Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer

Sex specific molecular networks and key drivers of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD.

METHODS

We integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models.

RESULTS

Gene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34.

CONCLUSIONS

These findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.

SLC6A14 Depletion Contributes to Amino Acid Starvation to Suppress EMT-Induced Metastasis in Gastric Cancer by Perturbing the PI3K/AKT/mTORC1 Pathway

Metastasis is the main obstacle for the treatment of gastric cancer (GC), leading to low survival rate and adverse outcomes in CG patients. SLC6A14, a general amino acid transporter, can import all the essential amino acids in a manner dependent on the NaCl-generated osmotic gradients. Herein, we constructed GC cell sublines with high (SGC7901-M and MKN28-M) and low (MKN28-NM and SGC7901-NM) metastatic ability. Putative functional genes advancing GC metastasis were identified using mRNA microarray analysis and High-Content Screening. In particular, most significant change with a dampening trend in the migration potentiality of GC cells emerged after SLC6A14 gene was silenced. SLC6A14 expression was positively correlated with the migrated capability of different GC cell lines, and SLC6A14 was also constitutively expressed in GC patients with venous or lymphatic invasion, lymph node, or distant metastasis and poor prognosis, thus prompting SLC6A14 as a nonnegligible presence in supporting GC migration and invasion. Consistently, SLC6A14 depletion drastically depressed GC metastasis in vitro and in vivo. Most importantly, pharmacological blockade and gene silence of SLC6A14 both restricted epithelial-mesenchymal transition- (EMT-) driven GC metastasis, in which attenuated activation of the PI3K/AKT/mTORC1 pathway caused by amino acid starvation was involved. In summary, it is conceivable that targeting SLC6A14 has a tremendous promising for the treatment of metastatic GC.

LINC01857 promotes the proliferation, migration, and invasion of gastric cancer cells via regulating miR-4731-5p/HOXC6

The great importance of long non-coding RNAs (lncRNAs) in tumorigenesis has been acknowledged gradually. LINC01857 is previously reported to be highly expressed in gastric cancer (GC), while the regulatory mechanism of LINC01857 in gastric cancer is largely unknown. In this study, we detected high expression of LINC01857 from the gastric cancer microarray GSE109476. Additionally, LINC01857 expression is remarkably up-regulated in gastric cancer cell lines (AGS, MKN-45, HGC-27 and SGC-7901) compared to the normal gastric mucosal cell line GES-1. Functionally, LINC01857 knockdown suppressed the proliferation, migration, invasion, and epithelial-mesenchymal transformation (EMT) of GC cells, while LINC01857 overexpression promoted the proliferation, migration, invasion and EMT of GC cells. Furthermore, our data demonstrate that LINC01857 targeted miR-4731-5p and subsequently increased the expression of HOXC6 in GC. Rescue experiments showed that miR-4731-5p inhibition and HOXC6 overexpression could reverse the biological behavior of GC cells induced by LINC01857 knockdown. In conclusion, we demonstrated that LINC01857 sponged miR-4731-5p to promote the expression of HOXC6 and eventually acts as an oncogene in GC.

Abnormal phenotype of Nrf2 is associated with poor prognosis through hypoxic/VEGF-A-Rap1b/VEGFR2 pathway in gastric cancer

Metastasis is the major cause of death in gastric cancer patients and altered expression of Nrf2 is associated with cancer development. This study assessed Nrf2 and HO-1 expression and hypoxia-induced Nrf2 expression in the promotion of metastatic potential of gastric cancer cells, the relationship of Rap1b and Nrf2 was also discussed. Nrf2 and HO-1 expression were significantly associated with clinicopathological characteristic and were independent prognostic predictors in gastric cancer patients. Hypoxia up-regulated the expression of Nrf2, HO-1 and HIF-1α, whereas knockdown of Nrf2 inhibited cell invasion capacity and reduced the expression of Nrf2, HO-1 and HIF-1α. Patients in the Rap1b (+) Nrf2 (+) group had worst overall survival compared with those from other groups. Knockdown of Rap1b and Nrf2 significantly inhibited cell invasion capacity in the common group compared with the other groups. Hypoxia or VEGF-A facilitated the nuclear translocation of Nrf2 through Rap1b or VEGFR2. Hypoxia or VEGF-A did not induce the phosphorylation of P-Erk1/2 and P-Akt after knockdown of Rap1b or VEGFR2. Hypoxia promoted the gastric cancer malignant behavior through the upregulation of Rap1b and Nrf2. Hypoxia/VEGF-A-Rap1b/VEGFR2 facilitated the nuclear translocation of Nrf2. Targeting Rap1b and Nrf2 may be a novel therapeutic strategy for gastric cancer.

