Comparison of site-specific gene expression levels in primary tumors and synchronous lymph node metastases in advanced gastric cancer

The relationship between the gastric cancer microbiome and clinicopathological factors: a metagenomic investigation from the 100,000 genomes project and The Cancer Genome Atlas

BACKGROUND

Findings from previous gastric cancer microbiome studies have been conflicting, potentially due to patient and/or tumor heterogeneity. The intratumoral gastric cancer microbiome and its relationship with clinicopathological variables have not yet been characterized in detail. We hypothesized that variation in gastric cancer microbial abundance, alpha diversity, and composition is related to clinicopathological characteristics.

METHODS

Metagenomic analysis of 529 GC samples was performed, including whole exome sequencing data from The Cancer Genome Atlas (TCGA) and whole genome sequencing data from the 100,000 Genomes Project. Microbial abundance, alpha diversity, and composition were compared across patient age, sex, tumor location, geographic origin, pathological depth of invasion, pathological lymph node status, histological phenotype, microsatellite instability status, and TCGA molecular subtype.

RESULTS

Gastric cancer microbiomes resembled previous results, with Prevotella, Selenomonas, Stomatobaculum, Streptococcus, Lactobacillus, and Lachnospiraceae commonly seen across both cohorts. Within the TCGA cohort, microbial abundance and alpha diversity were greater in gastric cancers with microsatellite instability, lower pathological depth of invasion, intestinal-type histology, and those originating from Asia. Microsatellite instability status was associated with microbiome composition in both cohorts. Sex and pathological depth of invasion were associated with microbiome composition in the TCGA cohort.

CONCLUSION

The intratumoral gastric cancer microbiome appears to differ according to clinicopathological factors. Certain clinicopathological factors associated with favourable outcomes in gastric cancer were observed to be associated with greater microbial abundance and diversity. This highlights the need for further work to understand the underlying biological mechanisms behind the observed microbiome differences and their potential clinical and therapeutic impact.

Habitat radiomics assists radiologists in accurately diagnosing lymph node metastasis of adenocarcinoma of the esophagogastric junction

OBJECTIVES

This study aimed to develop a habitat radiomics (HR) model capable of preoperatively predicting lymph node metastasis (LNM) in adenocarcinoma of the esophagogastric junction (AEG) and to implement its use in clinical practice.

METHODS

In this retrospective analysis, 337 patients from three centers were enrolled and divided into three cohorts: training, validation, and test (208, 52, and 77 patients, respectively). We constructed HR models, conventional radiomics models, and combined models to identify LNM in AEG. The area under the curve (AUC) was employed to identify the optimal model, which was then evaluated for assisting radiologists in the empirical and RADS groups in diagnosing LNM. Finally, the prediction process of the optimal model was visualized using SHAP plots.

RESULTS

The HR model demonstrated superior performance, achieving the highest AUC values of 0.876, 0.869, and 0.795 in the training, validation, and test cohorts, respectively. Regardless of seniority, the empirical group of radiologists showed a significant improvement in the AUC and accuracy when using the HR model, compared to working alone (p < 0.05). Furthermore, the RADS group radiologists exhibited strong reclassification ability, effectively reevaluating patients with false-negative LN initially classified as Node-RADS score 1 or 2 by themselves.

CONCLUSION

The HR model facilitates the accurate prediction of LNM in AEG and holds potential as a valuable tool to augment radiologists' diagnostic capabilities in daily clinical practice.

CRITICAL RELEVANCE STATEMENT

The habitat radiomics model could accurately predict the lymph node status of adenocarcinoma in the esophagogastric junction and assist radiologists in improving diagnostic efficacy, which lays the foundation for accurate staging and effective treatment.

KEY POINTS

Accurate lymph node diagnosis in esophagogastric junction adenocarcinoma is beneficial for prognosis. Habitat radiomics model accurately predicted and assisted physicians in diagnosing lymph nodes. The habitat model effectively reclassified false-negative lymph nodes at Node-RADS 1 and 2.

CT radiomics-based intratumoral and intertumoral heterogeneity indicators for prognosis prediction in gastric cancer patients receiving neoadjuvant chemotherapy

OBJECTIVES

CT-based intratumoral and intertumoral heterogeneity indicators were integrated to develop a prognostic model for locally advanced gastric cancer (LAGC) patients undergoing neoadjuvant chemotherapy (NACT).

METHODS

This retrospective study included 568 LAGC patients treated with NACT from two hospitals. The intratumor heterogeneity score (ITHscore) was developed to quantify the intratumoral heterogeneity of LAGCs on CT; intertumoral heterogeneity was characterized by combining the primary tumor (PT) and lymph node (LN) sizes on CT. CT indicators were measured on baseline and posttreatment CT scans; the reduction rates (%Δ) were calculated. The overall survival (OS) of all patients was recorded. Cox regression analysis was used to construct a preoperative survival prediction model (Pre-SPM) based on the baseline indicators and %Δ indicators. The predictive performance of Pre-SPM for OS was assessed. The clinicopathological data, including the ypTNM stage, were also collected to evaluate their impact on OS.

