Characterization of alternative splicing events and prognostic signatures in gastric cancer.

BACKGROUND: Accumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation. METHODS: The expression profile of GC and normal samples were downloaded from TCGA. AS events were achieved from SpliceSeq database. Cox regression together with LASSO analysis were employed to identify survival-associated AS events (SASEs) and calculate risk scores. PPI and pathway enrichment analysis were implemented to determine the function and pathways of these genes. Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic Curves were used to evaluate the clinical significance of genes of SASEs. Q-PCR were applied to validate the hub genes on the survival prognosis in 47 GC samples. Drug sensitivity and immune cell infiltration analysis were conducted. RESULTS: In total, 48 140 AS events in 10 610 genes from 361 GC and 31 normal samples were analyzed. Through univariate Cox regression, 855 SASEs in 763 genes were screened out. Further, these SASEs were analyzed by PPI and 17 hub genes were identified. Meanwhile, using Lasso and multivariate Cox regression analysis, 135 SASEs in 132 genes related to 7 AS forms were further screened and a GC prognostic model was constructed. K-M curves indicates that high-risk group has poorer prognosis. And the nomogram analysis on the basis of the multivariate Cox analysis was disclosed the interrelationships between 7 AS forms and clinical parameters in the model. Five key genes were then screened out by PPI analysis and Differential Expression Gene analysis based on TCGA and Combined-dataset, namely STAT3, RAD51B, SOCS2, POLE2 and TSR1. The expression levels of AS in STAT3, RAD51B, SOCS2, POLE2 and TSR1 were all significantly correlated with survival by qPCR verification. Nineteen drugs were sensitized to high-risk patients and eight immune cells showed significantly different infiltration between the STAD and normal groups. CONCLUSIONS: In this research, the prognostic model constructed by SASEs can be applied to predict the prognosis of GC patients and the selected key genes are expected to become new biomarkers and therapeutical targets for GC treatment.

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression.

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i.e., survival prediction (by 4.9% on C-index), pathological stage classification (by 11.6% on accuracy), and lymph node classification (by 12.0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features.

Androgen receptor promotes cell stemness via interacting with co-factor YAP1 in gastric cancer.

Cancer stem cells (CSCs) have been proposed to explain tumor relapse and chemoresistance in various types of cancers, and androgen receptor (AR) has been emerged as a potential regulator of stemness in cancers. However, the underlying mechanism of AR-regulated CSCs properties and chemoresistance in gastric cancer (GC) remains unknown. Here, we shown that AR is upregulated in GC tissues and correlates with poor survival rate and CSCs phenotypes of GC patients. According to our experimental data, overexpression of AR upregulated the expression of CSCs markers and this was consistent with the result concluded from data analysis that the expression of AR was positively correlated with CD44 in GC patients. In addition, AR overexpression obviously enhanced the tumor sphere formation ability and chemoresistance of GC cells in vitro. Whereas these effects were attenuated by inhibition of AR. These results were further validated in vivo that MGC-803 cells overexpressing AR had stronger properties to initiate gastric tumorigenesis than the control cells, and inhibition of AR increased the chemosensitivity of GC cells. Mechanically, AR upregulated CD44 expression by directly binding to its promoter region and Yes-associated protein 1 (YAP1) served as the co-factor of AR, which was demonstrated by the fact that the promoting effects of AR on GC cells stemness were partially counteracted by YAP1 knockdown. Thus, this study revealed that AR facilitates CSCs properties and chemoresistance of GC cells via forming complex with YAP1and indicates a potential therapeutic approach to GC patients.

BPTF Drives Gastric Cancer Resistance to EGFR Inhibitor by Epigenetically Regulating the C-MYC/PLCG1/Perk Axis.

