Cancer Genome Research Articles

Investigation of the Mechanism of SEMA5A and Its Associated Autophagy-Related Genes in Gastric Cancer.

Semaphorin 5A (SEMA5A) and autophagy-related genes (ARGs) are pivotal in the pathogenesis of gastric cancer (GC). However, the potential regulatory role of SEMA5A in autophagy via its associated ARGs and the underlying molecular mechanisms remain unresolved. GC-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) between GC and control samples. The intersection of DEGs with ARGs produced candidate genes, which were further analyzed using Spearman correlation with SEMA5A to identify signature genes. Stratification of GC samples based on signature gene expression, followed by Kaplan-Meier survival analysis, identified key genes. Subsequent analyses, including gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint evaluation, were conducted on the key genes and SEMA5A. The mRNA expression level was quantified using real-time quantitative polymerase chain reaction (RT-qPCR). Ninety candidate genes were identified for Spearman correlation with SEMA5A, revealing TNFSF11, BMP6, ITPR1, and DLC1 with correlation coefficients exceeding 0.3. Survival analysis underscored DLC1 and BMP6 as key genes due to significant prognostic differences. GSEA implicated SEMA5A, BMP6, and DLC1 in the ECM receptor interaction pathway. Immune infiltration analysis indicated a negative correlation of SEMA5A and BMP6 with M1 macrophages, while DLC1 exhibited the strongest association with the immune checkpoint PDCD1LG2 (p < 0.05, cor = 0.43). The mRNA expression level of SEMA5A was significantly upregulated in AGS parental cells compared to GES-1 cells (p < 0.01), whereas DLC1 and BMP6 mRNA levels were markedly downregulated in AGS parental cells relative to GES-1 (p < 0.0001). ARGs BMP6 and DLC1, associated with SEMA5A, were identified, and their prognostic significance in GC was demonstrated. Additionally, their regulatory mechanisms were further elucidated through immune infiltration analysis and molecular network construction, providing a theoretical foundation for future research on the molecular mechanisms in patients with GC.

Cancer Care in Resource-Limited Countries: Jordan as an Example.

Jordan, a lower- to middle-income country, is relatively small, but with rapidly growing population and a challenged economy. Cancer is a growing health care problem and currently ranked second, after cardiovascular diseases, as a cause of death. Jordan's national cancer registry continues to suffer from problems mostly related to long lag time in reporting, absence of outcome data, and accurate staging. The number of new patients with cancer diagnosed in Jordan is increasing at an expected, none disturbing rate, fueled by population growth, improving life expectancy, changing population structure that hosts more older population, high rate of obesity, smoking, and lack of adequate exercise. However, age-standardized rate for cancer incidence is significantly lower than Western societies, yet, mortality rate is higher. Despite efforts, cancer is still diagnosed at more advanced stages and at younger age. The Jordan breast cancer program represents a great example of opportunistic screening that led to significant downstaging of breast cancer. Efforts to evaluate the feasibility of screening programs for colorectal and lung cancers are underway. Tremendous efforts resulted in the execution of the largest clinical cancer genetics program in the region that helps identify patients and at-risk relatives for hereditary cancers. Low-resourced countries, including Jordan, will not be able to keep up with the rapidly increasing cost of cancer care. A better access to clinical trials and moving cancer care to ambulatory settings should offset some of this cost. A cancer control program that addresses all issues of cancer care from screening and early detection, through active cost-effective treatment that assures wider access to palliative care, hospice, and survivorship programs under an expanded universal health coverage, is an urgent national health priority.

Exploring the prognosis value, immune correlation, and drug responsiveness prediction of homeobox C6 (HOXC6) in lung adenocarcinoma

