COL12A1, a novel potential prognostic factor and therapeutic target in gastric cancer

Genome-wide investigation of lncRNAs revealed their tight association with gastric cancer

BACKGROUND

Gastric cancer (GC) is a significant health issue globally, ranking as the fifth most common cancer with over 10,000 new cases reported annually. Long non-coding RNA (lncRNA) has emerged as a critical player in cellular functions, influencing GC's development, growth, metastasis, and prognosis. However, our understanding of lncRNA's role in the pathogenesis of GC remains limited. Therefore, it is particularly important to explore the relationship between lncRNA and gastric cancer.

METHODS

we conducted a comprehensive analysis of RNA sequencing data from the GEO database and stomach adenocarcinoma (STAD) data from the TCGA database to identify lncRNAs that exhibit altered expression levels in GC and the mechanisms underlying lncRNA-mediated transcription and post-transcriptional regulation were explored.

RESULTS

This study uncovered 94 lncRNAs with differential expression and, through co-expression analysis, linked these to 1508 differentially expressed genes (DEGs). GO functional enrichment analysis highlighted that these DEGs are involved in critical pathways, such as cell adhesion and the positive regulation of cell migration. By establishing a lncRNA-miRNA-mRNA regulatory network, we found that the ceRNA mechanism, particularly involving RP11-357H14.17 and CTD-2377D24.4, could play a role in GC progression. Experimental validation of selected differentially expressed lncRNAs and mRNAs (including RP11-357H14.17-CLDN1, BBOX1, TRPM2-AS, CLDN1, PLAU, HOXB7) confirmed the RNA-seq results.

CONCLUSIONS

Overall, our findings highlight the critical role of the lncRNA-mRNA regulatory network in the development and progression of GC, offering potential biomarkers for diagnosis and targets for innovative treatment strategies.

A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.

Exploration of molecular markers related to chemotherapy efficacy of hepatoid adenocarcinoma of the stomach

PURPOSE

Preoperative neoadjuvant chemotherapy may not improve the prognosis of patients with hepatoid adenocarcinoma of the stomach (HAS), a rare pathological type of gastric cancer. Thus, the study aimed at the genomic and transcriptomic impacts of preoperative chemotherapy on HAS.

METHODS

Patients with HAS who underwent surgical resection at Peking University Cancer Hospital were retrospectively included in this study. Whole exome sequencing and transcriptome sequencing were performed on pre-chemotherapy, non-chemotherapy and post-chemotherapy samples. We then compared the alterations in molecular markers between the post-chemotherapy and non-chemotherapy groups, and between the chemotherapy-effective and chemotherapy-ineffective groups, respectively.

RESULTS

A total of 79 tumor samples from 72 patients were collected. Compared to the non-chemotherapy group, the mutation frequencies of several genes were changed after chemotherapy, including TP53. In addition, there was a significant increase in the frequency of frameshift mutations and cytosine transversion to adenine (C > A), appearance of COSMIC signature 6 and 14, and a reduced gene copy number amplification. Interestingly, the same phenomenon was observed in chemotherapy-ineffective patients. In addition, many HAS patients had ERBB2, FGFR2, MET and HGF gene amplification. Moreover, the expression of immune-related genes, especially those related to lymphocyte activation, was down-regulated after chemotherapy.

CONCLUSION

Chemotherapy is closely associated with changes in the molecular characteristics of HAS. After chemotherapy, at genomic and transcriptome level, many features were altered. These changes may be molecular markers of poor chemotherapeutic efficacy and play an important role in chemoresistance in HAS. In addition, ERBB2, FGFR2, MET and HGF gene amplification may be potential therapeutic targets for HAS.

Jian Yun Qing Hua Decoction inhibits malignant behaviors of gastric carcinoma cells via COL12A1 mediated ferroptosis signal pathway

BACKGROUND

Jian Yun Qing Hua Decoction (JYQHD), a traditional Chinese medicine decoction, which has been applied in the treatment of gastric cancer (GC). We attempt to confirm the anti-gastric cancer effect of JYQHD and explore the mechanism of JYQHD.