Erlotinib, an EGFR tyrosine kinase inhibitor, is used for treating patients with cancer exhibiting EGFR overexpression or mutation. However, the response rate of erlotinib is low among patients with gastric cancer (GC). The findings of this study illustrated that the overexpression of bromodomain PHD finger transcription factor (BPTF) is partially responsible for erlotinib resistance in GC, and the combination of the BPTF inhibitor AU-1 with erlotinib synergistically inhibited tumor growth both in vivo and in vitro. AU-1 inhibited the epigenetic function of BPTF and decreased the transcriptional activity of c-MYC on PLCG1 by attenuating chromosome accessibility of the PLCG1 promoter region, thus decreasing the expression of p-PLCG1 and p-Erk and eventually improving the sensitivity of GC cells to erlotinib. In patient-derived xenograft (PDX) models, AU-1 monotherapy exhibited remarkable tumor-inhibiting activity and is synergistic anti-tumor effects when combined with erlotinib. Altogether, the findings illustrate that BPTF affects the responsiveness of GC to erlotinib by epigenetically regulating the c-MYC/PLCG1/pErk axis, and the combination of BPTF inhibitors and erlotinib is a viable therapeutic approach for GC.

VPS35 promotes cell proliferation via EGFR recycling and enhances EGFR inhibitors response in gastric cancer.

BACKGROUND: Vacuolar protein sorting-associated protein 35 (VPS35) is a core component of the retromer complex which mediates intracellular protein transport. It is well known that dysfunctional VPS35 functions in the accumulation of pathogenic proteins. In our previous study, VPS35 was found to be a potential gene related to poor prognosis in gastric cancer. However, the biological functions of VPS35 in gastric cancer remain unclear. METHODS: Cell viability assays were performed to examine whether VPS35 affected cell proliferation. Immunoprecipitation and biotin assays showed that VPS35 bound to epidermal growth factor receptor (EGFR) in the cytoplasm and recycled it to the cell surface. Patient-derived xenografts and organoids were used to evaluate the effect of VPS35 on the response of gastric cancer to EGFR inhibitors. FINDINGS: VPS35 expression levels were upregulated in tumour tissues and correlated with local tumour invasion and poor survival in patients with gastric cancer. VPS35 promoted cell proliferation and increased tumour growth. Mechanistically, VPS35 selectively bound to endocytosed EGFR in early endosomes and recycled it back to the cell surface, leading to the downstream activation of the ERK1/2 pathway. We also found that high VPS35 expression levels increased the sensitivity of the xenograft and organoid models to EGFR inhibitors. INTERPRETATION: VPS35 promotes cell proliferation by recycling EGFR to the cell surface, amplifying the network of receptor trafficking. VPS35 expression levels are positively correlated with gastric cancer sensitivity to EGFR inhibitors, which offers a potential method to stratify patients for EGFR inhibitor utilisation. FUNDING: National Natural Science Foundation of China.

Identification of prognostic gene expression signatures based on the tumor microenvironment characterization of gastric cancer.

Increasing evidence has elucidated that the tumor microenvironment (TME) shows a strong association with tumor progression and therapeutic outcome. We comprehensively estimated the TME infiltration patterns of 111 gastric cancer (GC) and 21 normal stomach mucosa samples based on bulk transcriptomic profiles based on which GC could be clustered as three subtypes, TME-Stromal, TME-Mix, and TME-Immune. The expression data of TME-relevant genes were utilized to build a GC prognostic model-GC_Score. Among the three GC TME subtypes, TME-Stomal displayed the worst prognosis and the highest GC_Score, while TME-Immune had the best prognosis and the lowest GC_Score. Connective tissue growth factor (CTGF), the highest weighted gene in the GC_Score, was found to be overexpressed in GC. In addition, CTGF exhibited a significant correlation with the abundance of fibroblasts. CTGF has the potential to induce transdifferentiation of peritumoral fibroblasts (PTFs) to cancer-associated fibroblasts (CAFs). Beyond characterizing TME subtypes associated with clinical outcomes, we correlated TME infiltration to molecular features and explored their functional relevance, which helps to get a better understanding of carcinogenesis and therapeutic response and provide novel strategies for tumor treatments.

The dynamic alteration of transcriptional regulation by crucial TFs during tumorigenesis of gastric cancer.

