Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer

Breast cancer prediction based on gene expression data using interpretable machine learning techniques

Breast cancer remains a global health burden, with an increase in deaths related to this particular cancer. Accurately predicting and diagnosing breast cancer is important for treatment development and survival of patients. This study aimed to accurately predict breast cancer using a dataset comprising 1208 observations and 3602 genes. The study employed feature selection techniques to identify the most influential predictive genes for breast cancer using machine learning (ML) models. The study used K-nearest Neighbors (KNN), random forests (RF), and a support vector machine (SVM). Furthermore, the study employed feature- and model-based importance and explainable ML methods, including Shapley values, Partial dependency (PDPS), and Accumulated Local Effects (ALE) plots, to explain the genes' importance ranking from the ML methods. Shapley values highlighted the significance of some of the genes in predicting cancer presence. Model-based feature ranking techniques, particularly the Leaving-One-Covariate-In (LOCI) method, identified the ten most critical genes for predicting tumor cases. The LOCI rankings from the SVM and RF methods were aligned. Additionally, visualization methods such as PDPS and ALE plots demonstrated how individual feature changes affect predictions and interactions with other genes. By combining feature selection techniques and explainable ML methods, this study has demonstrated the interpretability and reliability of machine learning models for breast cancer prediction, emphasizing the importance of incorporating explainable ML approaches for medical decision-making.

Kidney transcriptome analysis reveals the molecular responses to salinity adaptation in largemouth bass (Micropterus salmoides)

Recently, against the background of increasing land salinization and global warming, many studies have examined the mechanisms of freshwater fish adaptation to elevated salinity. However, the mechanisms underlying salinity tolerance in the kidney of Micropterus salmoides, a popular saline aquaculture species, remain poorly understood. We used RNA-seq to explore the differentially expressed genes (DEGs) in the kidney of M. salmoides at 0 ‰, 5 ‰, and 10 ‰ salinity for 24 d and 48 d. These DEGs mainly affected metabolism-related pathways, such as secondary metabolite biosynthesis, arachidonic acid metabolism, etc., and immunity-related pathways, such as IL-17 signaling and ECM-receptor interaction. Trend analysis on days 24 and 48 showed that, as salinity increased, the up-regulated genes were notably enriched in the cytochrome P450 xenobiotic metabolic pathway, and down-regulated genes substantially linked to cell cycle, phagosome, etc. More importantly, we identified a total of 22 genes enriched in the cytochrome P450 xenobiotic metabolic pathway, including seven UDP-glucuronosyltransferase genes (UGTs) and five glutathione S-transferase genes (GSTs). We speculated that M. salmoides kidneys removed toxic substances produced due to salinity stress and mitigated oxidative damage by up-regulating UGTs and GSTs, hence maintaining normal physiological function. In addition, genes such as Cystatin A1, significantly up-regulated with increasing salinity stress and duration, favoured the recovery of kidney injury. This research delved into the molecular processes involved in the adaptability of M. salmoides to high salinity stress and provided valuable information for the future breeding of salinity-tolerant strains.

High expression of stearoyl-coenzyme A desaturase in colorectal cancer oncogenic functions and its potential as a therapeutic target

BACKGROUND

The stearoyl-coenzyme A desaturase (SCD) gene influences colorectal cancer (CRC) pathogenesis, with its expression linked to tumor cell survival and resistance, necessitating further investigation into its role in CRC.

AIM

To explore the clinical and pathological significance of SCD expression in CRC tissues and to evaluate the affinity between nitidine chloride (NC) and SCD as a target.

METHODS

Multi-center high-throughput data related to CRC were integrated to calculate the standardized mean difference of SCD mRNA expression levels. Immunohistochemical staining results, Clustered Regularly Interspaced Short Palindromic Repeats knockout screening results of cell growth, and single-cell sequencing were employed to verify the significance of SCD expression in CRC. The clinical and pathological significance of SCD was assessed using pooled receiver operating characteristic curves, sensitivity, specificity, and likelihood ratios. The molecular mechanism of NC against CRC was clarified using the SwissTarget Prediction and functional enrichment, and molecular docking techniques were utilized to explore the targeting affinity between NC and SCD.

RESULTS

Data from 18 platforms, including 2482 CRC samples and 1334 non-cancerous colorectal tissue controls. SCD expression was significantly upregulated in CRC, with a standardized mean difference of 2.05 [95% confidence interval (CI): 1.69-2.41]. The area under the pooled receiver operating characteristic curve was 0.95 (95%CI: 0.92-0.96), with a sensitivity of 0.86 (95%CI: 0.81-0.90) and a specificity of 0.90 (95%CI: 0.87-0.93). Positive and negative likelihood ratios were 9.02 (95%CI: 6.49-12.51) and 0.15 (95%CI: 0.10-0.22), respectively. High SCD protein expression was noted in 208 CRC patients, significantly associated with vascular invasion (P < 0.001). At the single-cell level, SCD was significantly overexpressed in CRC cells (P < 0.001). A total of 33 CRC cell lines depended on SCD for growth. The potential mechanism of NC against CRC might involve modulation of the cell cycle, positioning SCD as a potential target for NC.

CONCLUSION

SCD promotes CRC cell growth and thus acts as an oncogenic factor, making it a potential therapeutic target for NC in CRC treatment.

Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme

Aberrant alternative splicing and abnormal alternative splicing events (ASEs) in glioblastoma multiforme (GBM) remain largely elusive. The prognostic-associated ASEs in GBM were identified and summarized into 123 genes using GBM and LGG datasets from ASCancer Atlas and TCGA. The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. The model was trained on the TCGA-GBM cohort and validated with four external datasets from CGGA and GEO, achieving AUC values of 0.808, 0.814, 0.763, 0.859, and 0.836 for 3-year survival rates, respectively. ASRS could be an independent prognostic factor for GBM patients (HR > 1.8 across three datasets) through multivariate Cox regression analysis. The high-risk group demonstrated poorer prognosis, elevated immune scores, increased levels of immune cell infiltration, and greater differences in drug sensitivity. We found that FN1, used for model construction, contained 4 abnormal ASEs resulting in high expression of non-canonical transcripts and the presence of premature termination codon. These abnormal ASEs may be regulated by tumour-related splicing factors according to the PPI network. Furthermore, both mRNA and protein levels of FN1 were highly expressed in GBM compared to LGG, correlating with poor prognosis in GBM. In conclusion, our findings highlight the role of ASEs in affecting the progression of GBM, and the model showed a potential application for prognostic risk of patients. FN1 may serve as a promising splicing biomarker for GBM, and mechanisms of processes of aberrant splicing need to be revealed in the future.

Deciphering the prognostic impact of aberrant DNA methylation on ANGPT1 gene in breast cancer

Breast cancer (BC) is a multifaceted disease distinguished by a range of molecular subtypes and varying clinical prognoses. The involvement of DNA methylation in the dysregulation of gene expression has been linked to the development and progression of BC. Therefore, this study aimed to investigate the association between ANGPT1 gene expression and DNA methylation in BC patients. Eight Saudi female blood samples were used to undergo for whole genome bisulfite sequencing (WGBS) and RNA sequencing for the identification of novel DNA methylation targets. Several public domain BC datasets including the METABRIC cohort, TCGA, and Kaplan Meier Plotter datasets, were used to explore the prognostic significance of ANGPT1 gene. Then, the demethylation agent 5-aza-2'-deoxycytidine was used to examine the potential association between DNA methylation and ANGPT1 expression. Finally, the validation was conducted on blood samples from 49 Saudi females using methylight techniques. Our results shows that upregulation of ANGPT1 gene expression exhibited hypomethylation pattern in BC samples. these results were confirmed by MCF7 cell line experiments. Demethylating using 5-aza in MCF7 and MCF10A showed a high expression of ANGPT1 in both cell lines. ANGPT1 mRNA expression was found to poor prognostic biomarker and lower Breast Cancer-Specific Survival (BCSS) in BC patients. The potential importance of abnormal DNA methylation in the development and advancement of BC is significant. ANGPT1 may act as an oncogene and could be extensively studied further to behave as a predictive biomarker for breast cancer.

LINC00870 promotes imatinib resistance in gastrointestinal stromal tumor via inhibiting PIGR glycosylation modifications

Imatinib is the first-line targeted therapy for gastrointestinal stromal tumor (GIST), but resistance frequently occurs during treatment, limiting its efficacy and clinical application. We performed high-throughput sequencing of tissue specimens from imatinib-resistant GIST patients, and identified significantly high expression of polymeric immunoglobulin receptor (PIGR) in imatinib-resistant cell lines. Further investigation revealed that PIGR binds specifically to LINC00870. The findings from in vitro cell functional experiments provide evidence of a strong association between LINC00870 and PIGR and the processes of proliferation and metastasis in GIST. Overexpression of LINC00870 in GIST significantly inhibits the glycosylation modification and secretion of the extracellular region of PIGR, leading to immune dysregulation. The inhibition of PIGR or LINC00870 effectively surmounts imatinib resistance. Our study identified PIGR as a critical molecule in regulating GIST imatinib resistance and elucidated the mechanism by which PIGR promotes imatinib resistance through LINC00870 inhibition of PIGR glycosylation modifications. These findings provide a new theoretical basis for blocking drug resistance and improving prognosis in GIST.