Comprehensive analysis of tumor immune microenvironment and prognosis of m6A-related lncRNAs in gastric cancer

Background

N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approaches of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between m6A and lncRNAs enrolling in the shaping of TIME remains unclear.

Methods

The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering method was implemented to classify GC patients into two clusters. Survival analysis, the infiltration level of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. A competing endogenous RNA (ceRNA) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which m6A-related lncRNAs enriched. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal lncRNAs, and risk model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the response to immune checkpoint inhibitors (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect expression patterns of the selected lncRNAs in the 35 tumor tissues and their paired adjacent normal tissues, and validated the prognostic value of risk model in our cohort (N = 35).

Results

The expression profiles of 15 lncRNAs were included to cluster patients into 2 subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and lower mutation rates of the genes. Different immune cell infiltration patterns were also displayed between the two clusters. GSEA showed that cluster1 preferentially enriched in tumor hallmarks and tumor-related biological pathways. KEGG pathway analysis found that the target mRNAs which m6A-related lncRNAs regulated by sponging miRNAs mainly enriched in vascular smooth muscle contraction, cAMP signaling pathway and cGMP-PKG signaling pathway. Next, eight lncRNAs were selected by LASSO regression algorithm to construct risk model. Patients in the high-risk group had poor prognoses, which were consistent in our cohort. As for predicting responses to ICIs therapy, patients from high-risk group were found to have lower tumor mutation burden (TMB) scores and account for large proportion in the Microsatellite Instability-Low (MSI-L) subtype. Moreover, patients had distinct immunophenoscores in different risk groups.

Conclusion

Our study revealed that the interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to ICIs therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09377-8.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Risk Factors to Predict Ocular Metastasis in Older Adult Patients With Gastric Cancer:LDL, ApoA1, and CA724

Objective: The purpose of this study was to explore the risk factors for Ocular metastasis (OM) of Gastric cancer (GC). Methods: This is a retrospective cohort study. A total of 1165 patients with GC were enrolled in this study and divided into OM and non-ocular metastasis (NOM) groups. Chi-square and independent samples t tests were used to determine whether differences in demographic characteristics and serological indicators (SI) between the two groups were significant. In addition, binary logistic regression was used to analyze the value of various SI as risk factors for OM in patients with GC. The statistical threshold was set as P < .05. Finally, receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of various SI in differentiating the occurrence of OM in patients with GC. Results: The incidence of OM in older adults with GC was 1.1%. Adenocarcinoma was the most common type of GC in both groups, and there was no significant difference in demographic characteristics between the groups. Low-density lipoprotein (LDL), carbohydrate antigen-724 (CA724), and carcinoembryonic antigen levels were significantly higher in the OM group than the NOM group, while those of apolipoprotein A1 (ApoA1) were significantly lower in the OM than the NOM group. Binary logistic analysis showed that LDL, ApoA1, and CA724 were independent risk factors for OM in patients with GC ( P < .001, P = .033, and P = .008, respectively). ROC curve analysis generated area under the curve (AUC) values of 0.881, 0.576, and 0.906 for LDL, ApoA1, and CA724, respectively. In addition, combined analysis of LDL, ApoA1, and CA724 generated the highest AUC value of 0.924 ( P < .001). Conclusion: Among SI, LDL, ApoA1, and CA724 have predictive value for the occurrence of OM in GC, with the three factors combined having the highest value.