BACKGROUND: The mechanisms of Gastric cancer (GC) initiation and progression are complicated, at least partly owing to the dynamic changes of gene regulation during carcinogenesis. Thus, investigations on the changes in regulatory networks can improve the understanding of cancer development and provide novel insights into the molecular mechanisms of cancer. METHODS: Differential co-expression analysis (DCEA), differential gene regulation network (GRN) modeling and differential regulation analysis (DRA) were integrated to detect differential transcriptional regulation events between gastric normal mucosa and cancer samples based on GSE54129 dataset. Cytological experiments and IHC staining assays were used to validate the dynamic changes of CREB1 regulated targets in different stages. RESULTS: A total of 1955 differentially regulated genes (DRGs) were identified and prioritized in a quantitative way. Among the top 1% DRGs, 14 out of 19 genes have been reported to be GC relevant. The four transcription factors (TFs) among the top 1% DRGs, including CREB1, BPTF, GATA6 and CEBPA, were regarded as crucial TFs relevant to GC progression. The differentially regulated links (DRLs) around the four crucial TFs were then prioritized to generate testable hypotheses on the differential regulation mechanisms of gastric carcinogenesis. To validate the dynamic alterations of gene regulation patterns of crucial TFs during GC progression, we took CREB1 as an example to screen its differentially regulated targets by using cytological and IHC staining assays. Eventually, TCEAL2 and MBNL1 were proved to be differentially regulated by CREB1 during tumorigenesis of gastric cancer. CONCLUSIONS: By combining differential networking information and molecular cell experiments verification, testable hypotheses on the regulation mechanisms of GC around the core TFs and their top ranked DRLs were generated. Since TCEAL2 and MBNL1 have been reported to be potential therapeutic targets in SCLC and breast cancer respectively, their translation values in GC are worthy of further investigation.

Identification of ARGLU1 as a potential therapeutic target for gastric cancer based on genome-wide functional screening data.

BACKGROUND: Due to the molecular mechanism complexity and heterogeneity of gastric cancer (GC), mechanistically interpretable biomarkers were required for predicting prognosis and discovering therapeutic targets for GC patients. METHODS: Based on a total of 824 GC-specific fitness genes from the Project Score database, LASSOCox regression was performed in TCGA-STAD cohort to construct a GC Prognostic (GCP) model which was then evaluated on 7 independent GC datasets. Targets prioritization was performed in GC organoids. ARGLU1 was selected to further explore the biological function and molecular mechanism. We evaluated the potential of ARGLU1 serving as a promising therapeutic target for GC using patients derived xenograft (PDX) model. FINDINGS: The 9-gene GCP model showed a statistically significant prognostic performance for GC patients in 7 validation cohorts. Perturbation of SSX4, DDX24, ARGLU1 and TTF2 inhibited GC organoids tumor growth. The results of tissue microarray indicated lower expression of ARGLU1 was correlated with advanced TNM stage and worse overall survival. Over-expression ARGLU1 significantly inhibited GC cells viability in vitro and in vivo. ARGLU1 could enhance the transcriptional level of mismatch repair genes including MLH3, MSH2, MSH3 and MSH6 by potentiating the recruitment of SP1 and YY1 on their promoters. Moreover, inducing ARGLU1 by LNP-formulated saRNA significantly inhibited tumor growth in PDX model. INTERPRETATION: Based on genome-wide functional screening data, we constructed a 9-gene GCP model with satisfactory predictive accuracy and mechanistic interpretability. Out of nine prognostic genes, ARGLU1 was verified to be a potential therapeutic target for GC. FUNDING: National Natural Science Foundation of China.

DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer.

SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer.

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

Systematical Analysis of the Cancer Genome Atlas Database Reveals EMCN/MUC15 Combination as a Prognostic Signature for Gastric Cancer.

Digestive cancers-including gastric cancer (GC), colorectal cancer, hepatocellular carcinoma, esophageal cancer, and pancreatic cancer-accounted for 26% of cancer cases and 35% of cancer deaths worldwide in 2018. It is crucial and urgent to develop biomarkers for the diagnosis, prognosis, and therapeutic benefits of digestive cancers, especially for GC, since the incidence of GC is lower only than lung cancer in China, is hard to detect at an early stage, and is associated with poor prognosis. Mucins, glycoproteins encoded by MUC family genes, act as a part of a physical barrier in the digestive tract and participate in various signaling pathways. Some mucins have been used or proposed as biomarkers for carcinomas, such as MUC16 (CA125) and MUC4. However, there are no systematic investigations on the association of MUC family members with diagnoses and clinical outcomes even though relevant data have been largely accumulated in the past decade. By analyzing transcriptomic and clinical data of digestive cancer samples from TCGA involving colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), and pancreatic adenocarcinoma (PAAD), it was found that expressions levels of MUC15, MUC13, and MUC21 were individually associated with survival for digestive cancers, and high expressions of EMCN (MUC14) and MUC15 were correlated with poor survival for STAD. Cox regression analysis indicated the predictive power of an EMCN/MUC15 combination for overall survival (OS) of GC patients, which was validated on an independent dataset from GEO. EMCN/MUC15 correlated genes were identified to be enriched in cancer-related processes, such as vasculature development, mitosis, and immunity. Therefore, we propose that an EMCN/MUC15 combination could be a potential prognostic signature for gastric cancer.

