Unlocking Exosome-Based Theragnostic Signatures: Deciphering Secrets of Ovarian Cancer Metastasis

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

Background

Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet.

Methods

We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas.

Results

A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue.

Conclusion

A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Alteration of the gut microbiota in patients with lung cancer accompanied by chronic obstructive pulmonary diseases

Aim

To explore the abundance and diversity of the gut microbiota in patients with lung cancer accompanied by chronic obstructive pulmonary disease (LC-COPD).

Methods

The study cohort comprised 15 patients with LC-COPD, 49 patients with lung cancer, and 18 healthy control individuals. ELISA was used to detect inflammatory factors in venous blood. 16S rDNA sequencing was performed to determine the abundance and diversity of the gut microbiota. Gas chromatography-mass spectrometry was used to determine the concentration of short-chain fatty acids (SCFAs) in feces samples.

Results

The α-diversity index indicated that the richness and diversity of the gut microbiota were lower in patients with LC-COPD compared with patients with lung cancer and controls. Principal component analysis revealed significant differences among the three groups (P < 0.05). The linear discriminant analysis effect size algorithm indicated that the o_Lactobacillales, g_Lactobaccillus, f_Lactobaccillaceae, s_Lactobaccillus_oris, c_Bacilli, g_Anaerofustis, s_uncultured organism, and s_bacterium_P1C10 species were prevalent in patients with LC-COPD, while the g_Clostridium_XIVa and g_Butyricicoccus species were prevalent in patients with lung cancer. Furthermore, the concentrations of the SCFAs butyric acid, isobutyric acid, isovaleric acid, and valeric acid tended to be lower in patients with LC-COPD compared with patients with lung cancer and healthy controls, although these intergroup differences were not significant (P > 0.05). Patients with lung cancer had the lowest serum concentration of tumor necrosis factor (TNF)-a. There were no intergroup differences in the concentrations of other inflammatory factors.

Conclusions

The present study indicated that the abundance and structure of the gut microbiota is altered, and the concentrations of SCFAs may be decreased in patients with LC-COPD. In addition, patients with lung cancer had the lowest serum concentration of TNF-a.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients

Background

According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV.

Methods

In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model.

Results

Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples.

Conclusions

Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Identification and immunological role of cuproptosis in osteoporosis

Background

osteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model.

Methods

we investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency.

Results

Differences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis.

Conclusion

Our study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.

Efficacy and safety of compound kushen injection for treating advanced colorectal cancer: A protocol for a systematic review and meta-analysis

Introduction

Compound Kushen Injection (CKI) is a traditional Chinese medicine extracted from Sophora flavescens Aiton and Heterosmilax japonica Kunth. Widely utilized in China for the comprehensive treatment of colorectal cancer (CRC), this study aims to systematically assess the efficacy and safety of CKI when combined with chemotherapy for the treatment of advanced CRC, based on available data.

Methods

Randomized controlled trials investigating the efficacy and safety of CKI combined with chemotherapy in the treatment of advanced CRC will be comprehensively searched from databases, including PubMed, Web of Science, Cochrane Library, EMBASE, China National Knowledge Infrastructure, Chinese Scientific Journal Database, Wanfang, Chinese Biomedicine Database Searches, Chinese Clinical Trial Registry, and ClinicalTrials.gov until November 2022. Two independent reviewers will screen the studies, assess the risk of bias, and extract data in duplicate. The ROB2 tool will be employed to assess the quality of included studies. Stata 16 will be used for data analysis, and publication bias will be assessed using funnel plots and Egger's test. The quality of evidence will be evaluated according to GRADE, and trial sequence analysis (TSA) will be utilized to calculate the final total sample size required for the meta-analysis. The results of this systematic review will be published in a peer-reviewed journal. The proposed review protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42022380106).

Discussion

This systematic review will integrate current evidence on CKI in advanced CRC and analyze the clinical efficacy and safety of CKI combined with different chemotherapy regimens, providing valuable guidance on the use of CKI in CRC patients.

Role of Exosomes in Epithelial-Mesenchymal Transition

Epithelial-mesenchymal transition (EMT) is a fundamental process driving cancer metastasis, transforming non-motile cells into a motile population that migrates to distant organs and forms secondary tumors. In recent years, cancer research has revealed a strong connection between exosomes and the EMT. Exosomes, a subpopulation of extracellular vesicles, facilitate cellular communication and dynamically regulate various aspects of cancer metastasis, including immune cell suppression, extracellular matrix remodeling, metastasis initiation, EMT initiation, and organ-specific metastasis. Tumor-derived exosomes (TEXs) and their molecular cargo, comprising proteins, lipids, nucleic acids, and carbohydrates, are essential components that promote EMT in cancer. TEXs miRNAs play a crucial role in reprogramming the tumor microenvironment, while TEX surface integrins contribute to organ-specific metastasis. Exosome-based cancer metastasis research offers a deeper understanding about cancer and an effective theranostic platform development. Additionally, various therapeutic sources of exosomes are paving the way for innovative cancer treatment development. In this Review, we spotlight the role of exosomes in EMT and their theranostic impact, aiming to inspire cancer researchers worldwide to explore this fascinating field in more innovative ways.