BackgroundsHomeobox C6 (HOXC6) is a gene that encodes for a transcription factor involved in various cellular processes, including development and differentiation, and regulates cancer progression. However, the carcinogenesis and effect of HOXC6 in lung adenocarcinoma (LUAD) still need further investigation.MethodsThe differential HOXC6 expression levels at the mRNA and protein level were explored in multiple public datasets, including The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) dataset. Gene Expression Omnibus (GSE31210), International Cancer Genome Consortium (ICGC) datasets and the LUAD sample from Affiliated Hospital of Guangxi Medical University. We also investigated the relation between HOXC6 expression and clinicopathologic indexes. Furthermore, the correlation of immune infiltration, drug responsiveness and HOXC6 were explored.ResultsThe upregulated HOXC6 expressions at mRNA and protein levels were found in LUAD tissues compared to the normal lung tissues. Besides, the relatively shorter overall survival time, worse T and N stages, and lower immune scores were found in the high-expression HOXC6 subgroup. Notably, T cells regulatory (Tregs), Macrophages M0, and Plasma cells had the higher infiltration levels in the high-HOXC6 expression subgroup, while NK cells activated, Monocytes, Dendritic cells resting, and Mast cells resting had the lower infiltration levels. In drug sensitivity analysis, we revealed that LUAD patients with high-HOXC6 expression may be more susceptible to Camptothecin, Cytarabine, Docetaxel, Elesclomol, Rapamycin, Sorafinib, Temsirolimus, and Vorinostat.ConclusionsTaken together, there is a great potential for HOXC6 to become a prognosis biomarker and contribute to develop treatment strategies for LUAD patients. Further mechanism exploration and drug development for HOXC6 are needed.

Developing a prognostic signature: identifying differentially expressed genes in cardia and non-cardia gastric cancer for immunity and therapeutic sensitivity analysis.

Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM). We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data. A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups. This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

Identification of a lactylation-related gene signature to characterize subtypes of hepatocellular carcinoma using bulk sequencing data.

Prior studies indicate that lactylation regulates various biological mechanisms within cancer. However, lactylation-related genes (LRGs) have been found to have limited value in predicting the prognosis of hepatocellular carcinoma (HCC). The aim of this study was to review HCC LRGs using data from The Cancer Genome Atlas (TCGA). The RNA sequencing data and related clinical information of patients with HCC patients were collected from the TCGA database. A total of 20 LRGs were selected and bioinformatics analysis was performed. A consistency cluster analysis was conducted to classify the HCC tumors. Using a lactylation-related model of HCC, prognosis, immune cell infiltration, and immunotherapy was evaluated. A total of 4,378 genes were associated with prognosis. Twenty LRGs (i.e., ACIN1, RAN, PPP1CB, ALDOB, SUMO2, THOC2, HDAC1, SF3A1, SF3B1, HNRNPM, PPP1CC, SRRM1, PRPF6, HDAC2, H2AFV, ALYREF, H2AFZ, H2AFX, HNRNPK, and MAGOH) were identified. The 20 LRGs were used to divide TCGA-HCC patients into low-risk (G1) and high-risk (G2) categories. The upregulated genes in the G1 group primarily participate in the p53 signaling pathway, focal adhesion, extracellular matrix (ECM)-receptor interaction, and cell cycle, while the downregulated genes primarily participate in the glycolysis/gluconeogenesis, carbon metabolism, and biosynthesis of amino acids. The box plots showed a significant difference in the immune cell populations, with a higher abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells in the G1 than the G2 HCC samples. Further, the box plots showed higher expression levels of seven of the eight immune checkpoint inhibitor (ICI)-related genes in the G1 HCC samples than the G2 samples. There was a significant disparity in the cancer stem cell (CSC) scores between the G1 and G2 TCGA-HCC patients. Additionally, the G1 TCGA-HCC patients had higher tumor immune dysfunction and exclusion (TIDE) scores than the G2 TCGA-HCC patients. The prognosis of the HCC patients was also predicted using a six-LRG model, comprising HDAC2, SRRM1, SF3B1, HDAC1, THOC2, and PPP1CB. Strong correlation between LRGs and tumor classification as well as immunity in patients with HCC was identified. LRG signatures serve as reliable prognostic markers for HCC.

Bioinformatics analysis of SH2D4A in glioblastoma multiforme to evaluate immune features and predict prognosis.