Core-shell poly(lactide-co-ε-caprolactone)-gelatin fiber scaffolds as pH-sensitive drug delivery systems.

Dual-drug-loaded pH-responsive fiber scaffolds were successfully prepared by coaxial electrospinning. These were designed with the aim of being sutured into the resection site after tumor removal, to aid recovery and prevent cancer recurrence. The shell was made up of a mixture of gelatin and sodium bicarbonate (added to provide pH-sensitivity), and was loaded with the anti-inflammatory drug ciprofloxacin; the core comprised poly(lactide-co-ε-caprolactone) with the chemotherapeutic doxorubicin hydrochloride. Scanning electron microscopy revealed most fibers were smooth and homogeneous. Transmission electron microscopy demonstrated the presence of a clear core/shell structure. The fiber scaffolds were further characterized using infrared spectroscopy and X-ray diffraction, which proved that both drugs were present in the fibers in the amorphous form. The gelatin shells were cross-linked with glutaraldehyde to enhance their stability, and water contact angle measurements used to confirm they remained hydrophilic after this process, with angles between 10 and 35°. This is important for onward applications, since a hydrophilic surface is known to encourage cell proliferation. During in vitro drug release studies, a rapid and acid-responsive release of ciprofloxacin was seen, accompanied by sustained and long-term doxorubicin release. Both the release profiles and the mechanical strength of the fibers can effectively be tuned through the sodium bicarbonate content of the fibers: for instance, the break stress varies from 2.00 MPa to 2.57 MPa with an increase in sodium bicarbonate content. The pH values of aqueous media exposed to the scaffolds decrease only slightly, by less than 0.5 pH units, over the two-month timescale, suggesting that only minimal fiber degradation occurs during this time. The fiber scaffolds also have good biocompatibility, as revealed by in vitro cytotoxicity experiments. Overall, our results demonstrate that the novel scaffolds reported here are promising pH-sensitive drug delivery systems, and may be candidates for use after tumor resection surgery.

Electrospun gelatin/sodium bicarbonate and poly(lactide-co-ε-caprolactone)/sodium bicarbonate nanofibers as drug delivery systems.

In this work, we report electrospun nanofibers made of model hydrophobic (poly(lactide-co-ε-caprolactone); PLCL) and hydrophilic (gelatin) polymers. We explored the effect on drug release of the incorporation of sodium bicarbonate (SB) into these fibers, using the potent antibacterial agent ciprofloxacin as a model drug. The fibers prepared are smooth and have relatively uniform diameters lying between ca. 600 and 850nm. The presence of ciprofloxacin in the fibers was confirmed using IR spectroscopy. X-ray diffraction showed the drug to be incorporated into the fibers in the amorphous form. In vitro drug release studies revealed that, as expected, more rapid drug release was seen with gelatin fibers than those made of PLCL, and a greater final release percentage was obtained. The inclusion of SB in the gelatin fibers imparts them with pH sensitivity: gelatin/SB fibers showed faster release at pH5 than pH7.4, while fibers without SB gave the same release profiles at both pHs. The PLCL fibers have no pH sensitivity, even when SB was included, as a result of their hydrophobic structure precluding the ingress of solvent. In vitro cell culture studies showed that all the fibers are able to promote cell proliferation. The ciprofloxacin loaded fibers are effective in inhibiting Escherichia coli and Staphylococcus aureus growth in antibacterial tests. Thus, the gelatin-based fibers can be used as pH-responsive drug delivery systems, with potential applications for instance in the treatment of tumor resection sites. Should these become infected, the pH would drop, resulting in ciprofloxacin being released and the infection halted.