METHODS

Acute toxicity test was used to understand the toxicity of JYQHD. We studied the expression and prognostic outcome of COL12A1 within GC tissues through the network databases. Using several web-based databases, we analyzed the major components and targets of JYQHD, as well as known therapeutic targets in gastric cancer. The Venn diagram was utilized to obtain the overlapped genes. Lentiviral vector, shRNAs and plasmids, were used to transfect GC cells. Cell counting kit-8 (CCK8), sphere formation, malondialdehyde (MDA), glutathione (GSH), reactive oxygen species (ROS), Fe2+, transmission electron microscopy (TEM), quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), Western-Blot (WB), and immunohistochemical (IHC) assays were employed to investigate the role and mechanism of COL12A1 and JYQHD in GC.

RESULTS

The results showed that JYQHD was non-toxic and safe. JYQHD inhibited growth and sphere formation ability through inducing the ferroptosis of GC cells, and suppressed the GC cells induced subcutaneous xenograft tumor growth. COL12A1 was highly expressed in gastric cancer tissues, indicating poor prognosis. COL12A1 specifically enhanced GC cell progression and stemness via suppressing ferroptosis. JYQHD down-regulated COL12A1 in order to suppress the stemness of GC cells via inducing ferroptosis.

CONCLUSION

COL12A1 inhibited ferroptosis and enhanced stemness in GC cells. JYQHD inhibited the development of GC cells by inhibiting cancer cell stemness via the ferroptosis pathway mediated by COL12A1.

ABERRANT EXPRESSION OF COL14A1, CELRS3, and CTHRC1 IN BREAST CANCER СELLS

BACKGROUND

Collagens, which are the major components of the extracellular matrix involved in the regulation of tumor microenvironment, could be differentially expressed in breast cancer (BC) with different transcriptome profiling.

AIM

To analyze the transcript level expression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, COL14A1, CTHRC1, and CELRS3 genes and the clinical relevance of their differential expression in BC.

MATERIALS AND METHODS

The transcript level expression of the genes was analyzed using the quantitative real-time PCR (qPCR) in tumor tissue of 60 BC patients.

RESULTS

Overexpression of COL1A1, COL5A1, COL10A1, COL11A1, COL12A1, CTHRC, and CELRS3 anddown-regulated expression of COL14A1 were observed. COL14A1 down-regulation was associated with aggressive, basal, and Her-2/neu BC subtypes (p = 0.031). Overexpression of CELSR3 was found to be associated with the older age of the patients (> 55 years, p = 0.049). Further analysis with the TCGA BC data set has shown a concordance in the differential expression of the above genes. Furthermore, overexpression of CTHRC1 was associated with poor overall survival (OS), particularly with poor prognosis (p = 0.00042) for the luminal BC subtype. On the other hand, CELSR3 overexpression was associated with mucinous tumors and poor prognosis in post-menopausal women. In silicotarget prediction identified several BC-associated miRNAs and members of miR-154, -515, and -10 families to perform a likely regulatory role in the above ECM genes.

CONCLUSION

The present study shows that the expression of COL14A1 and CTHRC1 may serve as potential biological markers for the detection of basal BC and the prognosis of survival for patients with the luminal subtype of BC.

Bioinformatics and pathway enrichment analysis identified hub genes and potential biomarker for gastric cancer prognosis

Introduction

Gastric cancer is one of the most common cancers in the world. This study aimed to identify genes, biomarkers, and metabolic pathways affecting gastric cancer using bioinformatic analysis and meta-analysis.

Methods

Datasets containing gene expression profiles of tumor lesions and adjacent non-tumor mucosa samples were downloaded. Common differentially expressed genes between data sets were selected to identify hub genes and further analysis. Gene Expression Profiling and Interactive Analyses (GEPIA) and the Kaplan-Meier method were used to further validate the expression level of genes and plot the overall survivalcurve, respectively.