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer in adults. This study aimed to obtain data on immune cell infiltration based on public datasets and to examine the prognostic significance of SH2 domain containing 4A (SH2D4A) for GBM. SH2D4A expression in GBM was analyzed using a Tumor Immunity Estimation Resource (TIMER) 2.0 dataset, and a gene expression profile interaction analysis (GEPIA), and the results were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The Chinese Glioma Genome Atlas (CGGA) dataset was used to assess the effect of SH2D4A on GBM patient survival. The SH2D4A co-expression network of the LinkedOmics dataset and GeneMANIA dataset was also investigated. Least absolute shrinkage and selection operator (LASSO) regression models and a nomogram were constructed to assess the prognosis of GBM patients. A Gene Set Enrichment Analysis (GSEA) was performed using The Cancer Genome Atlas (TCGA) dataset to find functional differences. The relationship between SH2D4A expression and tumor-infiltrating immune cells was analyzed using xCELL, the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm, and the TIMER dataset. We discovered that SH2D4A expression was upregulated in GBM patients, and elevated SH2D4A expression was also substantially correlated with tumor grade. The survival curve analysis and multivariate Cox regression analysis showed that high SH2D4A expression was a significant independent predictor of poor overall survival (OS) in GBM patients. The immunoassay results suggested that altered SH2D4A expression may affect the immune infiltration of GBM tissues and thus the survival outcomes of GBM patients. In addition to being a possible prognostic marker and therapeutic target for GBM, SH2D4A may also accelerate the progression of GBM.

Comprehensive pan-cancer analysis reveals CDC6 as a potential immunomodulatory agent and promising therapeutic target in pancreatic cancer.

CDC6 is critical in DNA replication initiation, but its expression patterns and clinical implications in cancer are underexplored. This study uses multi-omics data from The Cancer Genome Atlas (TCGA) to comprehensively analyze CDC6 across various cancers, aiming to evaluate its potential as a prognostic biomarker and explore its role in immunotherapy. By leveraging multi-omics data from TCGA, we conducted a comprehensive analysis of CDC6 expression across a variety of cancer types. Least absolute shrinkage and selection operator (LASSO) regression was employed to assess the association of CDC6 with key molecules implicated in pancreatic cancer. CDC6 expression was found to be significantly upregulated across a broad spectrum of cancers. High levels of CDC6 expression were associated with poor prognosis in several cancer types. Notable associations were observed between CDC6 expression and tumor mutational burden (TMB), microsatellite instability (MSI), as well as immune cell infiltration. Co-expression analysis revealed significant associations between CDC6 and prevalent immune checkpoint genes. A risk model incorporating CDC6-related genes, including CCNA1, CCNA2, CCND1, CCND2, CDC25B, CDC6, and CDK2, was developed for pancreatic cancer. CDC6 emerges as a promising prognostic biomarker and a potential target for immunotherapy across various cancers, including pancreatic cancer. It appears to modulate immune responses across cancer types, highlighting its regulatory role. Further exploration into the biological functions and clinical implications of CDC6 is warranted.

Integrative analysis of the role of the DPH gene family in hepatocellular carcinoma and expression validation.

The diphthamide (DPH) gene family is a group of genes that encode a set of enzymes that specifically modify eukaryotic elongation factor 2 (eEF2). Although previous studies have shown a link between the DPH genes (DPHs) and carcinogenesis, it is still unknown how the DPHs affect hepatocellular carcinoma (HCC). This study aimed to describe the expression, clinical significance, and potential mechanisms of DPHs in HCC. Real-time quantitative polymerase chain reaction (RT-qPCR), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) databases were utilized to research the expression of DPHs in HCC. The relationship between the expression of DPHs and the clinicopathological characteristics of HCC patients was investigated using TCGA data, and their diagnostic value was evaluated using receiver operating characteristic (ROC) curves and their prognostic value was analyzed using Kaplan-Meier curves and univariate and multivariate Cox regression analyses. Potential reasons for the upregulation of DPH2 and DPH3 (DPH2,3) expression in HCC were analyzed using multiple databases. Additionally, this study also explored the potential biological functions of DPH2,3 in HCC via gene sets enrichment analysis (GSEA). Correlation analysis of DPH2,3 expression with immune-related genes and immune checkpoints was performed using Spearman's correlation analysis, and single-sample GSEA was used to assess the distribution of tumor-infiltrating immune cell types. DPH1,7 expression was downregulated in tumor tissues while DPH2,3,5,6 expression was upregulated and showed a similar expression pattern in HCC. The results of the ROC analysis suggested that DPHs had valuable diagnostic properties in HCC. Kaplan-Meier analysis demonstrated that DPH2,3,7 had prognostic predictive value in HCC. Furthermore, univariate and multivariate Cox regression suggested that DPH2,3 was an independent predictive factor for HCC. GSEA analysis revealed that DPH2,3 might be tightly associated with Pathways in cancer, cell cycles, Fc gamma R mediated phagocytosis, etc. Additionally, DPH2,3 expression and numerous immune-related genes showed a positive connection, including chemokines receptor genes, immunosuppressive genes, chemokines genes, human leukocyte antigen (HLA) genes, and immunostimulatory genes. Further analysis of the association between 24 immune infiltrating cells and DPH2,3 revealed the greatest negative correlation between natural killer (NK) cells and Th17 cells, but the greatest positive correlation with Th2 cells. DPHs significantly influence the development and progression of HCC. DPH2,3 has significant diagnostic and prognostic potential and may be a promising target for immunotherapy.