Lipid Metabolism in Breast Cancer: From Basic Research to Clinical Application

Breast cancer remains the most prevalent cancer among women globally, with significant links to obesity and lipid metabolism abnormalities. This review examines the role of lipid metabolism in breast cancer progression, highlighting its multifaceted contributions to tumor biology. We discuss key metabolic processes, including fatty acid metabolism, triglyceride metabolism, phospholipid metabolism, and cholesterol metabolism, detailing the reprogramming that occurs in these pathways within breast cancer cells. Alterations in lipid metabolism are emphasized for their roles in supporting energy production, membrane biogenesis, and tumor aggressiveness. Furthermore, we examine how lipid metabolism influences immune responses in the tumor microenvironment, affecting immune cell function and therapeutic efficacy. The potential of lipid metabolism as a target for novel therapeutic strategies is also addressed, with a focus on inhibitors of key metabolic enzymes. By integrating lipid metabolism with breast cancer research, this review underscores the importance of lipid metabolism in understanding breast cancer biology and developing treatment approaches aimed at improving patient outcomes.

COL7A1 indicates crucial potential as a basal membrane-related prognostic biomarker and therapeutic target in lung adenocarcinoma

Introduction

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer. Basal membrane (BM) is important to the invasive processes of LUAD. Our object is to explore hub BM-related genes in LUAD.

Methods

The gene expression data of LUAD were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The weighted gene co-expression network analysis and differentially expressed gene analysis were used to identify candidates. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were used to evaluate their functions. Univariate Cox regression analysis was used to evaluate the prognostic value, and multivariate Cox regression analysis was used to verify its independence as a prognostic risk factor. The qPCR and Western blot were performed to ascertain the hub gene expression. The survival curve of two groups was drawn using Kaplan-Meier method. The hub gene-related immune characteristics were analyzed in independent cohorts by ESTIMATE and CIBERSORT methods.

Results

We successfully identified COL7A1 as a BM-related prognostic biomarker in LUAD, with elevated expression compared to controls, and associated with poor prognosis. Functional enrichment analysis revealed it was involved in pathways related to cell proliferation and inflammation like ECM-receptor interaction. Time-dependent ROC analysis results showed that the AUC of COL7A1 in predicting 1-, 3-, and 5-year survival all exceeded 0.78. Immune infiltration characteristic analysis showed that the higher COL7A1 expression group exhibited lower ESTIMATE scores and higher TIDE scores.

Discussion

Our study identified COL7A1 as a reliable BM-related prognostic biomarker, providing a new reference for the mechanistic understanding and target therapy of LUAD.

Sequential Therapy for Osteosarcoma and Bone Regeneration via Chemodynamic Effect and Cuproptosis Using a 3D-Printed Scaffold with TME-Responsive Hydrogel

Post-surgical recurrence and extensive bone defects pose significant challenges during osteosarcoma treatment. These issues can be addressed using a novel strategy that promotes bone repair after removing residual tumors. Therefore, a 3D-printed porous polylactic acid (PLA) scaffold (PH-GBS@CCP) filled with hydrogel and surface-modified with nano-hydroxyapatite (nHA) is designed. The hydrogel, composed of gelatin modified with methacrylic anhydride (GelMA), sodium alginate (SA), and borax, contains Cu-Cys-PEG nanoparticles (CCP) modified with cRGDfk-PEG2K-DSPE. It is injected into the PLA scaffold and crosslinked under UV. This hydrogel acts as a buffer medium between scaffold and bone, reducing cell abrasion, and as a carrier for the responsive release of tumor-targeting CCP. The scaffold provides the support and microenvironment required for bone repair. In early treatment, the acidic tumor microenvironment promotes hydrogel disintegration and CCP release, depleting glutathione and converting Cu2+ to Cu+ for the Fenton-like reaction. This generates reactive oxygen species, strengthening the proptosis effect, and killing the tumor. In later treatment, after tumor elimination, normalized pH and slow CCP release, along with scaffold nHA, promote osteogenic differentiation, providing a sustained osteogenic effect. Overall, the multifunctional composite scaffold achieved the sequential management of post-surgical osteosarcoma through early tumor-killing and later osteogenic effects.

Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach

MOTIVATION

Spatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical need for the development of highly flexible, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data.

RESULTS

We propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes.

Pan-cancer and experimental analyses reveal the immunotherapeutic significance of CST2 and its association with stomach adenocarcinoma proliferation and metastasis

Purpose

Cystatin 2 (CST2) is a cysteine protease inhibitor, and recent research suggests its potential involvement in cancer development. However, its role in the occurrence, progression, and prognosis of pan-cancer has not been systematically investigated.

Materials and methods

This study comprehensively analyzes the differential expression of CST2 in pan-cancer. The expression distribution patterns of CST2 were examined using single-cell datasets. Furthermore, we conducted a comprehensive evaluation of the correlation between CST2 expression and various factors. These factors include prognosis, immune cell infiltration, immune-related genes, mutations, methylation, tumor mutation burden (TMB), and microsatellite instability (MSI). In addition, we analyzed the sensitivity of drugs dependent on CST2 expression. We utilized gene set enrichment analysis (GSEA) analysis to explore the biological functions of CST2 across different cancer types. Finally, in gastric cancer cell lines, we will investigate the impact of CST2 knockout on expression levels, clonal proliferation, cell apoptosis, and cell migration.

Results

CST2 exhibits abnormal overexpression in multiple tumors. Single-cell analysis reveals high expression of CST2 in fibroblasts. CST2 is closely associated with prognosis, immune cell infiltration, immune-related genes, mutations, methylation, TMB, and MSI. Enrichment analysis demonstrated a significant correlation between CST2 and immune-related pathways. In stomach adenocarcinoma (STAD), CST2-related risk models are associated with prognosis and demonstrate strong predictive capabilities, while also being closely linked to the immune microenvironment. Drug sensitivity analysis indicates the correlation between CST2 and 21 chemotherapy drugs. Finally, experimental validation revealed significantly elevated expression of CST2 in STAD, indicating its role as a driver gene in regulating malignant cell proliferation and migration.

Conclusion

CST2 serves as a potential tumor immune biomarker, playing a critical facilitating role in the proliferation and migration processes of STAD.

Intratumoral microbiota, fatty acid metabolism, and tumor microenvironment constitute an unresolved trinity in colon adenocarcinoma

The intratumoral microbiota, fatty acid metabolism (FAM), and tumor microenvironment (TME) all provide insights into the management of colon adenocarcinoma (COAD). But the biological link among the three remains unclear. Here, we analyzed intratumoral microbiome samples and matched host transcriptome samples from 420 patients with COAD in The Cancer Genome Atlas (TCGA). All patients were divided into two subtypes (FAM_high and FAM_low) based on the Gene set variation analysis (GSVA) score of FAM pathway. Furthermore, we found significant difference in the intratumoral microbiota signatures between the two subtypes. In-depth analysis suggested that specific microbes in tumors may indirectly modify the TME, particularly stromal cell populations, by modulating the FAM process. More importantly, the crosstalk between the three can have a significant impact on prognosis, response to immunotherapy, and drug sensitivity of patients. Pathological image profiling showed that changes in the TME originating from intratumoral microbiota disturbance could be reflected in pathological image features. In summary, our study provides novel insights into the biological links among the intratumoral microbiota, FAM, and the TME in COAD, and offer guidance for the therapeutic opportunities that target intratumoral microbes.

Tumour DNA methylation markers associated with breast cancer survival: a replication study

BACKGROUND

Tumour DNA methylation has been investigated as a potential marker for breast cancer survival, but findings often lack replication across studies.

METHODS

This study sought to replicate previously reported associations for individual CpG sites and multi-CpG signatures using an Australian sample of 425 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS). Candidate methylation sites (N = 22) and signatures (N = 3) potentially associated with breast cancer survival were identified from five prior studies that used The Cancer Genome Atlas (TCGA) methylation dataset, which shares key characteristics with the MCCS: comparable sample size, tissue type (formalin-fixed paraffin-embedded; FFPE), technology (Illumina HumanMethylation450 array), and participant characteristics (age, ancestry, and disease subtype and severity). Cox proportional hazard regression analyses were conducted to assess associations between these markers and both breast cancer-specific survival and overall survival, adjusting for relevant participant characteristics.

RESULTS

Our findings revealed partial replication for both individual CpG sites (9 out of 22) and multi-CpG signatures (2 out of 3). These associations were maintained after adjustment for participant characteristics and were stronger for breast cancer-specific mortality than for overall mortality. In fully-adjusted models, strong associations were observed for a CpG in PRAC2 (per standard deviation [SD], HR = 1.67, 95%CI: 1.24-2.25) and a signature based on 28 CpGs developed using elastic net (per SD, HR = 1.48, 95%CI: 1.09-2.00).