RESULTS

Patients with lower baseline ITHscore had better prognoses (p < 0.001). Approximately 13.01% of patients exhibited contradictory changes in PT and LN sizes. Cox regression analysis selected the baseline ITHscore, baseline PT area, %ΔPT, and %ΔLN to establish the Pre-SPM. In the external validation cohort, the c-index of Pre-SPM for predicting OS was 0.72, while the AUC for predicting 5-year OS was 0.73. After adjusting for the influence of clinicopathological features, including the ypTNM stage, Pre-SPM remained an independent prognostic factor.

CONCLUSION

The Pre-SPM model, combining intratumoral heterogeneity and intertumoral heterogeneity, has the potential to predict the OS of LAGC patients receiving NACT.

KEY POINTS

Question Increased tumor heterogeneity in LAGC affects prognosis, but effective non-invasive CT methods for assessing intratumoral and intertumoral heterogeneity are lacking. Findings ITHscore indicates intratumoral heterogeneity, while changes in PT and LN sizes reflect intertumoral heterogeneity. The Pre-SPM model, integrating both, independently predicts patient outcomes. Clinical relevance Pre-SPM enhances prognosis prediction by quantifying intratumoral and intertumoral heterogeneity, potentially guiding more personalized and effective treatment strategies for patients with LAGC.

Prognostic biomarkers and molecular pathways mediating Helicobacter pylori–induced gastric cancer: a network-biology approach

Cancer of the stomach is the second most frequent cancer-related death worldwide. The survival rate of patients with gastric cancer (GC) remains fragile. There is a requirement to discover biomarkers for prognosis approaches. Helicobacter pylori in the stomach is closely associated with the progression of GC. We identified the genes associated with poor/favorable prognosis in H. pylori–induced GC. Multivariate statistical analysis was applied on the Gene Expression Omnibus (GEO) dataset GSE54397 to identify differentially expressed miRNAs (DEMs) in gastric tissues with H. pylori–induced cancer compared with the H. pylori–positive with non-cancerous tissue. A protein interaction map (PIM) was built and subjected to DEMs targets. The enriched pathways and biological processes within the PIM were identified based on substantial clusters. Thereafter, the most critical genes in the PIM were illustrated, and their prognostic impact in GC was investigated. Considering p-value less than 0.01 and |Log2 fold change| as >1, five microRNAs demonstrated significant changes among the two groups. Gene functional analysis revealed that the ubiquitination system, neddylation pathway, and ciliary process are primarily involved in H. pylori–induced GC. Survival analysis illustrated that the overexpression of DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, and TXNIP was associated with poor prognosis, while increased MRPS5 expression was related to a favorable prognosis in GC patients. DOCK4, GNAS, CTGF, TGF-b1, ESR1, SELE, TIMP3, SMARCE1, TXNIP, and MRPS5 may be considered prognostic biomarkers for H. pylori–induced GC. However, experimental validation is necessary in the future.

Differential gene expression identifies KRT7 and MUC1 as potential metastasis-specific targets in sarcoma

BACKGROUND

Despite numerous discoveries regarding the molecular genesis and progression of primary cancers, the biology of metastasis remains poorly understood. Compared to very large numbers of circulating tumor cells that are now known to accompany nearly all cancers, a relatively limited number of lesions actually develop in most patients with metastases. We hypothesized that phenotypic changes driven by differential gene expression in a finite subpopulation of tumor cells render those cells capable of metastasis and sought to identify key pathways through analysis of gene expression in primary and metastatic lesions from the same patients.

METHODS

We compared whole-genome expression in 4 matched samples of primary and metastatic sarcoma, then evaluated candidate genes with differential expression via quantitative PCR in 30 additional matched sets, tumor tissue immunostaining, siRNA loss-of-function in a sarcoma cell migration assay, and clinical correlation with overall and disease-free survival after metastasectomy.

RESULTS

Comparison of microarray signals identified differential expression of cell adhesion genes, including upregulation of KRT7 and MUC1 in metastases; KRT7 and MUC1 upregulation was confirmed in 22 (73%) and 20 (67%) matched sets of metastatic/primary tumors, respectively. Silencing of KRT7 and MUC1 via targeted siRNAs suppressed sarcoma cell migration in vitro, and a significant correlation (two-sided) was observed between both KRT7 and MUC1 expression in metastases and overall patient survival.