Identification of a prognostic disulfidptosis-related gene signature in hepatocellular cancer.

Disulfidptosis regulate various biological processes in cancer. However, there is limited research on the genes related to disulfidptosis in predicting the prognosis of hepatocellular carcinoma (HCC). We aimed to develop a reliable disulfidptosis-related gene signature, which will characterize different HCC subtypes and predict their prognosis. The Cancer Genome Atlas (TCGA)-HCC dataset, comprising RNA sequencing data and clinical information, was obtained from the TCGA database. The crucial disulfidptosis-related genes were selected for bioinformatic analysis in HCC. HCC tumor classification was established through a consistent cluster analysis. The prognosis and immune-cell infiltration were investigated in association with a disulfidptosis-related HCC model. In TCGA-HCC patients, a total of 3,621 prognostic genes and 30 key prognostic disulfidptosis-related genes were identified. Using key prognostic disulfidptosis-related genes, TCGA-HCC patients were categorized into low- and high-risk clusters. The upregulated differentially expressed genes (DEGs) in high-risk cluster 1 (C1) could significantly impact cell cycle, DNA replication, and the p53 signaling pathway, whereas the pathways associated with the downregulated DEGs in high-risk C1 could significantly impact metabolism of xenobiotics by cytochrome P450, the PPAR signaling pathway, and tyrosine metabolism. Furthermore, the immune activity of the high-risk C1 group was different to that of the low-risk cluster 2 (C2) group. The 13 disulfidptosis-related genes were finally screened using least absolute shrinkage and selection operator (LASSO) regression analysis, including ANP32E, BOP1, RPN1, SLC7A11, PPIH, PCBP2, ME1, PRDX1, FLNC, INF2, MYH11, LRPPRC, and HNRNPM. The genes related to disulfidptosis are closely associated with tumor classification and immunity in patients with HCC. This is the first gene signature related to disulfidptosis demonstrated a strong predictive performance for the prognosis of HCC, which provide new perspectives for the diagnosis and treatment of HCC.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Bioinformatic prediction of miR-320a as a potential negative regulator of CDGSH iron-sulfur domain 2 (CISD2), involved in lung adenocarcinoma bone metastasis via MYC activation, and associated with tumor immune infiltration.