Results and disscussion

KEGG pathway analysis showed that the most important pathway was enriched in ECM-receptor interaction. Hub genes includingCOL1A2, FN1, BGN, THBS2, COL5A2, COL6A3, SPARC and COL12A1 wereidentified. The top interactive miRNAs including miR-29a-3p, miR-101-3p,miR-183-5p, and miR-15a-5p targeted the most hub genes. The survival chart showed an increase in mortality in patients with gastric cancer, which shows the importance of the role of these genes in the development of the disease and can be considered candidate genes in the prevention and early diagnosis of gastric cancer.

Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve

We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.

A novel metabolism-related prognostic gene development and validation in gastric cancer

BACKGROUND

The importance of metabolism-related alterations in the development of gastric cancer (GC) is increasingly recognized. The present study aimed to identify metabolism-related genes to facilitate prognosis of GC patients.

METHODS

Gene expression datasets and clinical information of GC patients were downloaded from TCGA and GEO databases. We scored the enrichment of human metabolism-related pathways (n = 86) in GC samples by GSV, constructed prognostic risk models using LASSO algorithm and multivariate Cox regression analysis, combined with clinical information to construct a nomogram, and finally cis score algorithm to analyze the abundance of immune-related cells in different subtypes. We used Weka software to screen for prognosis-related marker genes and finally validated the expression of the selected genes in clinical cancer patient tissues.

RESULTS

We identified that two GC metabolism-related signatures were strongly associated with OS and the levels of immune cell infiltration. Moreover, a survival prediction model for GC was established based on six GC metabolism-related genes. Time-dependent ROC analysis showed good stability of the risk prediction scoring model. The model was successfully validated in an independent ACRG cohort, and the expression trends of key genes were also verified in the GC tissues of patients. DLX1, LTBP2, FGFR1 and MMP2 were highly expressed in the cluster with poorer prognosis while SLC13A2 and SLCO1B3 were highly expressed in the cluster with better prognosis.

CONCLUSIONS

We identified a risk predictive score model based on six metabolism-related genes related to survival, which may serve as prognostic indicators and potential therapeutic targets for GC.

Dietary Moringa oleifera leaf powder improves jejunal permeability and digestive function by modulating the microbiota composition and mucosal immunity in heat stressed rabbits

Heat stress (HS) has detrimental effects on intestinal health by altering digestive and immune responses in animals. Dietary Moringa oleifera leaf powder (MOLP) has been implicated in ameliorating the impact of HS, but its effects in terms of intestinal function improvement under HS remain poorly characterized. Therefore, the current study investigated the impact of HS and MOLP supplementation on tight junction barriers, intestinal microbiota (jejunal digesta), and differentially expressed genes (DEGs) in jejunal mucosa of heat-stressed rabbits by using the next-generation sequencing techniques. A total of 21 male New Zealand White rabbits (32 weeks old mean body weight of 3318 ± 171 g) were divided into three groups (n = 7/group) as control (CON, 25 °C), heat stress (HS, 35 °C for 7 h daily), and HS with MOLP supplementation (HSM, 35 °C for 7 h daily) gavage at 200 mg/kg body weight per day for 4 weeks. The results indicated that MOLP supplementation increased mRNA expression of tight junction proteins and glutathione transferase activity, while the malonaldehyde concentration was decreased in the jejunal mucosa compared to HS group (P < 0.05). Furthermore, MOLP decreased the concentrations of lipopolysaccharide, pro-inflammatory cytokines, and myeloperoxidase compared with HS group (P < 0.05). Intestinal microbiota analysis revealed that at phyla level, the relative abundance of Bacteroidetes was higher in HSM group compared to CON and HS groups. MOLP supplementation also resulted in higher abundance of putatively health-associated genera such as Christensenellaceae R-7 gut group, Ruminococcaceae NK4A214 group, Ruminococcus 2, Lachnospiraceae NK4A136 group, and Lachnospiraceae unclassified along with higher butyrate levels in HSM group as compared to HS group. The analysis of DEGs revealed that MOLP reversed inflammatory response by downregulation of genes, such as TNFRSF13C, LBP, and COX2 in enriched KEGG pathway of NF-kβ pathway. MOLP supplementation also significantly upregulated the expression of genes in protein digestion and absorption pathway, including PRSS2, LOC100349163, CPA1, CPB1, SLC9A3, SLC1A1, and SLC7A9 in HSM group. Three genes of fibrillar collagens, i.e., COL3A1, COL5A3, and COL12A1 in protein digestion were also down-regulated in HSM group. In conclusion, MOLP supplementation could improve jejunal permeability and digestive function, positively modulate microbiota composition and mucosal immunity in heat-stressed rabbits.

