACC-treated A549 cells.

CONCLUSION

This study supports the ACC anti-malignant buffering hypothesis by demonstrating decelerated tumor growth, reduced cathepsin B activity, and altered gene expressions to produce anti-cancerous effects.

Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer

Epithelial ovarian cancer is by far the most lethal gynecological malignancy. The exploration of promising immunomarkers to predict prognosis in ovarian cancer patients remains challenging. In our research, we carried out an integrated bioinformatic analysis of genome expressions and their immune characteristics in the ovarian cancer microenvironment with validation in different experiments. We filtrated 332 differentially expressed genes with 10 upregulated hub genes from the Gene Expression Omnibus database. These genes were closely related to ovarian tumorigenesis. Subsequently, the survival and immune infiltration analysis demonstrated that the upregulation of five candidate genes, ITGB2, VEGFA, CLDN4, OCLN, and SPP1, were correlated with an unfavorable clinical outcome and increased immune cell infiltration in ovarian cancer. Of these genes, ITGB2 tended to be the gene most correlated with various immune cell infiltrations and had a strong correlation with significant M2 macrophages infiltration (r = 0.707, p = 4.71 × 10^-39), while it had a moderate correlation with CD4+/CD8+ T cells and B cells. This characteristic explains why the high expression of ITGB2 was accompanied by immune activation but did not reverse carcinogenesis. Additionally, we confirmed that ITGB2 was over-expressed in ovarian cancer tissues and was mainly located in cytoplasm, detected by Western blotting and the immunohistochemical method. In summary, ITGB2 may serve as a prognostic immunomarker for ovarian cancer patients.

Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning

Background

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.

Methods

Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.

Results

Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.

Conclusion

By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.