Ferroptosis, a form of regulated cell death associated with iron-dependent lipid peroxidation, plays a role in cancer progression. However, the specific mechanisms of ferroptosis in lung adenocarcinoma (LUAD) bone metastasis (BM) remain unclear. Using bioinformatics analysis, this study sought to identify the ferroptosis-associated genes involved in BM in LUAD, thus providing potential novel targets for the treatment of BM in LUAD. The RNA expression dataset GSE10799 was acquired from the Gene Expression Omnibus (GEO) database, and intersected with the ferroptosis dataset to identify ferroptosis-related differentially expressed genes (DEGs). The expression of candidate genes and their correlation with the prognosis of LUAD patients were validated in The Cancer Genome Atlas (TCGA) database. A protein gene interaction network was constructed using GeneMania and Retrieval of Interacting Genes/Proteins (STRING) databases. The association between the candidate genes and immune cells was assessed via TCGA and Tumor IMmune Estimation Resource (TIMER) databases. The potential mechanisms were elucidated by a gene set enrichment analysis (GSEA). The relevant microRNAs (miRNAs or miRs) that bind to the 3'untranslated region (3'UTR) end of candidate genes' mRNA was explored using the TargetScan database. The expression of these candidate miRNAs in LUAD was validated and the correlation between candidate miRNAs and candidate mRNAs was tested using the TCGA database. Finally, the clinical data of 40 LUAD patients were retrospectively analyzed to evaluate the clinical value of candidate gene expression for LUAD BM patients. In this research, 15 ferroptosis-related DEGs in LUAD BM were identified. TCGA database analysis indicated that patients with low levels of CDGSH iron-sulfur domain 2 (CISD2) in LUAD had better disease-specific survival (DSS), overall survival (OS), and a better progression-free interval (PFI) than those with high levels of CISD2. The TIMER database results show that the expression of CISD2 is correlated with the infiltration levels of various immune cells. The GSEA indicated that CISD2 might influence biological activity in LUAD by participating in cell-cycle regulation, mitochondrial translation, DNA damage repair, c-Myc (MYC) activation, and the P53 signaling pathway. Through the combined analysis of the TargetScan and TCGA databases, hsa-miR-320a was identified as the optimal upstream regulatory miRNA. The immunohistochemistry data indicated that the positive CISD2 expression rates and immunohistochemistry scores of the patients with BM were significantly higher than those of the patients without BM (P<0.05). The high expression of CISD2 is a significant risk factor for BM in LUAD. The downregulation of CISD2 expression may extend DSS, OS, and the PFI of LUAD patients. Thus, CISD2 could serve as a novel predictive biomarker for LUAD patients. Further, miR-320a might negatively regulate CISD2 and participate in LUAD BM by activating MYC. These data provide a potential perspective for developing anticancer therapies for LUAD-BM patients.

Potential molecular metabolic mechanisms underlying the effects of cimifugin in gastric cancer through single-cell and bulk RNA sequencing combined with network pharmacology.

Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, posing a significant clinical challenge due to its complex tumor microenvironment (TME) and metabolic heterogeneity. Despite continuous improvements in treatment strategies including surgery, chemotherapy, and targeted therapies, the metabolic reprogramming in GC continues to impede treatment efficacy, highlighting an urgent need for the development of novel therapeutic strategies. This persistent issue underscores the urgent need for novel therapeutic approaches that can effectively address the diverse and dynamic characteristics of GC. Cimifugin, a traditional Chinese medicine (TCM), has garnered attention for its potential role in alleviating inflammation, neurological disorders, pain, and metabolic disorders. Its multi-targeting properties and minimal side effects suggest a broad potential for cancer management, which is currently being explored. This study aims to delineate the molecular mechanisms that cimifugin may impact within the TME and metabolic pathways of GC, with the expectation of contributing to a deeper understanding of GC and the development of innovative treatment strategies. We identified the GC-related TME cell types and metabolic profiles and pathways by using relevant data from the single-cell RNA sequencing (scRNA-seq) database GSE134520 and the stomach adenocarcinoma (STAD) data set from The Cancer Genome Atlas (TCGA). We also assessed the effects of cimifugin on MKN28 cell proliferation, invasion, and migration. By using six public platforms, we comprehensively predicted the potential biological targets of cimifugin. Clinical prognosis and immunohistochemistry (IHC), molecular docking, and dynamics simulations were used to confirm the clinical relevance and stability of the aforementioned targets. Cimifugin inhibited MKN28 cell proliferation, migration, and invasion. Cimifugin may potentially act on various metabolic pathways in GC, including folate biosynthesis, xenobiotic metabolism via cytochrome P450 (CYP), glutathione metabolism, steroid hormone biosynthesis, and tryptophan metabolism. Cimifugin was noted to stably bind to three significant core targets associated with metabolic reprogramming in GC: AKR1C2, MAOB, and PDE2A; all three targets were strongly expressed in endocrince cells, pit mucous cells (PMCs), and common myeloid progenitors (CMPs). We verified the pharmacological effects of cimifugin on GC cell proliferation, invasion, and migration. AKR1C2, MAOB, and PDE2A were identified as the key targets of cimifugin in GC-related metabolic reprogramming and pathogenesis. Our research provides preliminary insights into the potential therapeutic effects of cimifugin, which could be considered for future exploration in the context of GC treatment.

Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications.