Identification and epidemiological evaluation of gastric cancer risk factors: based on a field synopsis and meta-analysis in Chinese population

To summarize and assess the credibility and strength of non-genetic factors and genetic variation on gastric cancer risk, we performed a field synopsis and meta-analysis to identify the risk of gastric cancer in Chinese population. Cumulative evidence was graded according to the Venice criteria, and attributable risk percentage (ARP) and population attributable risk percentage (PARP) were used to evaluate the epidemiological effect. A total of 956 studies included non-genetic (404 studies) and genetic factors (552 studies) were quantified, and data on 1161 single nucleotide polymorphisms (SNPs) were available. We identified 14 non-genetic factors were significantly associated with gastric cancer risk. For the analysis of time trends, H. pylori infection rate in gastric cancer and population showed a downward trend. Meanwhile 22 variants were identified significantly associated with gastric cancer: 3 (PLCE1 rs2274223, PSCA rs2976392, MUC1 rs4072037) were high and 19 SNPs were intermediate level of summary evidence, respectively. For non-genetic factors, the top three for ARP were 54.75% (pickled food), 65.87% (stomach disease), and 49.75% (smoked and frying). For PARP were 34.22% (pickled food), 34.24% (edible hot food) and 23.66%(H. pylori infection). On the basis of ARP and PARP associated with SNPs of gastric cancer, the top three for ARP were 53.91% (NAT2, rs1799929),53.05% (NAT2 phenotype), and 42.85% (IL-10, rs1800896). For PARP (Chinese Han in Beijing) were 36.96% (VDR, rs731236), 25.58% (TGFBR2, rs3773651) and 20.56% (MUC1, rs4072037). Our study identified non-genetic risk factors and high-quality biomarkers of gastric cancer susceptibility and their contribution to gastric cancer.

Knockdown of CENPK inhibits cell growth and facilitates apoptosis via PTEN-PI3K-AKT signalling pathway in gastric cancer

Previous studies have indicated that centromere protein K (CENPK) is upregulated in several cancers and related to tumorigenesis. Nevertheless, the potential function of CENPK in gastric cancer (GC) remains unknown. Here, we investigated the function of CENPK on oncogenicity and explored its underlying mechanisms in GC. Our results showed that CENPK was dramatically overexpressed in GC and was associated with poor prognosis through bioinformatics analysis. We demonstrated that CENPK is upregulated in GC tissues and cell lines. Moreover, knockdown of CENPK significantly inhibited proliferation in vitro and attenuated the growth of implanted GCs in vivo. In addition, CENPK silencing induced G1 phase cell cycle arrest and facilitated apoptosis of GC cells. KEGG pathway analysis indicated that the PI3K-AKT signalling pathway was considerably enriched. Knockdown of CENPK decreased the expression of PI3K, p-Akt (Ser437) and p-GSK3β (Ser9) in GC cells, and increased the expression of PTEN. In conclusion, this study indicated that CENPK was overexpressed in GC and may promote gastric carcinogenesis through the PTEN-PI3K-AKT signalling pathway. Thus, CENPK may be a potential target for cancer therapeutics in GC.

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

Network models of primary melanoma microenvironments identify key melanoma regulators underlying prognosis

Melanoma is the most lethal skin malignancy, driven by genetic and epigenetic alterations in the complex tumour microenvironment. While large-scale molecular profiling of melanoma has identified molecular signatures associated with melanoma progression, comprehensive systems-level modeling remains elusive. This study builds up predictive gene network models of molecular alterations in primary melanoma by integrating large-scale bulk-based multi-omic and single-cell transcriptomic data. Incorporating clinical, epigenetic, and proteomic data into these networks reveals key subnetworks, cell types, and regulators underlying melanoma progression. Tumors with high immune infiltrates are found to be associated with good prognosis, presumably due to induced CD8+ T-cell cytotoxicity, via MYO1F-mediated M1-polarization of macrophages. Seventeen key drivers of the gene subnetworks associated with poor prognosis, including the transcription factor ZNF180, are tested for their pro-tumorigenic effects in vitro. The anti-tumor effect of silencing ZNF180 is further validated using in vivo xenografts. Experimentally validated targets of ZNF180 are enriched in the ZNF180 centered network and the known pathways such as melanoma cell maintenance and immune cell infiltration. The transcriptional networks and their critical regulators provide insights into the molecular mechanisms of melanomagenesis and pave the way for developing therapeutic strategies for melanoma.

While the molecular profiling of melanoma progression has been extensively characterised by large-scale studies, there is still need for the comprehensive integration of such datasets. Here the authors construct predictive gene network models for prognostic and therapeutic purposes.