CONCLUSIONS

While further studies are needed to confirm and expand on these findings, our study suggests that DNA methylation markers hold promise for improving breast cancer prognostication.

Pan-Cancer Analysis of KANK2: Clinical and Molecular Insights into Tumor Progression and Therapeutic Implications

BACKGROUND

KANK2, a gene crucial for cell migration and movement, is implicated in neoplastic and non-neoplastic diseases. This study aimed to analyze KANK2's expression and its diagnostic and prognostic significance across 33 cancers using multiple online databases.

METHODS

This study aimed to comprehensively analyze KANK2 in 33 cancers using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Multiple web platforms and software were used for data analysis, including R, Cytoscape, HPA, TISIDB, UALCAN, GEO, cBioPortal, STRING, GSCALite, and CancerSEA. WB and qPCR experiments were used to verify the results.

RESULTS

KANK2 is widely expressed in various tissues and has significant diagnostic value in multiple cancers, with AUC values exceeding 0.75 in 13 cancer types. Survival analysis indicated that KANK2 expression is significantly associated with overall survival (OS) and disease-specific survival (DSS) in several cancers. KANK2 expression varied significantly across different molecular and immune subtypes and was associated with specific genetic mutations and DNA methylation patterns. Functional state analysis highlighted correlations with processes such as EMT, angiogenesis, and apoptosis. GSEA identified pathways related to proliferation, migration, and extracellular matrix remodeling. The key interacting proteins were identified by PPI network analysis, and the sensitive drug molecules were found by GSCA database. The results were also confirmed by two GEO datasets and WB and qPCR results.

CONCLUSION

KANK2 serves as a valuable biomarker for diagnosis and prognosis in various cancers, and its expression is intricately linked to multiple molecular and cellular processes, offering potential therapeutic targets for future research.

Mechanistic insights into SIRT7 and EZH2 regulation of cisplatin resistance in bladder cancer cells

Cisplatin (CDDP) resistance has been established to significantly impact Bladder Cancer (BCa) therapy. On the other hand, the crucial regulatory involvement of SIRT7 and EZH2 in bladder cancer development is well known. Herein, the collaborative regulatory roles and underlying mechanisms of SIRT7 and EZH2 in CDDP resistance in bladder cancer were explored. Immunohistochemistry (IHC) and Western Blot (WB) analyses were used to assess the expression levels of SIRT7/EZH2 and RND3 in bladder cancer tissues, normal ureteral epithelial cells, and bladder cancer cell lines. Furthermore, the impact of various treatments on of UMUC3 cell proliferation and CDDP sensitivity was assessed using CCK-8 assays, plate cloning assays, and flow cytometry analysis. Additionally, the levels of H3K18ac and H3K27me3 at the promoter region of the RND3 gene, the binding abilities of SIRT7 and EZH2, and the succinylation level of the EZH2 protein were examined using ChIP-qPCR assays, CO-IP assays, and IP assays, respectively. Moreover, in vivo experiments were conducted using a bladder cancer mouse model created by subcutaneously injecting UMUC3 cells into Balb/c nude mice. According to the results, SIRT7 correlated with the sensitivity of bladder cancer cells to both the platinum-based chemotherapy and CDDP. Specifically, SIRT7 could bind to the RND3 promoter, downregulating H3K18ac and RND3, ultimately leading to an increased CDDP sensitivity in UMUC3 cells. Furthermore, EZH2 siRNA could decrease H3K27me3 levels in the RND3 promoter, upregulating RND3. Overall, in the promoter region of the RND3 gene, SIRT7 upregulated H3K27me3 and EZH2 downregulated H3K18ac, leading to a decline in RND3 expression and CDDP sensitivity in bladder cancer cells. Additionally, SIRT7 reduced the succinylation of the EZH2 protein resulting in an EZH2-mediated RND3 downregulation. Therefore, targeting SIRT7 and EZH2 could be a viable approach to enhancing CDDP efficacy in bladder cancer treatment.

Prognostic significance of NUAK1 and its association with immune infiltration in stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) represents a significant global health burden, accounting for a considerable proportion of cancer-related mortalities, and NUAK1, a protein kinase, plays a crucial role in cellular metabolism, cell cycle regulation, migration, and tumor progression. However, its relationship with prognosis and immune infiltration in STAD has not been thoroughly investigated.

METHODS

RNA sequencing data from the Cancer Genome Atlas (TCGA) and Genotypic Tissue Expression Project (GTEx) databases were employed to assess disparities in NUAK1 expression between STAD tumour and normal tissues. Additionally, we investigated the correlation between NUAK1 expression and patient prognosis, in addition to the level of immune cell infiltration. The potential functions were elucidated through an examination of the Gene Ontology (GO) Encyclopedia, the Kyoto Encyclopedia of Genes and Genomes (KEGG), and an enrichment analysis (GSEA). The GeneMANIA was used to validate the functions of nuak1-related genes.

RESULTS

Our analysis demonstrated that NUAK1 expression in tumour tissues exhibited a notable disparity from that observed in normal tissues, with elevated levels detected in STAD tissues. We used the GeneMANIA database to identify functionally similar genes with significantly higher expression for some genes in the unpaired group samples. An elevated NUAK1 expression level was found to correlate with a poorer overall survival (OS), disease-specific survival (DSS), and progression-free intervals (PFI). Additionally, immune infiltration analysis indicated a significant positive correlation between NUAK1 expression and various tumor-infiltrating immune cells, while a negative correlation was observed with T helper cell 17(Th17) cells. Furthermore, enrichment analysis was conducted to identify relevant biological features and pathways.

CONCLUSION

The expression levels of NUAK1 are significantly increased in STAD, and this heightened expression correlates with diminished OS, DSS, and PFI among affected patients. These observations indicate that NUAK1 has the potential to function as a prognostic biomarker for STAD and may represent a viable therapeutic target for intervention in its management.

Ganoderma lucidum (Curtis) P. Karst. Immunomodulatory Protein Has the Potential to Improve the Prognosis of Breast Cancer Through the Regulation of Key Prognosis-Related Genes

Background/Objectives: Breast cancer in women is the most commonly diagnosed and most malignant tumor. Although luminal A breast cancer (LumA) has a relatively better prognosis, it still has a persistent pattern of recurrence. Ganoderma lucidum (Curtis) P. Karst. is a kind of traditional Chinese medicine and has antitumor effects. In this study, we aimed to identify the genes relevant to prognosis, find novel targets, and investigate the function of the bioactive protein from G. lucidum, called FIP-glu, in improving prognosis. Methods: Gene expression data and clinical information of LumA breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using bioinformatics methods, a predictive risk model was constructed to predict the prognosis for each patient. The cell counting kit-8 (CCK8) and clone formation assays were used to validate gene function. The ability of FIP-glu to regulate RNA levels of risk genes was validated. Results: Six risk genes (slit-roundabout GTPase-activating protein 2 (SRGAP2), solute carrier family 35 member 2 (SLC35A2), sequence similarity 114 member A1 (FAM114A1), tumor protein P53-inducible protein 11 (TP53I11), transmembrane protein 63C (TMEM63C), and polymeric immunoglobulin receptor (PIGR)) were identified, and a prognostic model was constructed. The prognosis was worse in the high-risk group and better in the low-risk group. The receiver operating characteristic (ROC) curve confirmed the model's accuracy. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the differentially expressed genes (DEGs) between the high- and low-risk groups were significantly enriched in the immune responses. TMEM63C could promote tumor viability, growth, and proliferation in vitro. FIP-glu significantly regulated these risk genes, and attenuated the promoting effect of TMEM63C in breast cancer cells. Conclusions: SRGAP2, SLC35A2, FAM114A1, TP53I11, TMEM63C, and PIGR were identified as the potential risk genes for predicting the prognosis of patients. TMEM63C could be a potential novel therapeutic target. Moreover, FIP-glu was a promising drug for improving the prognosis of LumA breast cancer.

Advancing precision and personalized breast cancer treatment through multi-omics technologies

Breast cancer is the most common malignant tumour in women, with more than 685,000 women dying of breast cancer each year. The heterogeneity of breast cancer complicates both treatment and diagnosis. Traditional methods based on histopathology and hormone receptor status are now no longer sufficient. Recently, advances in multi-omics techniques, including genomic, proteomic, and transcriptomic analyses, have deepened our understanding of breast cancer. Combining these approaches allows for precise molecular subtyping, which is essential for the detection of key mutations, protein interactions and gene expression patterns that are highly relevant to different therapeutic strategies. Genomic analyses have been effectively identifying key mutations in cancer. Meanwhile, proteomics and transcriptomics complement by identifying new therapeutic targets and elucidating gene expression dynamics. Integrating multi-omics and conventional diagnostics improves tumour characterisation and enables prognostic accuracy comparable to established standards and treatment response. Existing and emerging technologies enable real-time enhanced tumour follow-up and data analysis through liquid biopsy and artificial intelligence, respectively. Despite these clinical implementation challenges, multi-omics including clinical phenotyping offers significant potential for precision breast cancer treatment. This article describes recent advances in molecular subtyping and multi-omics technologies that are driving key innovations to optimise patient outcomes and further develop personalised medicine in the context of breast cancer care.