Breast cancer (BRCA) has surpassed lung cancer to become the malignant tumor with the highest incidence in female population. It occurs in malignant cells in breast tissue and is common worldwide. An increasing body of research indicates that M2 macrophages are critical to the occurrence and progression of BRCA. The aim of this work is to build a predictive model of genes related to invasion and migration of M2 macrophages, forecast the prognosis of patients with BRCA, and then evaluate the efficacy of some targeted treatments. The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database supplied the GSE20685 dataset, whereas the expression profile a clinical details of BRCA patients were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database. The genes linked to M2 macrophages and the differentially elevated genes of invasion and migration were found in GSE20685. To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation. After calculating each patient's risk score, the prognostic model was examined by analyses of immune infiltration, medication sensitivity, mutation, and enrichment of the risk score. The risk score had a strong correlation with both several immune cells and popular anti-tumor medications. Additionally, it was discovered that the risk score was a separate prognostic factor for BRCA. Based on invasion and migration-related M2 macrophage genes, we investigated and validated predictive characteristics in our study that may offer helpful insights into the progression and prognosis of BRCA.

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis

The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscopic examination of tissue slices, providing valuable insights into cellular structures and pathological features. On the other hand, genomic data provides information about tumor gene expression and functionality. The fusion of these two distinct data types is crucial for gaining a more comprehensive understanding of tumor characteristics and progression. In the past, many studies relied on single-modal approaches for tumor diagnosis. However, these approaches had limitations as they were unable to fully harness the information from multiple data sources. To address these limitations, researchers have turned to multi-modal methods that concurrently leverage both histopathological images and genomic data. These methods better capture the multifaceted nature of tumors and enhance diagnostic accuracy. Nonetheless, existing multi-modal methods have, to some extent, oversimplified the extraction processes for both modalities and the fusion process. In this study, we presented a dual-branch neural network, namely SG-Fusion. Specifically, for the histopathological modality, we utilize the Swin-Transformer structure to capture both local and global features and incorporate contrastive learning to encourage the model to discern commonalities and differences in the representation space. For the genomic modality, we developed a graph convolutional network based on gene functional and expression level similarities. Additionally, our model integrates a cross-attention module to enhance information interaction and employs divergence-based regularization to enhance the model’s generalization performance. Validation conducted on glioma datasets from the Cancer Genome Atlas unequivocally demonstrates that our SG-Fusion model outperforms both single-modal methods and existing multi-modal approaches in both survival analysis and tumor grading.

A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.

Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis. Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed. A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells. This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.

AIBP promotes cell proliferation and migration through the ERK1/2-MAPK signaling pathway in hepatocellular carcinoma.

As a highly aggressive cancer, hepatocellular carcinoma (HCC) is often found at an advanced stage and has a poor prognosis. Therefore, in addition to the surgical treatment of HCC, the drug therapy for HCC is still under continuous exploration. The primary apolipoprotein of high-density lipoproteins (HDLs) is apolipoprotein A-I binding protein (AIBP), which has a significant impact on cholesterol metabolism, angiogenesis, and a wide range of inflammatory disorders, including cancer. The AIBP function in HCC is, however, yet unknown. This study aims to reveal the underlying mechanisms of AIBP influencing HCC proliferation and migration through mitogen-activated protein kinase (MAPK) pathways. AIBP expression and its clinical prognostic association were investigated using The Cancer Genome Atlas (TCGA) data. The AIBP expression was studied in human HCC tissues using immunohistochemistry (IHC) and western blotting. Colony formation assays (CFAs) and cell counting kit-8 (CCK-8) were used to determine in vitro cell proliferation. Cell migration and invasion were evaluated using wound-healing and transwell assays. A xenograft tumor model was employed to investigate HCC cell proliferation in nude mice. Tissues from HCC patients had much increased AIBP expression compared to nearby normal tissues. The prognosis for patients was bleak when AIBP expression was high. When AIBP was overexpressed in SMMC-7721 cells, the cells may become more proliferative, migrative, and invasive. In contrast, the HCC-LM3 cells' ability to proliferate, migrate, and invade was drastically decreased once AIBP was knocked down in vitro. Furthermore, in vivo research showed that AIBP overexpression may enhance cell proliferation in HCC. Finally, we discovered that AIBP could control the MAPK signaling pathway-involved genes expression, including P-MEK, MEK, c-Myc, P-ERK1/2, and ERK1/2, and that GDC-0994, a specific ERK1/2 inhibitor, could suppress the AIBP overexpression induced cell migration and proliferation abilities. These findings demonstrated that the ERK/MAPK signaling pathway might be stimulated by AIBP in HCC tissues, leading to increased cell invasion, migration, and proliferation. It was hypothesized that AIBP might act as a useful prognostic and diagnostic marker for HCC.