Temporal profiling of the breast tumour microenvironment reveals collagen XII as a driver of metastasis

The tumour stroma, and in particular the extracellular matrix (ECM), is a salient feature of solid tumours that plays a crucial role in shaping their progression. Many desmoplastic tumours including breast cancer involve the significant accumulation of type I collagen. However, recently it has become clear that the precise distribution and organisation of matrix molecules such as collagen I is equally as important in the tumour as their abundance. Cancer-associated fibroblasts (CAFs) coexist within breast cancer tissues and play both pro- and anti-tumourigenic roles through remodelling the ECM. Here, using temporal proteomic profiling of decellularized tumours, we interrogate the evolving matrisome during breast cancer progression. We identify 4 key matrisomal clusters, and pinpoint collagen type XII as a critical component that regulates collagen type I organisation. Through combining our proteomics with single-cell transcriptomics, and genetic manipulation models, we show how CAF-secreted collagen XII alters collagen I organisation to create a pro-invasive microenvironment supporting metastatic dissemination. Finally, we show in patient cohorts that collagen XII may represent an indicator of breast cancer patients at high risk of metastatic relapse.

The distribution and organisation of matrix molecules in the tumour stroma help shape solid tumour progression. Here they perform temporal proteomic profiling of the matrisome during breast cancer progression and show that collagen XII secreted from CAFs provides a pro-invasive microenvironment.

Biomarkers of Cancer Stem Cells for Experimental Research and Clinical Application

The use of biomarkers in cancer diagnosis, therapy, and prognosis has been highly effective over several decades. Studies of biomarkers in cancer patients pre- and post-treatment and during cancer progression have helped identify cancer stem cells (CSCs) and their related microenvironments. These analyses are critical for the therapeutic application of drugs and the efficient targeting and prevention of cancer progression, as well as the investigation of the mechanism of the cancer development. Biomarkers that characterize CSCs have thus been identified and correlated to diagnosis, therapy, and prognosis. However, CSCs demonstrate elevated levels of plasticity, which alters their functional phenotype and appearance by interacting with their microenvironments, in response to chemotherapy and radiotherapeutics. In turn, these changes induce different metabolic adaptations of CSCs. This article provides a review of the most frequently used CSCs and stem cell markers.

Analysis of Yes-Associated Protein-1 (YAP1) Target Gene Signature to Predict Progressive Breast Cancer

Breast cancers are treated according to the ER/PR or HER2 expression and show better survival outcomes with targeted therapy. Triple-negative breast cancers (TNBCs) with a lack of expression of ER/PR and HER2 are treated with systemic therapy with unpredictable responses and outcomes. It is essential to investigate novel markers to identify targeted therapies for TNBC. One such marker is YAP1, a transcription co-activator protein that shows association with poor prognosis of breast cancer. YAP1 transcriptionally regulates the expression of genes that drive the oncogenic phenotypes. Here, we assess a potential YAP target gene signature to predict a progressive subset of breast tumors from METABRIC and TCGA datasets. YAP1 target genes were shortlisted based on expression correlation and concordance with YAP1 expression and significant association with survival outcomes of patients. Hierarchical clustering was performed for the shortlisted genes. The utility of the clustered genes was assessed by survival analysis to identify a recurring subset. Expression of the shortlisted target genes showed significant association with survival outcomes of HER2-positive and TNBC subset in both datasets. The shortlisted genes were verified using an independent dataset. Further validation using IHC can prove the utility of this potential prognostic signature to identify a recurrent subset of HER2-positive and TNBC subtypes.