CONCLUSION

KRT7 and MUC1 may play a significant role in enabling sarcoma metastasis, and they may therefore be important prognostic biomarkers as well as potential targets for therapeutic prevention of metastasis.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer

Patients with the luminal B subtype of breast cancer exhibit a poor prognosis, high metastatic risk and high incidence of chemotherapy resistance. Luminal B breast cancer is sub-classified into B1 and B2. The pathophysiological mechanism of luminal B2 breast cancer (LB2BC) progression has yet to be characterized. Therefore, the present study aimed to identify the genes involved in the pathogenesis of LB2BC. The data of 117 LB2BC expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and differentially expressed genes (DEGs) were identified by comparison with non-tumor tissue expression profiles. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) networks were used to obtain insight into the functions of DEGs. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis was performed to validate the expression level of DEGs in tissue samples. A total of 2,251 DEGs, including 759 upregulated and 1,492 downregulated genes, were identified between LB2BC and non-tumor tissues. The top 15 upregulated and downregulated genes were used to construct a PPI network: Epidermal growth factor receptor (EGFR), fibronectin-1 (FN1) and Polo-like kinase-1 had the highest connectivity degrees. KEGG analysis identified that DEGs were most significantly enriched in 'focal adhesion', 'pathways in cancer' and 'ECM-receptor interaction' pathways. The results of RT-qPCR demonstrated that EGFR was significantly downregulated in LB2BC, whereas FN1 was significantly upregulated, whereas neurotrophic receptor tyrosine kinase 2 (NTRK2) trended towards downregulation. In conclusion, the DEGs identified in the present study, including NTRK2, FN1 and EGFR, may serve pivotal roles in the tumorigenesis of LB2BC by affecting the 'focal adhesion', 'pathways in cancer' and 'ECM-receptor interaction' KEGG pathways.

Downregulation of HIF-1α inhibits the proliferation and invasion of non-small cell lung cancer NCI-H157 cells

Lung cancer, specifically non-small cell lung cancer (NSCLC), is the leading cause of cancer-associated mortality in the world. In previous years, almost no significant advancements have been made towards the molecular characterization of NSCLC, which highlights the requirement for novel target genes. Hypoxia inducible factor-1α (HIF-1α) is known to be essential in tumorigenesis, as it regulates the expression of numerous factors that are involved in angiogenesis, cellular proliferation and apoptosis. However, no direct association between HIF-1α and NSCLC treatment has previously been established. The aim of the present study was to characterize the effect of HIF-1α on NSCLC and to explore the possible mechanism. Additionally, HIF-1α small interfering (si)RNA and diamminedichloroplatinum (DDP) were used in combination to explore the combined effects on NSCLC cells. Lung carcinoma NCI-H157 cells were treated with HIF-1α small interfering (si)RNA, 5 µg/ml DDP or a combination of the two, and the proliferation, apoptosis and invasion ability of the cells were detected using a cell counting kit-8 assay, Annexin V/propidium iodide staining and a Transwell assay, respectively. In addition, the protein levels of caspase-3/9, anti-apoptotic protein B-cell lymphoma-2 (Bcl-2), vascular endothelial growth factor (VEGF), pigment epithelium-derived factor (PEDF), phosphoinositide 3-kinase (PI3K), phosphorylated (p-)PI3K, protein kinase B (AKT), p-AKT, extracellular signal-regulated kinase (ERK) and p-ERK were detected using western blot analysis. Similar to DPP treatment, HIF-1α siRNA treatment may reduce cell proliferation and the invasiveness of tumor cells while promoting apoptosis. Additionally, HIF-1α siRNA may increase the levels of the apoptotic proteins caspases 3 and 9 and inhibit the expression of Bcl-2. These anti-tumor effects may be acting through the VEGF/PEDF, PI3K/AKT and Raf/mitogen-activated protein kinase kinase/ERK signaling pathways. The effects of HIF-1α siRNA may be strengthened by DDP. The present data indicated that HIF-1α siRNA is important in the inhibition of NSCLC cells. Additionally, the effects of HIF-1α siRNA may be strengthened by DDP, which suggests that HIF-1α siRNA may be combined with DDP for the treatment of tumors.

Knockdown of EGFR inhibits growth and invasion of gastric cancer cells

The epidermal growth factor receptor (EGFR) is an oncogenic trans-membranous receptor, which is overexpressed in multiple human cancers. However, the role of EGFR in gastric cancer (GC) is still elusive. In this study, we aimed to investigate the expression and molecular mechanisms of EGFR in GC cells. Forty cases of GC and the corresponding adjacent non-cancerous tissues (ANCT) were collected, and the expression of EGFR was assessed using immunohistochemistry in biopsy samples. Furthermore, EGFR signaling was blocked by constructed recombinant small hairpin RNA lentiviral vector (Lv-shRAGE) used to transfect into human GC SGC-7901 cells. The expression of AKT, proliferating cell nuclear antigen (PCNA) and matrix metallopeptidase-9 (MMP-9) was detected by real-time PCR and western blotting assays. Cell proliferative activities and invasive capability were, respectively, determined by MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) and Transwell assays. Cell apoptosis and cycle distribution were analyzed by flow cytometry. EGFR was found highly expressed in cancer tissues compared with the ANCT and correlated with lymph node metastases. Knockdown of EGFR reduced cell proliferation and invasion of GC with decreased expression of AKT, PCNA and MMP-9 and induced cell apoptosis and cycle arrest. Upregulation of EGFR expression is associated with lymph node metastases of GC, and blockade of EGFR signaling suppresses growth and invasion of GC cells through AKT pathway, suggesting that EGFR may represent a potential therapeutic target for this aggressive malignancy.