Alterations in the placental proteome in association with the presence of black carbon particles: A discovery study

BACKGROUND

Exposure to ambient air pollution is known to cause direct and indirect molecular expression changes in the placenta, on the DNA, mRNA, and protein levels. Ambient black carbon (BC) particles can be found in the human placenta already very early in gestation. However, the effect of in utero BC exposure on the entire placental proteome has never been studied to date.

OBJECTIVES

We explored whether placental proteome differs between mothers exposed to either high or low BC levels throughout the entire pregnancy.

METHODS

We used placental tissue samples from the ENVIRONAGE birth cohort, of 20 non-smoking, maternal- and neonate characteristic-matched women exposed to high (n = 10) or low (n = 10) levels of ambient BC throughout pregnancy. We modeled prenatal BC exposure levels based on the mother's home address and measured BC levels in the fetal side of the placenta. The placental proteome was analyzed by nano-liquid chromatography Q-TOF mass spectrometry. PEAKS software was used for protein identification and label-free quantification. Protein-protein interaction and functional pathway enrichment analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software.

RESULTS

The accumulation of BC particles in placenta was 2.19 times higher in the high versus low exposure group (20943.4 vs 9542.7 particles/mm³; p = 0.007). Thirteen proteins showed a ≥2-fold expression difference between the two exposure groups, all overexpressed in the placentas of women prenatally exposed to high BC levels. Three protein-protein interactions were enriched within this group, namely between TIMP3 and COL4A2, SERPINE2 and COL4A2, and SERPINE2 and GP1BB. Functional pathway enrichment analysis put forward pathways involved in extracellular matrix-receptor interaction, fibrin clot formation, and sodium ion transport regulation.

DISCUSSION

Prenatal BC exposure affects the placental proteome. Future research should focus on the potential consequences of these alterations on placental functioning, and health and disease during early childhood development.

The effects of different doses of compound enzyme preparations on the production performance, meat quality and rumen microorganisms of yak were studied by metagenomics and transcriptomics

Yak (Bos grunniens) is a large ruminant endemic to the Tibetan plateau. The addition of enzyme complexes to feed can significantly improve their growth performance. Therefore, studying the effects of ruminant compound enzyme preparations dosage on yak rumen microorganisms and production performance is crucial to promoting the development of the yak industry. This study aimed to determine the effects of feeding yaks with different doses of ruminant enzyme compounds on the performance, meat quality, and rumen microorganisms of yaks. Three kinds of experimental diets with doses of 0.5 g/kg (LE group), 1 g/kg (ME group), and 2 g/kg (HE group) were selected to determine the growth index, meat quality, serum biochemical indexes, rumen fluid pH and other indexes of the three experimental groups. Metagenomics studies were used to investigate the differences in rumen microbial composition and function among yak groups, and transcriptome sequencing of the longest dorsal muscle was performed to reveal the expression of differential genes among different groups. It was determined that the levels of dietary enzyme complexes significantly affected growth performance, rumen fluid pH, and serum biochemical indices. At the phylum level, the dominant phylum in all three treatment groups was Bacteroidota, Bacillota, Kiritimatiellota, and Pseudomonadota. At the genus level, Prevotella, Methanobrevibacter, Oscillibacter. Fibrobacter showed statistically significant differences in abundance (p < 0.05). CAZymes family analysis revealed significant differences in GHs, CTs, and CEs among the three groups. Genome-wide differential gene expression in the longest muscle of the yak back was analyzed by RNA-seq between the three experimental groups. Some DEGs were found to be enriched in the ECM, PI3K-Akt, PPAR, and protein digestion and absorption receptor pathways. Combined metagenomics and transcriptomics analyses revealed that some microorganisms were significantly associated with the genes COL11A1, POSTN, and PTHLH, which are involved in growth metabolism. In summary, this study investigated the effects and interrelationships of ruminant complex enzymes on yak performance, meat quality, and rumen environment. The results of this study provide a scientific basis for adding ruminant enzymes to yaks.

Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking

BACKGROUND/OBJECTIVES

Breast cancer is the second most common malignancy worldwide and poses a significant threat to women's health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment.

METHODS

Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments.

RESULTS

A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells.

CONCLUSIONS

Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer.

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell-cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low-dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial-to-mesenchymal transition and PI3K/AKT signaling in specific sub-regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder-based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

CST2 promotes cell proliferation and regulates cell cycle by activating Wnt-β-catenin signalling pathway in serous ovarian cancer

BACKGROUND

Cystatin SA (CST2) plays multiple roles in different types of malignant tumours; however, its role in serous ovarian cancer (SOC) remains unclear. Therefore, we aimed to investigate the expression levels, survival outcomes, immune cell infiltration, proliferation, cell cycle, and underlying molecular mechanisms associated with the CST2 signature in SOC.

METHODS

The Cancer Genome Atlas database was used to acquire clinical information and CST2 expression profiles from patients with SOC. Wilcoxon rank-sum tests were used to compare CST2 expression levels between SOC and normal ovarian tissues. A prognostic assessment of CST2 was conducted using Cox regression analysis and the Kaplan-Meier method. Differentially expressed genes were identified using functional enrichment analysis. Immune cell infiltration was examined using a single-sample gene set enrichment analysis. Cell cycle characteristics and proliferation were assessed using a colony formation assay, flow cytometry, and a cell counting kit-8 assay. Western blots and quantitative reverse transcription PCR analyses were employed to examine CST2 expressions and related genes involved in the cell cycle and the Wnt-β-catenin signalling pathway.

RESULTS

Our findings revealed significant upregulation of CST2 in SOC, and elevated CST2 expression was correlated with advanced clinicopathological characteristics and unfavourable prognoses. Pathway enrichment analysis highlighted the association between the cell cycle and the Wnt signalling pathway. Moreover, increased CST2 levels were positively correlated with immune cell infiltration. Functionally, CST2 played vital roles in promoting cell proliferation, orchestrating the G1-to-S phase transition, and driving malignant SOC progression through activating the Wnt-β-catenin signalling pathway.

CONCLUSIONS

The elevated expression of CST2 may be related to the occurrence and progression of SOC by activating the Wnt-β-catenin pathway. Additionally, our findings suggest that CST2 is a promising novel biomarker with potential applications in therapeutic, prognostic, and diagnostic strategies for SOC.

Integration analysis of cis- and trans-regulatory long non-coding RNAs associated with immune-related pathways in non-small cell lung cancer

Background

Long non-coding RNAs (lncRNAs) are importantly involved in the initiation and progression of non-small cell lung cancer (NSCLC). However, the classification and mechanisms of lncRNAs remain largely elusive.

Aim

Hence, we addressed this through bioinformatics analysis.

Methods and results

We utilized microarray technology to analyze lncRNAs and mRNAs in twenty paired NSCLC tumor tissues and adjacent normal tissues. Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were conducted to discern the biological functions of identified differentially expressed transcripts. Additionally, networks of lncRNA-mRNA co-expression, including cis-regulation, lncRNA-transcription factor (TF)-mRNA, trans-regulation, and lncRNA-miRNA-mRNA interactions were explored. Furthermore, the study examined differentially expressed transcripts and their prognostic values in a large RNA-seq dataset of 1016 NSCLC tumors and normal tissues extracted from the Cancer Genome Atlas (TCGA). The analysis revealed 391 lncRNAs and 344 mRNAs with differential expression in NSCLC tumor tissues compared to adjacent normal tissues. Subsequently, 43,557 co-expressed lncRNA-mRNA pairs were identified, including 27 lncRNA-mRNA pairs in cis, 9 lncRNA-TF-mRNA networks, 34 lncRNA-mRNA pairs in trans, and 8701 lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks. Notably, these lncRNAs were found to be involved in immune-related pathways. Six significant transcripts, including NTF4, PTPRD-AS, ITGA11, HID1-AS1, RASGRF2-AS1, and TBX2-AS1, were identified within the ceRNA network and trans-regulation.

Conclusion

This study brings important insights into the regulatory roles of lncRNAs in NSCLC, providing a fresh perspective on lncRNA research in tumor biology.

TRPV4 drives the progression of leiomyosarcoma by promoting ECM1 generation and co-activating the FAK/PI3K/AKT/GSK3β pathway

PURPOSE

Leiomyosarcoma (LMS) is an aggressive mesenchymal malignant tumor with poor therapeutic options, but the molecular mechanisms underlying LMS remain largely unknown. Increasing evidence indicates that transient receptor potential vanilloid 4 (TRPV4) levels are closely related to the advancement of various malignant tumors through diverse molecular mechanisms. However, the roles and regulatory mechanisms of TRPV4 in LMS progression remain unclear.