Clinical features and prognostic biomarkers in patients with SMARCA4-mutated non-small cell lung cancer.

Patients with non-small cell lung cancer (NSCLC) carrying SMARCA4 mutations (SMARCA4-Mut) tend to have more advanced disease and a poor prognosis. However, due to the rarity of this mutation and the lack of related studies, the characteristics of SMARCA4-Mut NSCLC patients remains poorly determined. To clarify the clinical characteristics and prognostic factors of SMARCA4-Mut NSCLC, we initiated the present study to provide a clinical reference. We used data from two cohorts of NSCLC-SMARCA4-mutated samples: The Cancer Genome Atlas (TCGA) database and our center's clinical data. The TCGA database was used to obtain 481 NSCLC-SMARCA4-Mut samples for clinical characterization. The center collected data on 224 consecutive NSCLC patients treated between December 2020 to July 2022. Among them, 26 harbored SMARCA4 mutations, and 20 were eligible for inclusion in the study. Clinical, pathological, and molecular features, as well as prognostic role of SMARCA4 mutations were analyzed. Additionally, we analyzed the prognostic impact of Napsin A expression in SMARCA4-Mut patients. The TCGA database included 480 patients with SMARCA4-Mut NSCLC, 311 males (64.8%) and 169 females (35.2%), with a median age of 67 years. Among the 20 SMARCA4-Mut patients in our center series, 12 (60%) were males and 8 (40%) females, with a median age of 63. The intergroup prognostic correlation analysis showed that SMARCA4-Mut patients had significantly worse prognosis than those the wild-type SMARCA4 (SMARCA4-WT) (P=0.04). Within the SMARCA4-Mut group, patients with Napsin A expression had longer overall survival (OS) (P=0.03) than those without expression. Median survival in the Napsin A-positive and negative groups was 32 and 15 months, respectively. According to time-dependent receiver operating curve analysis, patients with Napsin A expression had significantly longer first-line treatment progression-free survival (PFS1) [area under the curve (AUC) =0.748] and OS (AUC =0.586). No prognostic value of Napsin A was found in patients SMARCA4-WT patients. SMARCA4-Mut is an adverse prognostic feature in NSCLC patients. Napsin A expression in SMARCA4-Mut patients is associated with prolonged OS.

GGT5 as a promising prognostic biomarker and its effects on tumor cell progression in gastric cancer.

Gastric cancer (GC) is a gastric malignant tumor with over 1 million new cases globally each year. There are many diagnostic methods for GC, but due to the hidden early symptoms of GC, early GC is easy to be missed and misdiagnosed, which affects the follow-up treatment of patients. The early and accurate diagnosis of GC is of great significance for the treatment and survival of GC patients. Our laboratory study found that gamma-glutamyl transferase (GGT) was highly expressed in GC patients, but the mechanism of GGT family genes in the occurrence and development of GC remained to be further studied. Therefore, this study aimed to explore the mechanism of GGT family functional gene GGT5 regulating the proliferation and migration of GC cells, and provide a possible new biomarker for the early diagnosis of GC. The value of serum GGT in GC patients was first statistically analyzed. Then, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the mRNA expression of GGT5 in GC, and its clinical relationship and function. Furthermore, expression of GGT5 was reduced by lentivirus RNA interference and verified by polymerase chain reaction (PCR), Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays were used to detect cell proliferation after GGT5 knockdown. Scratch and Transwell assays were applied to observe cell migration after knockdown of GGT5. Finally, Western blot assays were observed to demonstrate PI3K/AKT-MAPK and MMPs expression levels after knockdown of GGT5. Serum GGT was expressed at a high level in GC patients. GGT5 was highly expressed in GC tissues, and was associated with poor prognosis and clinical stage of GC. GGT5 might be involved in the regulation of vascular development and angiogenesis, as well as in the mechanisms of cell motility and migration, and it was positively correlated with the PI3K/AKT pathway. The proliferation and migration capacity of GC cells was dampened by downregulation of GGT5. GGT5 mediated proliferation and migration of GC cells by directly targeting PI3K/AKT-MAPK-MMPs pathways. Low expression of GGT5 reduced proliferation and migration in GC cells by modulating the PI3K/AKT-MAPK-MMPs pathway, and GGT5 might be a new target for GC.