Collagen Family and Other Matrix Remodeling Proteins Identified by Bioinformatics Analysis as Hub Genes Involved in Gastric Cancer Progression and Prognosis

Gastric cancer has remained in the top five cancers for over ten years, both in terms of incidence and mortality due to the shortage of biomarkers for disease follow-up and effective therapies. Aiming to fill this gap, we performed a bioinformatics assessment on our data and two additional GEO microarray profiles, followed by a deep analysis of the 40 differentially expressed genes identified. PPI network analysis and MCODE plug-in pointed out nine upregulated hub genes coding for proteins from the collagen family (COL12A1, COL5A2, and COL10A1) or involved in the assembly (BGN) or degradation of collagens (CTHRC1), and also associated with cell adhesion (THBS2 and SPP1) and extracellular matrix degradation (FAP, SULF1). Those genes were highly upregulated at the mRNA and protein level, the increase being correlated with pathological T stages. The high expression of BGN (p = 8 × 10⁻¹²), THBS2 (p = 1.2 × 10⁻⁶), CTHRC1 (p = 1.1 × 10⁻⁴), SULF1 (p = 3.8 × 10⁻⁴), COL5A1 (p = 1.3 × 10⁻⁴), COL10A1 (p = 5.7 × 10⁻⁴), COL12A1 (p = 2 × 10⁻³) correlated with poor overall survival and an immune infiltrate based especially on immunosuppressive M2 macrophages (p-value range 4.82 × 10⁻⁷–1.63 × 10⁻¹³). Our results emphasize that these genes could be candidate biomarkers for GC progression and prognosis and new therapeutic targets.

Somatic mutations in collagens are associated with a distinct tumor environment and overall survival in gastric cancer

Background

Gastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix.

Methods

We used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors.

Results

In stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors.

Conclusions

These observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09136-1.

Identification of Potential Prognostic Biomarkers Associated With Macrophage M2 Infiltration in Gastric Cancer

Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.

Identification of Three Core Secretome Genes Associated with Immune Infiltration in High Tumor Mutation Burden Across 14 Major Solid Tumors

Background

Secretome genes, encoding proteins secreted from the cell, are involved in the tumor immune response and correlated with levels of tumor mutation burden (TMB) in multiple tumors. This study aimed to identify core secretome genes and their potential association with immunomodulators and immune infiltration in high TMB groups across 14 major solid tumors through bioinformatics analysis.

Methods

Multi-omics data for 14 major solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. Patients were divided into high TMB (TMB-high) and low TMB (TMB-low) groups using the median TMB values for each of the solid tumors. The CIBERSORT algorithm was conducted to estimate the proportion of 22 tumor-infiltrating immune cells (TIICs). Kaplan-Meier analysis and the log-rank test were utilized to screened prognosis-related genes. The correlations between core secretome genes and TIICs were analyzed using Spearman correlation coefficients.

Results

In TMB-high groups, multi-omics data analysis revealed that secretome genes were strongly associated with clinical characteristics, and 65 prognosis-related secretome genes were screened. Among the prognosis-related genes, 21 core secretome genes were identified, and strongly associated with five types of TIICs, namely activated NK cells, follicular helper T cells, CD8 T cells, and macrophages M0 and M2. Notably, three secretome genes (ADAMTS12, COL12A1, and COL5A2) were significantly related to immunomodulators and TIICs in multiple solid tumors. In addition, 12 core secretome genes were significantly differentially expressed between responding and non-responding patients receiving immunotherapy. Furthermore, core secretome genes may be involved in the PI3K/AKT signaling pathway.

Conclusion

We examined the prognostic significance of secretome genes and their potential association with immunomodulators and immune infiltration across 14 major solid tumors. In summary, three secretome genes (ADAMTS12, COL12A1, and COL5A2) may be pivotal mediators of immune infiltration in TMB-high patients, which may help to identify patients who could benefit from immunotherapy.