METHODS

Immunohistochemistry, Western blot, and immunofluorescence were used to investigate the relationship between TRPV4 expression and LMS. Survival analysis was conducted to evaluate the association between TRPV4 levels and prognosis in LMS patients. Intracellular Ca2+ measurement, colony formation, CCK-8, wound healing and Transwell assays and peritoneal metastasis mouse model were used to verify the effect of TRPV4 activity and expression on LMS proliferation and metastasis. RNA-seq and proteomics were performed to explore the underlying mechanism.

RESULTS

TRPV4 was upregulated in LMS tissues and cells and served as a novel prognostic factor. Moreover, TRPV4 overexpression enhanced cell proliferation, cell migration and invasion of LMS cells in vitro, as well as promoted tumor metastasis in vivo, which could be blocked by HC067047 intervention or TRPV4 knockdown. Combined RNA-seq and proteomics analysis of KEGG pathway indicated that ECM receptor interaction was obviously activated. Extracellular matrix protein 1 (ECM1) was identified as downstream gene of TRPV4. Mechanistically, TRPV4 overexpression increased ECM1 level and activated the FAK/PI3K/AKT/GSK3β pathway, which could be reversed by TRPV4 knockdown or LY294002 treatment. Moreover, ECM1 overexpression enhanced the activation of FAK/PI3K/AKT/GSK3β pathway. And simultaneous overexpression of TRPV4 and ECM1 synergistically activated this pathway.

CONCLUSION

Our findings provide a novel mechanism by which TRPV4 directly activates Ca2+/FAK/PI3K/AKT/GSK3β pathway and further indirectly enhances the FAK/PI3K/AKT/GSK3β pathway through the promotion and secretion of ECM1 to promote LMS malignant progression. Targeting the TRPV4/FAK axis might be a promising potential strategy for prognosis and treatment of LMS.

Deciphering progressive lesion areas in breast cancer spatial transcriptomics via TGR-NMF

Identifying spatial domains is critical for understanding breast cancer tissue heterogeneity and providing insights into tumor progression. However, dropout events introduces computational challenges and the lack of transparency in methods such as graph neural networks limits their interpretability. This study aimed to decipher disease progression-related spatial domains in breast cancer spatial transcriptomics by developing the three graph regularized non-negative matrix factorization (TGR-NMF). A unitization strategy was proposed to mitigate the impact of dropout events on the computational process, enabling utilization of the complete gene expression count data. By integrating one gene expression neighbor topology and two spatial position neighbor topologies, TGR-NMF was developed for constructing an interpretable low-dimensional representation of spatial transcriptomic data. The progressive lesion area that can reveal the progression of breast cancer was uncovered through heterogeneity analysis. Moreover, several related pathogenic genes and signal pathways on this area were identified by using gene enrichment and cell communication analysis.

Unveiling epigenetic regulatory elements associated with breast cancer development

Breast cancer is the most common cancer in women and the 2nd most common cancer worldwide, yearly impacting over 2 million females and causing 650 thousand deaths. It has been widely studied, but its epigenetic variation is not entirely unveiled. We aimed to identify epigenetic mechanisms impacting the expression of breast cancer related genes to detect new potential biomarkers and therapeutic targets. We considered The Cancer Genome Atlas database with over 800 samples and several omics datasets such as mRNA, miRNA, DNA methylation, which we used to select 2701 features that were statistically significant to differ between cancer and control samples using the Monte Carlo Feature Selection and Interdependency Discovery algorithm, from an initial total of 417,486. Their biological impact on cancerogenesis was confirmed using: statistical analysis, natural language processing, linear and machine learning models as well as: transcription factors identification, drugs and 3D chromatin structure analyses. Classification of cancer vs control samples on the selected features returned high classification weighted Accuracy from 0.91 to 0.98 depending on feature-type: mRNA, miRNA, DNA methylation, and classification algorithm. In general, cancer samples showed lower expression of differentially expressed genes and increased β-values of differentially methylated sites. We identified mRNAs whose expression is well explained by miRNA expression and differentially methylated sites β-values. We recognized differentially methylated sites possibly affecting NRF1 and MXI1 transcription factors binding, causing a disturbance in NKAPL and PITX1 expression, respectively. Our 3D models showed more loosely packed chromatin in cancer. This study successfully points out numerous possible regulatory dependencies.

Developing hypoxia and lactate metabolism-related molecular subtypes and prognostic signature for clear cell renal cell carcinoma through integrating machine learning

BACKGROUND

The microenvironment of clear cell renal cell carcinoma (ccRCC) is characterized by hypoxia and increased lactate production. However, the impact of hypoxia and lactate metabolism on ccRCC remains incompletely understood. In this study, a new molecular subtype is developed based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs), aiming to create a tool that can predict the survival rate, immune microenvironment status, and responsiveness to treatment of ccRCC patients.

METHOD

We obtained RNA-seq data and clinical information of patients with ccRCC from TCGA and GEO. HRGs and LMRGs are sourced from the Molecular Signatures Database. Integrating 10 machine learning algorithms and 101 frameworks, we constructed a prognostic model related to hypoxia and lactate metabolism. Its accuracy and reliability are evaluated through constructing prognostic nomograms, drawing ROC curves, and validating with clinical datasets. Additionally, risk subgroups are evaluated based on functional enrichment, tumor mutational burden (TMB), immune cell infiltration degree, and immune checkpoint expression level. Finally, we evaluate the responsiveness of risk subgroups to immunotherapy and determine personalized drugs for specific risk subgroups.

RESULTS

85 valuable prognostic genes were screened out. Functional enrichment analysis shows that the group with high-risk hypoxia and lactate metabolism-related genes scores (HLMRGS) is mainly involved in the activation of immune-related activities, while the low risk HLMRGS group is more active in metabolic and tumor-related pathways. At the same time, differences in the cellular functional states in the tumor microenvironment between the high risk HLMRGS group and the low risk HLMRGS group were observed. Finally, potential drugs for specific risk subgroups were determined.

CONCLUSION

We have developed a novel prognostic signature that integrates hypoxia and lactate metabolism. It is expected to become an effective tool for prognosis prediction, immunotherapy and personalized medicine of ccRCC.

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer

Background

Platelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.

Methods

We downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).

Results

A ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.

Conclusion

The novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

Engineering a porphyrin COFs encapsulated by hyaluronic acid tumor-targeted nanoplatform for sequential chemo-photodynamic multimodal tumor therapy

Numerous barriers hinder the entry of drugs into cells, limiting the effectiveness of tumor pharmacotherapy. Effective penetration into tumor tissue and facilitated cellular uptake are crucial for the efficacy of nanotherapeutics. Photodynamic therapy (PDT) is a promising approach for tumor suppression. In this study, we developed a size-adjustable porphyrin-based covalent organic framework (COF), further modified with hyaluronic acid (HA), to sequentially deliver drugs for combined chemo-photodynamic tumor therapy. A larger COF (P-COF, approximately 500 nm) was loaded with the antifibrotic drug losartan (LST) to create LST/P-COF@HA (LCH), which accumulates at tumor sites. After injection, LCH releases LST, downregulating tumor extracellular matrix (ECM) component levels and decreasing collagen density, thus reducing tumor solid stress. Additionally, the reactive oxygen species (ROS) generated from LCH under 660 nm laser irradiation induce lipid peroxidation of cell membranes. Owing to its larger particle size, LCH primarily functions extracellularly, paving the way for subsequent treatments. Following intravenous administration, the smaller COF (p-COF, approximately 200 nm) loaded with doxorubicin (DOX) and modified with HA (DOX/p-COF@HA, DCH) readily enters cells in the altered microenvironment. Within tumor cells, ROS generated from DCH facilitates PDT, while the released DOX targets cancer cells via chemotherapy, triggered by disulfide bond cleavage in the presence of elevated glutathione (GSH) levels. This depletion of GSH further enhances the PDT effect. Leveraging the size-tunable properties of the porphyrin COF, this platform achieves a multifunctional delivery system that overcomes specific barriers at optimal times, leading to improved outcomes in chemo-photodynamic multimodal tumor therapy in vivo.

Integrative analysis of recurrence related gene signature and STC1 in colorectal cancer proliferation and metastasis

Colorectal cancer remains a formidable global health challenge, characterized by high recurrence rates and poor prognosis. This study introduces a novel Recurrence Related Gene Signature (RRGS), designed to predict therapy response and enhance prognostic accuracy in colorectal cancer. Through analysis of the GSE17536 cohort, we identified 79 differentially expressed genes (DEGs) between recurrent and non-recurrent cases, comprising 54 upregulated and 25 downregulated genes. Pathway analysis revealed that upregulated genes were enriched in cancer progression-related pathways, while downregulated genes were associated with immune-related processes. Leveraging these findings, we developed the RRGS using LASSO regression, resulting in an innovative 11-gene model with robust diagnostic and prognostic capabilities. Notably, the RRGS demonstrated significant predictive value for both overall survival and disease-free survival across multiple datasets, with higher RRGS scores correlating with advanced tumor stages and poorer outcomes, particularly in post-chemotherapy patients. This predictive power highlights the RRGS's potential in guiding personalized treatment strategies. Furthermore, we identified STC1 as a critical component of the RRGS, playing a significant role in tumor progression and immune evasion. Through rigorous in vitro and in vivo experiments we confirmed that STC1 knockdown substantially reduced cell proliferation and metastasis, emphasizing its potential as a therapeutic target. This comprehensive study not only elucidates the molecular mechanisms driving colorectal cancer recurrence but also introduces a powerful tool for enhancing prognostic accuracy and personalizing therapeutic interventions.