Dihydroartemisinin inhibits the tumorigenesis and invasion of gastric cancer by regulating STAT1/KDR/MMP9 and P53/BCL2L1/CASP3/7 pathways

Dihydroartemisinin (DHA), an effective antimalarial drug, has been widely investigated as an anti-tumor agent. Although previous studies have indicated the potential therapeutic effects of DHA on multiple malignancies, its detailed molecular mechanisms in gastric cancer (GC) are still undocumented. In the present study, we applied network pharmacology and bioinformatics (gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses) to obtain the collective targets of DHA and GC and analyzed their involvement in constructing a protein-protein interaction (PPI) network. The top 10% hub targets in this network were identified, and TCGA database was utilized for the single gene analysis of their correlation with the prognosis of GC. CCK8, EdU, Transwell, and flow cytometry analyses were conducted, and subcutaneous xenograft tumor models were constructed to assess the effects of DHA on the tumorigenesis and invasion of GC. Furthermore, the targets of DHA were verified by molecular docking, quantitative real-time PCR (qPCR) and western blot analyses in GC cells. The results indicated that the common targets of DHA and GC were enriched in multiple cancer-related pathways including KDR, STAT1 and apoptosis signaling pathways, where the core genes included KDR, MMP9, STAT1, TP53, CASP3/7 and BCL2L1. The lowered expression of KDR and increased expression of TP53 and CASP7 harbored a favorable survival for patients with GC patients. CASP7 showed a positive correlation with CASP3 but a negative correlation with KDR and could be regarded as an independent protective factor for overall survival in GC. Moreover, DHA treatment induced cell apoptosis and suppressed the cell proliferation, DNA synthesis, cycle progression and invasive capabilities both in vitro and in vivo. DHA also upregulated p53, CASP3, and cleaved-CASP3 and downregulated BCL2L1, MMP9, KDR, p-KDR, STAT1 and p-STAT1 in GC cell lines. In conclusion, DHA could suppress the tumorigenesis and invasion of GC by regulating STAT1/KDR/MMP9 and p53/BCL2L1/CASP3/7 pathways. Our findings might provide a novel approach for the treatment of GC.

Gastric cancer mesenchymal stem cells regulate PD-L1-CTCF enhancing cancer stem cell-like properties and tumorigenesis

Rationale: Mesenchymal stem cells (MSCs) have been the focus of many studies because of their abilities to modulate immune responses, angiogenesis, and promote tumor growth and metastasis. Our previous work showed that gastric cancer MSCs (GCMSCs) promoted immune escape by secreting of IL-8, which induced programmed cell death ligand 1 (PD-L1) expression in GC cells. Mounting evidence has revealed that PD-L1 expression is related to intrinsic tumor cell properties. Here, we investigated whether GCMSCs maintained a pool of cancer stem cells (CSCs) through PD-L1 signaling and the specific underlying molecular mechanism.

Methods: Stem cell surface markers, aldehyde dehydrogenase (ALDH) activity, migration and sphere formation abilities were tested to evaluate the stemness of GC cells. PD-L1-expressing lentivirus and PD-L1 specific siRNA were used to analyze the effects of PD-L1 on GC cells stemness. Annexin V/PI double staining was used to assess apoptosis of GC cells induced by chemotherapy. Co-Immunoprecipitation (Co-IP) and Mass spectrometry were employed to determine the PD-L1 binding partner in GC cells. PD-L1^Negative and PD-L1^Positive cells were sorted by flow cytometry and used for limiting dilution assays to verify the effect of PD-L1 on tumorigenic ability in GC cells.

Results: The results showed that GCMSCs enhanced the CSC-like properties of GC cells through PD-L1, which led to the resistance of GC cells to chemotherapy. PD-L1 associated with CTCF to contribute to the stemness and self-renewal of GC cells. In vivo, PD-L1^Positive GC cells had greater stemness potential and tumorigenicity than PD-L1^Negative GC cells. The results also indicated that GC cells were heterogeneous, and that PD-L1 in GC cells had different reactivity to GCMSCs.

Conclusions: Overall, our data indicated that GCMSCs enriched CSC-like cells in GC cells, which gives a new insight into the mechanism of GCMSCs prompting GC progression and provides a potential combined therapeutic target.