Role of Lamin A/C as Candidate Biomarker of Aggressiveness and Tumorigenicity in Glioblastoma Multiforme

Nuclear lamina components have long been regarded as scaffolding proteins, forming a dense fibrillar structure necessary for the maintenance of the nucleus shape in all the animal kingdom. More recently, mutations, aberrant localisation and deregulation of these proteins have been linked to several diseases, including cancer. Using publicly available data we found that the increased expression levels of the nuclear protein Lamin A/C correlate with a reduced overall survival in The Cancer Genome Atlas Research Network (TCGA) patients affected by glioblastoma multiforme (GBM). We show that the expression of the LMNA gene is linked to the enrichment of cancer-related pathways, particularly pathways related to cell adhesion and cell migration. Mimicking the modulation of LMNA in a GBM preclinical cancer model, we confirmed both in vitro and in vivo that the increased expression of LMNA is associated with an increased aggressiveness and tumorigenicity. In addition, delving into the possible mechanism behind LMNA-induced GBM aggressiveness and tumorigenicity, we found that the mTORC2 component, Rictor, plays a central role in mediating these effects.

Lactobacillus acidophilus LA14 Alleviates Liver Injury

Although the probiotic Lactobacillus acidophilus LA14 is used worldwide, its effect on liver diseases remains unelucidated. Here, 32 rats were divided into four groups, gavaged with L. acidophilus LA14 (3 × 10⁹ CFU) or phosphate-buffered saline for 7 days, and then intraperitoneally injected with d-galactosamine or saline. After 24 h, blood, liver, ileum, and feces samples were collected for liver injury, inflammation, intestinal barrier, gut microbiota, metabolome, and transcriptome analyses. Pretreatment with L. acidophilus LA14 alleviated the d-galactosamine-induced elevation of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and bile acids; mitigated the histological injury to the liver and gut; and suppressed the inflammatory cytokines macrophage inflammatory protein 1α (MIP-1α), MIP-3α, and MCP-1. L. acidophilus LA14 also ameliorated the d-galactosamine-induced dysbiosis of the gut microbiota and metabolism, such as the enrichment of Bacteroides sp. strain dnLKV3 and the depletion of Streptococcus, butanoic acid, and N-acetyl-d-glucosamine. The underlying mechanism of L. acidophilus LA14 included prevention of not only the d-galactosamine-induced upregulation of infection- and tumor-related pathways but also the d-galactosamine-induced downregulation of antioxidation-related pathways during this process, as reflected by the liver transcriptome and proteome analyses. Furthermore, the administration of L. acidophilus LA14 to healthy rats did not alter the tested liver indicators but significantly enriched the beneficial Lactobacillus and Bifidobacterium species, promoted metabolism and regulated pathways to improve immunity. The ability of L. acidophilus LA14 to alleviate liver injury was further confirmed with an acetaminophen-induced mouse model. These results might provide a reference for future studies on the application of L. acidophilus LA14 for the prevention of liver injury.

IMPORTANCE The probiotic Lactobacillus acidophilus LA14 is widely used, but its effect on liver diseases has not been elucidated. We explored the protective effect of L. acidophilus LA14 on the liver using rats with d-galactosamine-induced liver injury. Pretreatment with L. acidophilus LA14 alleviated the d-galactosamine-induced elevation of serum ALT, AST, ALP, and bile acids, mitigated the histological injury to the liver and gut, and suppressed the inflammatory cytokines MIP-1α, MIP-3α, and MCP-1. These effects were correlated with the modulations of the gut microbiome, metabolome, and hepatic gene expression induced by L. acidophilus LA14. Moreover, the ability of L. acidophilus LA14 to alleviate liver injury was further confirmed with an acetaminophen-induced mouse model. These results might provide a reference for future studies on the application of L. acidophilus LA14 for the prevention of liver injury.

Bioinformatics Analysis of a Prognostic miRNA Signature and Potential Key Genes in Pancreatic Cancer

Background

In this study, miRNAs and their critical target genes related to the prognosis of pancreatic cancer were screened based on bioinformatics analysis to provide targets for the prognosis and treatment of pancreatic cancer.