Identification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study

OBJECTIVES

Radiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD).

METHODS

Using computed tomography (CT) imaging of 234 stage I-IIIA LUAD patients derived from three public multicenter cohorts, we proposed a rank-based method that utilized the relative rank patterns of quantitative values between radiomic feature pairs within individual patients and established a feature pair signature for LUAD prognosis. We collected a new clinical cohort with 162 LUAD patients for independent validation.

RESULTS

A rank-based radiomic signature, consisting of 12 feature pairs, was developed, and it could determine the mortality risk for an individual according to the rank patterns of 12 feature pairs within the patient's CT imaging. The prognostic performance of the rank-based signature was effectively validated in the new clinical cohort (log-rank P = 0.0051, C-index = 0.73), whereas other signatures lost their prognostic ability across centers. The novel proposed radiomic nomogram significantly improved the prognostic performance of clinicopathological factors. The further radiogenomic analyses revealed the underlying biological characteristics (e.g., Stemness, Ferroptosis, 'ECM') reflected by the rank-based radiomic signature.

CONCLUSIONS

This multicenter study illustrates the accuracy and stability of the rank-based radiomic signature for LUAD prognosis, and demonstrates a unique advantage of clinical individualized application. The biological characteristics underlying the rank-based radiomic signature would accelerate its clinical application.

Identification of basement membrane-related prognostic model associated with the immune microenvironment and synthetic therapy response in pancreatic cancer: integrated bioinformatics analysis and clinical validation

Pancreatic cancer (PC) is a common and highly malignant tumor. Basement membrane (BM) is formed by the crosslinking of extracellular matrix macromolecules and acts as a barrier against tumor cell metastasis. However, the role of BM in PC prognosis, immune infiltration, and treatment remains unclear. This study collected transcriptome and clinical survival data of PC via TCGA, GEO, and ICGC databases. PC patients (PCs) from the First Affiliated Hospital of Dalian Medical University were obtained as the clinical validation cohort. BM-related genes (BMRGs) were acquired from GeneCards and basement membraneBASE databases. A total of 46 differential-expressed BMRGs were identified. Then the BM-related prognostic model (including DSG3, MET, and PLAU) was built and validated. PCs with a low BM-related score had a better outcome and were more likely to benefit from oxaliplatin, irinotecan, and KRAS(G12C) inhibitor-12, and immunotherapy. Immune analysis revealed that BM-related score was positively correlated with neutrophils, cancer-associated fibroblasts, and macrophages infiltration, but negatively correlated with CD8+ T cells, NK cells, and B cells infiltration. PCs from the clinical cohort further verified that BM-related model could accurately predict PCs' outcomes. DSG3, MET, and PLAU were notably up-regulated within PC tissues and linked to a poor prognosis. In vitro experiments showed that DSG3 knockdown markedly suppressed the proliferation, migration, and invasion of PC cells. Molecular docking indicated that epigallocatechin gallate had a strong binding activity with DSG3, MET, and PLAU and may be used as a potential therapeutic agent for PC. In conclusion, this study developed a BM-related model associated with PC prognosis, immune infiltration, and treatment, which provided new insights into PC stratification and drug intervention.

Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers

Background/Objectives: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment. Methods: We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC. Results: Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjusted p-value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings. Conclusions: The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC.

Upregulation of miRNA-450b-5p targets ACTB to affect drug resistance and prognosis of ovarian cancer via the PI3K/Akt signaling pathway

Background

Ovarian cancer (OC) is the most malignant gynecologic cancer, and chemoresistance is a major cause of treatment failure in patients with OC. The understanding of microRNA (miRNA) in cancer is limited, and the role of miRNA (miR)-450b-5p in cancer drug resistance is unknown. In this study, we aim to evaluate the role of miR-450b-5p in drug-resistant OC and its underlying mechanisms.

Methods

MiR-450b-5p expression was assessed in drug-sensitive and resistant OC cells via quantitative real-time polymerase chain reaction. Cell viability was evaluated using the Cell Counting Kit-8 assay. Progression-free survival (PFS) and overall survival (OS) curves were generated using the Kaplan-Meier method and the log-rank test. Target genes of miR-450b-5p were identified from the Cancer MIRNome database. Co-expressed genes were obtained from The Cancer Genome Atlas and Cancer Genome cBioportal for pathway enrichment and functional clustering analysis.

Results

The miRNA-450b-5p expression was significantly increased in A2780 and SKOV3 OC-resistant cells and significantly increased by 17-fold in the A2780-CBP-Lv-miR-450b-5p cells compared to A2780-CBP and A2780-CBP-Lv-NC cells. The up-regulated expression of miR-450b-5p increased the cell viability and half maximal inhibitory concentration (IC50) of A2780 platinum-resistant cells and was associated with poor OS. We obtained 33 potential target genes of miR-450b-5p and beta-actin (ACTB) might be a potential target of miR-450b-5p. Low expression of ACTB predicted poor OS and PFS. We obtained 362 common genes co-expressed with ACTB, which involved 4 critical pathways. PI3K acted as an upstream pathway of the other three pathways, which ultimately responded to drug resistance regulation in OC. The genes enriched in four pathways were cross-analyzed and 13 overlapping genes were obtained. These 13 genes were also significantly and positively co-expressed with ACTB at both protein and mRNA levels.

Conclusions

High expression of miRNA-450b-5p might affect drug resistance and prognosis in OC by targeting 13 co-expressed genes of ACTB directly through the PI3K/Akt signaling pathway. Thus, miR-450b-5p might provide a new therapeutic target for drug resistance in OC.

The prognostic value of bioinformatics analysis of ECM receptor signaling pathways and LAMB1 identification as a promising prognostic biomarker of lung adenocarcinoma

The extracellular matrix (ECM) is a complex and dynamic network of cross-linked proteins and a fundamental building block in multicellular organisms. Our study investigates the impact of genes related to the ECM receptor interaction pathway on immune-targeted therapy and lung adenocarcinoma (LUAD) prognosis. This study obtained LUAD chip data (GSE68465, GSE31210, and GSE116959) from NCBI GEO. Moreover, the gene data associated with the ECM receptor interaction pathway was downloaded from the Molecular Signature Database. Differentially expressed genes were identified using GEO2R, followed by analyzing their correlation with immune cell infiltration. Univariate Cox regression analysis screened out ECM-related genes significantly related to the survival prognosis of LUAD patients. Additionally, Lasso regression and multivariate Cox regression analysis helped construct a prognostic model. Patients were stratified by risk score and survival analyses. The prognostic models were evaluated using receiver operating characteristic curves, and risk scores and prognosis associations were analyzed using univariate and multivariate Cox regression analyses. A core gene was selected for gene set enrichment analysis and CIBERSORT analysis to determine its function and tumor-infiltrating immune cell proportion, respectively. The results revealed that the most abundant pathways among differentially expressed genes in LUAD primarily involved the cell cycle, ECM receptor interaction, protein digestion and absorption, p53 signaling pathway, complement and coagulation cascade, and tyrosine metabolism. Two ECM-associated subtypes were identified by consensus clustering. Besides, an ECM-related prognostic model was validated to predict LUAD survival, and it was associated with the tumor immune microenvironment. Additional cross-analysis screened laminin subunit beta 1 (LAMB1) for further research. The survival time of LUAD patients with elevated LAMB1 expression was longer than those with low LAMB1 expression. Gene set enrichment analysis and CIBERSORT analyses revealed that LAMB1 expression correlated with tumor immune microenvironment. In conclusion, a prognostic model of LUAD patients depending on the ECM receptor interaction pathway was constructed. Screening out LAMB1 can become a prognostic risk factor for LUAD patients or a potential target during LUAD treatment.