Long non-coding RNA MIR4435-2HG promotes pancreatic cancer progression by regulating ABHD17C through sponging miR-128-3p.

The recently identified carcinogenic long non-coding RNA (lncRNA) MIR4435-2HG has been validated to contribute to the initiation and progression of several malignancies. Nonetheless, its specific mechanistic function in pancreatic cancer (PC) is yet to be determined. This study aims to investigate the expression and functional role of MIR4435-2HG in PC and to elucidate its potential mechanism. This study employed The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx)-Pancreas datasets for the analysis of MIR4435-2HG expression in PC and normal pancreatic tissues and its relations with prognosis in PC. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) was employed for analyzing MIR4435-2HG, miR-128-3p, and ABHD17C expressions within cells and tissues. Cell proliferation and apoptosis were detected in vitro through Cell Counting Kit 8 (CCK-8) assay and flow cytometry while utilizing transwell and wound healing assays to assess cell migration and invasion. Predicting miR-128-3p binding sites with MIR4435-2HG or ABHD17C was conducted via the online tool starBase and validated through a dual-luciferase reporter (DLR), RNA pull-down and RNA binding protein immunoprecipitation (RIP) assays. Herein, we deployed Western blot to assess protein expression levels. The in vivo role of MIR4435-2HG was studied using tumor xenografts. MIR4435-2HG overexpression exhibited a correlation with poor prognosis in PC. Knocking down MIR4435-2HG significantly hindered the proliferative, invading, and migrating PC cell abilities, accompanied by apoptosis induction, counteracted via a miR-128-3p inhibitor. Moreover, MIR4435-2HG could directly bind to miR-128-3p. Additionally, miR-128-3p directly targeted ABHD17C. Furthermore, in vitro as well as in vivo experiment results elucidated that knocking down MIR4435-2HG hindered PC progression by suppressing ABHD17C expression via miR-128-3p upregulation. MIR4435-2HG can serve as a dependable target for PC diagnosis and treatment by modulating the miR-128-3p/ABHD17C axis to promote its progression.

Therapeutic Potential of PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1 Genes in Triple-Negative Breast Cancer: Correlating Their Expression with Sensitivity to GSK 461364 and IKK 16 Drugs.

The treatment of triple-negative breast cancer (TNBC) has been associated with challenges due to the lack of expression of ER, PR, and HER2 receptors in tumor cells. This study aimed to identify genes with potential therapeutic targets in TNBC. Data from the cancer genome atlas regarding breast cancer (BC) were downloaded. After initial preprocessing, cancer samples were categorized into four groups: TNBC, HER2-positive, luminal A, and luminal B. Gene expression differences between these groups were calculated, focusing on genes that showed differential expression in TNBC. A protein-protein interaction network was conducted to identify hub genes among the candidate genes related to TNBC. The protein expression of candidate genes was assessed using immunohistochemistry data from the human protein atlas. Drug resistance and sensitivity associated with hub genes were identified using data from PharmacoDB. TNBC samples and the RT-qPCR method were used to confirm the results. Our findings revealed that eight genes, namely PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1, had significant upregulation at the RNA level in TNBC subgroup compared to other subgroups and could be considered hub genes in TNBC. Compared to other subgroups, their expression level in TNBC samples had high sensitivity and specificity. RT-qPCR results also demonstrated a significant increase in levels of SKA3 and PTTG1 in the TNBC compared to healthy tissue and other subgroups. The protein expression of these genes was notably high in some BC samples. PharmacoDB data showed that some candidate genes were closely linked to drug sensitivity of GSK 461364 and IKK 16. The results of this study showed a significant increase in the expression level of PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1 in TNBC compared to other BC subgroups. These genes show considerable promise as therapeutic targets for the TNBC subgroup.