PANoptosis-related molecular clustering and prognostic signature associated with the immune landscape and therapy response in breast cancer

Breast cancer (BC) remains one of the most pervasive and complex malignancies. PANoptosis represents a recently identified cellular mechanism leading to programmed cell death. However, the prognostic implications and influence on the immune microenvironment of BC pertaining to PANoptosis-related genes (PRGs) remain significantly understudied. We conducted differential expression analysis to identify prognostic-Related PRGs by the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic signature using LASSO and Cox regression analyses. ROC curves were employed to assess the performance of the signatures. Furthermore, drug sensitivity between low- and high-risk group were analysis. Finally, we conducted RT-qPCR to assess the gene expression levels involved in this signature. We categorized BC patients into 2 distinct molecular clusters based on PRGs and identified differentially expressed genes associated with prognosis. Subsequently, BC patients were then divided into 2 gene clusters. The identified PRGs molecular clusters and gene clusters demonstrated association with patient survival, immune system functions, and biological processes and pathways of BC. A prognostic signature comprising 5 genes was established, and BC patients were classified into low- and high-risk groups based on the risk scores. The ROC curves demonstrated that those in the low-risk category exhibited notably extended survival compared to the high-risk group. A nomogram model for patient survival was constructed based on the risk score in conjunction with other clinical features. High-risk group had higher tumor burden mutation, CSC index and lower StomalScore, ImmuneScore, and ESTIMATEScore. Subsequently, we established a correlation between the risk score and drug sensitivity among BC patients. Finally, qRT-PCR results showed that the expression of CXCL1, PIGR, and TNFRSF14 significantly decreased, while CXCL13 and NKAIN were significantly increased in BC tissues. We have developed a molecular clustering and prognostic signature based on PANoptosis to improve the prediction of BC prognosis. This discovery has the potential to not only assist in assessing overall patient prognosis but also to deepen our understanding of the underlying mechanisms of PANoptosis in BC pathogenesis.

The intersection of the nervous system and breast cancer

Breast cancer (BC) represents a paradigm of heterogeneity, manifesting as a spectrum of molecular subtypes with divergent clinical trajectories. It is fundamentally characterized by the aberrant proliferation of malignant cells within breast tissue, a process modulated by a myriad of factors that govern its progression. Recent endeavors outline the interplay between BC and the nervous system, illuminate the complex symbiosis between neural structures and neoplastic cells, and elucidate nerve dependence as a cornerstone of BC progression. This includes the neural modulations on immune response, neurovascular formation, and multisystem interactions. Such insights have unveiled the critical impact of neural elements on tumor dynamics and patient prognosis. This revelation beckons a deeper exploration into the neuro-oncological interface, potentially unlocking novel therapeutic vistas. This review endeavors to delineate the intricate mechanisms between the nervous system and BC, aiming to accentuate the implications and therapeutic strategies of this intersection for tumor evolution and the formulation of innovative therapeutic approaches.

FOXK2 amplification promotes breast cancer development and chemoresistance

Oncogene activation through DNA amplification or overexpression is a crucial driver of cancer initiation and progression. The FOXK2 gene, located on chromosome 17q25, encodes a transcription factor with a forkhead DNA-binding domain. Analysis of genomic datasets reveals that FOXK2 is frequently amplified and overexpressed in breast cancer, correlating with poor patient survival. Knockdown of FOXK2 significantly inhibited breast cancer cell proliferation, migration, anchorage-independent growth, and delayed tumor growth in a xenograft mouse model. Additionally, inhibiting FOXK2 sensitized breast cancer cells to chemotherapy. Co-overexpression of FOXK2 and mutant PI3KCA transformed non-tumorigenic MCF-10A cells, suggesting a role for FOXK2 in PI3KCA-driven tumorigenesis. CCNE2, PDK1, and ESR1 were identified as transcriptional targets of FOXK2 in MCF-7 cells. Small-molecule inhibitors of CCNE2/CDK2 (dinaciclib) and PDK1 (dichloroacetate) exhibited synergistic anti-tumor effects with PI3KCA inhibitor (alpelisib) in vitro. Inhibition of FOXK2 by dinaciclib synergistically enhanced the anti-tumor effects of alpelisib in a xenograft mouse model. Collectively, these findings highlight the oncogenic function of FOXK2 and suggest that FOXK2 and its downstream genes represent potential therapeutic targets in breast cancer.

Identification of key modules and hub genes involved in regulating the feather follicle development of Wannan chickens using WGCNA

Carcass appearance is important economic trait, which affects customers in making purchase decisions. Both density and diameter of feather follicles are two important indicators of carcass appearance. However, the regulatory network and key genes be involved in feather follicle development remain poorly understood. To identify key genes and modules that involved in feather follicle development in chickens, 16 transcriptome datasets of Wannan chickens skin tissue (3 birds at the E9, E11, and E14, respectively, and 7 birds at the 12W) were used for weighted gene co-expression network analysis (WGCNA) analysis, and 12 skin tissue samples (3 birds for each stage) were selected for DEGs analysis. A total of 5,025, 2,337, and 10,623 DEGs were identified in 3 comparison groups, including the E9 vs. E11, the E11 vs. E14, and the E14 vs. 12W. Additionally, 31 co-expression gene modules were identified by WGCNA and the dark-orange, cyan, and blue module were found to be significantly associated with feather follicle development (p < 0.01). In total, 92,898 and 8,448 hub genes were obtained in the dark-orange, cyan, and blue modules, respectively. We focused on the cyan and blue modules, as 6 and 336 hub genes of these modules were identified to overlap with the DEGs of the three comparison groups, respectively. The 6 overlapped genes such as LAMC2, COL6A3, and COL6A2 etc., were over-represented in 12 categories such as focal adhesion and ECM-receptor interaction signaling pathway. Among the 336 genes that overlapped between the blue module and different DEGs comparison groups several genes including WNT7A and WNT9B were enriched in Wnt and ECM-receptor interaction signaling pathway. These results suggested that the LAMC2, COL6A3, COL6A2, WNT7A, and WNT9B genes may play a crucial role in the regulation of feather follicle development in Wannan chickens. Our results provided a reference for the molecular regulatory network and key genes in the development of feather follicles and contribute to molecular breeding for carcass appearance traits in chickens.

Identification and validation of basement membrane-related genes predicting prognosis and immune infiltration associated with bladder cancer

Bladder cancer (BC) is fatal during muscle invasion and treatment progress is limited. In this study, we aimed to construct and validate basement membrane (BM)-associated gene prognosis to predict BC progression and tumor immune infiltration correlation. We choreographed BM-related genes in the Cancer Genome Atlas (TCGA) database using COX regression and least absolute shrinkage and selection operator (LASSO) analysis, and the predictive value of BM-related genes was further validated by the GSE32548, GSE129845, and immunohistochemistry staining. All analyses were performed with R-version 4.2.2, and its appropriate packages. Three genes were identified to construct a gene signature to predictive of BC prognosis. We divided the TCGA database into 2 groups, and patients in the high-risk group had worse overall survival (OS) than those in the low-risk group. In GSE32548, we confirmed that patients in the high-risk group had a poorer prognosis compared to those in the low-risk group in terms of OS. Immunohistochemical staining of EPEMP1, GPC2, and ITGA3 showed significantly higher expression at the protein level in BC tissues than in normal tissues. The Spearman analysis showed risk score was positively correlated with B cell naïve, Macrophages M2, and Mast cells resting. stromal score, immune score, and ESTIMATE scores were significantly higher in the high-risk group. drugs sensitivity analysis showed IC50 of Cisplatin, Gemcitabine, and Methotrexate in the high-risk group was significantly higher than that in the low-risk group. We identified 3 prognostic genes from a novel perspective of BM genes as effective risk stratification tools for BC patients.

Transcriptome profiling reveals dysregulation of inflammatory and protein synthesis genes in PCOS

To analyze the differential expression genes of polycystic ovary syndrome (PCOS), clarify their functions and pathways, as well as the protein-protein interaction network, identify HUB genes, and explore the pathological mechanism. PCOS microarray datasets were screened from the GEO database. Common differentially expressed genes (co-DEGs) were obtained using GEO2R and Venn analysis. Enrichment and pathway analyses were conducted using the DAVID online tool, with results presented in bubble charts. Protein-protein interaction analysis was performed using the STRING tool. HUB genes were identified using Cytoscape software and further interpreted with the assistance of the GeneCards database. A total of two sets of co-DEGs (108 and 102), key proteins (15 and 55), and hub genes (10 and 10) were obtained. The co-DEGs: (1) regulated inflammatory responses and extracellular matrix, TNF, and IL-17 signaling pathways; (2) regulated ribosomes and protein translation, ribosome and immune pathways. The key proteins: (1) regulated inflammation, immunity, transcription, matrix metabolism, proliferation/differentiation, energy, and repair; (2) regulated ubiquitination, enzymes, companion proteins, respiratory chain components, and fusion proteins. The Hub genes: (1) encoded transcription factors and cytokines, playing vital roles in development and proliferation; (2) encoded ribosomes and protein synthesis, influencing hormone and protein synthesis, associated with development and infertility. The dysregulated expression of inflammation and protein synthesis genes in PCOS may be the key mechanism underlying its onset and progression.

Transcriptomic profiling and discovery of key transcription factors involved in adventitious roots formation from root cuttings of mulberry

ARs plays a crucial role in plant morphogenesis and development. The limited and inefficient rooting of scions poses a significant challenge to the efficiency and quality of clonal propagation of forest trees in silvicultural practices. Building on previous research conducted by our team, we found that applying IBA at a concentration of 1000 mg/L significantly enhanced mulberry rooting. This study aims to uncover the molecular mechanisms underlying this effect by analyzing RNA sequencing data from mulberry phloem before and after treatment with IBA over time intervals of 10, 20, 30, and 40 days. We identified 5226 DEGs, which were then classified into GO terms and KEGG pathways, showing significant enrichment in hormone signaling processes. Using WGCNA, we identified eight co-expression modules, two of which were significantly correlated with the IBA treatment. Additionally, 18 transcription factors that potentially facilitate ARs formation in mulberry were identified, and an exploratory analysis on the cis-regulatory elements associated with these transcription factors was conducted. The findings of this study provide a comprehensive understanding of the mechanisms of ARs in mulberry and offer theoretical support for the discovery and utilization of exceptional genetic resources within the species.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study

BACKGROUND

Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis.

METHODS

This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns.

RESULTS

The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability.

CONCLUSIONS

Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

AEBP1 upregulation contributes to cervical cancer progression by facilitating cell proliferation, migration, and invasion

BACKGROUND

Aberrant expression of adipocyte enhancer-binding protein 1 (AEBP1) has been demonstrated to be involved in the tumorigenesis and progression of numerous cancers. This study was aimed to investigate the mechanism of AEBP1 in the development of cervical cancer.

METHODS

The expression of AEBP1 in cervical cancer was assessed by immunohistochemistry. The function of AEBP1 on cell proliferation, migration, and invasion was determined by methyl thiazolyl tetrazolium assay, colony formation, and transwell assay. The activation of related signaling pathway was determined by western blot. The bioinformatics analysis was performed by Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.

RESULTS

Higher protein expression of AEBP1 was observed in patients with cervical cancer. Overexpressed AEBP1 promoted cell proliferation, migration, and invasion abilities in cervical cancer cells. Moreover, the research manifested that AEBP1 activated the phosphorylation of STAT3. GO and KEGG analysis showed that genes positively related to AEBP1 were highly enriched in functions like epithelial cell proliferation, muscle cell migration, myoblast migration, smooth muscle tissue development, ECM-receptor interaction, transcriptional misregulation in cancer, and proteoglycans in cancer. While genes negatively related to AEBP1 were associated with immunity, including inflammatory response, external-stimulus response, neutrophil, granulocyte, and macrophage chemotaxis.

CONCLUSIONS

This study suggested that AEBP1 acts as an oncogened and might be a potential therapeutic target for the treatment of cervical cancer.

mRNA-lncRNA gene expression signature in HPV-associated neoplasia and cervical cancer

Cervical cancer is one of the most frequent cancers in women and is associated with human papillomavirus (HPV) in 70 % of cases. Cervical cancer occurs because of progression of low-differentiated cervical intraepithelial neoplasia through grade 2 and 3 lesions. Along with the protein-coding genes, long noncoding RNAs (lncRNAs) play an important role in the development of malignant cell transformation. Although human papillomavirus is widespread, there is currently no well-characterized transcriptomic signature to predict whether this tumor will develop in the presence of HPV-associated neoplastic changes in the cervical epithelium. Changes in gene activity in tumors reflect the biological diversity of cellular phenotype and physiological functions and can be an important diagnostic marker. We performed comparative transcriptome analysis using open RNA sequencing data to assess differentially expressed genes between normal tissue, neoplastic epithelium, and cervical cancer. Raw data were preprocessed using the Galaxy platform. Batch effect correction, identification of differentially expressed genes, and gene set enrichment analysis (GSEA) were performed using R programming language packages. Subcellular localization of lncRNA was analyzed using Locate-R and iLoc-LncRNA 2.0 web services. 1,572 differentially expressed genes (DEGs) were recorded in the "cancer vs. control" comparison, and 1,260 DEGs were recorded in the "cancer vs. neoplasia" comparison. Only two genes were observed to be differentially expressed in the "neoplasia vs. control" comparison. The search for common genes among the most strongly differentially expressed genes among all comparison groups resulted in the identification of an expression signature consisting of the CCL20, CDKN2A, CTCFL, piR-55219, TRH, SLC27A6 and EPHA5 genes. The transcription level of the CCL20 and CDKN2A genes becomes increased at the stage of neoplastic epithelial changes and stays so in cervical cancer. Validation on an independent microarray dataset showed that the differential expression patterns of the CDKN2A and SLC27A6 genes were conserved in the respective gene expression comparisons between groups.

Identification of Long Noncoding RNAs Expression Profiles Between Gallstone and Gallbladder Cancer Using Next-Generation Sequencing Analysis

Background

Gallstone disease (GS) is an important risk factor for Gallbladder cancer (GBC). However, the mechanisms of the progression of GS to GBC remain unclear. Long non-coding RNA (lncRNA), modulates DNA/RNA/proteins at epigenetic, pre-transcriptional, transcriptional and posttranscriptional levels, and plays a potential therapeutic role in various diseases. This study aims to identify lncRNAs that have a potential impact on GS-promoted GBC progression.

Methods and Results

Six GBC patients without GS, six normal gallbladder tissues, nine gallstones and nine GBC patients with GS were admitted to our hospital. The next-generation RNA-sequencing was performed to analyze differentially expressed (DE) lncRNA and messenger RNA (mRNA) in four groups. Then overlapping and specific molecular signatures were analyzed. We identified 29 co-DEGs and 500 co-DElncRNAs related to gallstone or GBC. The intersection and concatenation of co-DEGs and co-DElncRNA functionally involved in focal adhesion, Transcriptional misregulation in cancers, Protein digestion and absorption, and ECM-receptor interaction signaling pathways may contribute to the development of gallbladder cancer. Further exploration is necessary for early diagnosis and the potential treatment of GBC. FXYD2, MPZL1 and PAH were observed in both co-DEGs and co-DElncRNA and validated by qRT-PCR.

Conclusion

Our data identified a series of DEGs and DElncRNAs, which were involved in the progression of GBC and GS-related metabolism pathways. Compared to GBC, the GS profile was more similar to para-tumor tissues in transcriptome level and lower risk of cancer. Further exploration is necessary from GBC patients with different periods of follow-up gallstone.

Weighted gene co-expression network analysis and whole genome sequencing identify potential lung cancer biomarkers

Background

Tuberculosis (TB) leads to an increased risk of lung cancer (LC). However, the carcinogenetic mechanism of TB remains unclear. We constructed gene co-expression networks and carried out whole-exome sequencing (WES) to identify key modules, hub genes, and the most recurrently mutated genes involved in the pathogenesis of TB-associated LC.

Methods

The data used in this study were obtained from the Gene Expression Omnibus (GEO) and WES. First, we screened LC-related genes in GSE43458 and TB-related genes in GSE83456 by weighted gene co-expression network analysis (WGCNA). Subsequently, we screened differentially expressed genes related to LC and TB in GSE42834. We also performed WES of 15 patients (TB, n = 5; LC, n = 5; TB+LC, n = 5), constructed mutational profiles, and identified differences in the profiles of the three groups for further investigation.

Results

We identified 278 hub genes associated with tumorigenesis of pulmonary TB. Moreover, WES identified 112 somatic mutations in 25 genes in the 15 patients. Finally, four common genes (EGFR, HSPA2, CECR2, and LAMA3) were confirmed in a Venn diagram of the 278 hub genes and the mutated genes from WES. KEGG analysis revealed various pathway changes. The PI3K-AKT signaling pathway was the most enriched pathway, and all four genes are included in this pathway. Thus, these four genes and the PI3K-AKT signaling pathway may play important roles in LC.

Conclusion

Several potential genes and pathways related to TB-associated LC were identified, including EGFR and three target genes not found in previous studies. These genes are related to cell proliferation, colony formation, migration, and invasion, and provide a direction for future research into the mechanisms of LC co-occurring with TB. The PI3K-AKT signaling pathway was also identified as a potential key pathway involved in LC development.

Cervical extracellular matrix hydrogel optimizes tumor heterogeneity of cervical squamous cell carcinoma organoids

Cervical cancer, primarily squamous cell carcinoma, is the most prevalent gynecologic malignancy. Organoids can mimic tumor development in vitro, but current Matrigel inaccurately replicates the tissue-specific microenvironment. This limitation compromises the accurate representation of tumor heterogeneity. We collected para-cancerous cervical tissues from patients diagnosed with cervical squamous cell carcinoma (CSCC) and prepared uterine cervix extracellular matrix (UCEM) hydrogels. Proteomic analysis of UCEM identified several tissue-specific signaling pathways including human papillomavirus, phosphatidylinositol 3-kinase-AKT, and extracellular matrix receptor. Secreted proteins like FLNA, MYH9, HSPA8, and EEF1A1 were present, indicating UCEM successfully maintained cervical proteins. UCEM provided a tailored microenvironment for CSCC organoids, enabling formation and growth while preserving tumorigenic potential. RNA sequencing showed UCEM-organoids exhibited greater similarity to native CSCC and reflected tumor heterogeneity by exhibiting CSCC-associated signaling pathways including virus protein-cytokine, nuclear factor κB, tumor necrosis factor, and oncogenes EGR1, FPR1, and IFI6. Moreover, UCEM-organoids developed chemotherapy resistance. Our research provides insights into advanced organoid technology through native matrix hydrogels.

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.