Methods

R software was used to screen differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. A miRNA Cox proportional hazards regression model was constructed based on the miRNAs, and a miRNA prognostic model was generated. The target genes of the prognostic miRNAs were predicted using TargetScan and miRDB and then intersected with the DEGs to obtain common genes. The functions of the common genes were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. A protein-protein interaction (PPI) network of the common genes was constructed with the STRING database and visualized with Cytoscape software. Key genes were also screened with the MCODE and cytoHubba plug-ins of Cytoscape. Finally, a prognostic model formed by the key gene was also established to help evaluate the reliability of this screening process.

Results

A prognostic model containing four downregulated miRNAs (hsa-mir-424, hsa-mir-3613, hsa-mir-4772 and hsa-mir-126) related to the prognosis of pancreatic cancer was constructed. A total of 118 common genes were enriched in two KEGG pathways and 33 GO functional annotations, including extracellular matrix (ECM)-receptor interaction and cell adhesion. Nine key genes related to pancreatic cancer were also obtained: MMP14, ITGA2, THBS2, COL1A1, COL3A1, COL11A1, COL6A3, COL12A1 and COL5A2. The prognostic model formed by nine key genes also possessed good prognostic ability.

Conclusions

The prognostic model consisting of four miRNAs can reliably predict the prognosis of patients with pancreatic cancer. In addition, the screened nine key genes, which can also form a reliable prognostic model, are significantly related to the occurrence and development of pancreatic cancer. Among them, one novel miRNA (hsa-mir-4772) and two novel genes (COL12A1 and COL5A2) associated with pancreatic cancer have great potential to be used as prognostic factors and therapeutic targets for this tumor.

Some Reactive Lesions of Bone Are Probably Neoplasms: A Review

CONTEXT.—

A number of fibro-osseous and osteocartilaginous lesions, especially common in the small bones of hand and feet, pose a diagnostic challenge and have historically been thought to be reactive lesions. However, modern molecular techniques when supplementing clinical, radiographic, and histologic evaluation suggest they may, in fact, be neoplasms.

OBJECTIVE.—

To review the clinical presentation and histopathologic, molecular, and radiologic features of selective bone lesions, focusing most specifically on subungual exostosis, florid reactive periostitis, and bizarre periosteal osteochondromatous proliferation.

DATA SOURCES.—

Literature review and personal experience are the source of this review.

CONCLUSIONS.—

Some lesions previously thought to be reactive are locally aggressive and demonstrate reproducible molecular abnormalities, and thus may be neoplasms. Although most common in the bones of the fingers and toes, these lesions also occur in long and other bones. The clinical presentations, radiologic appearances, and histopathologic features often overlap, making the diagnosis challenging, and these lesions may require molecular evaluation to maximize accurate prognostication.

Identification of disease-promoting stromal components by comparative proteomic and transcriptomic profiling of canine mammary tumors using laser-capture microdissected FFPE tissue

Cancer-associated stroma (CAS) profoundly influences progression of tumors including mammary carcinoma (mCA). Canine simple mCA represent relevant models of human mCA, notably also with respect to CAS. While transcriptomic changes in CAS of mCA are well described, it remains unclear to what extent these translate to the protein level. Therefore, we sought to gain insight into the proteomic changes in CAS and compare them with transcriptomic changes in the same tissue. To this end, we analyzed CAS and matched normal stroma using laser-capture microdissection (LCM) and LC-MS/MS in a cohort of 14 formalin-fixed paraffin embedded (FFPE) canine mCAs that we had previously characterized using LCM-RNAseq. Our results reveal clear differences in protein abundance between CAS and normal stroma, which are characterized by changes in the extracellular matrix, the cytoskeleton, and cytokines such as TNF. The proteomics- and RNAseq-based analyses of LCM-FFPE show a substantial degree of correlation, especially for the most deregulated targets and a comparable activation of pathways. Finally, we validate transcriptomic upregulation of LTBP2, IGFBP2, COL6A5, POSTN, FN1, COL4A1, COL12A1, PLOD2, COL4A2, and IGFBP7 in CAS on the protein level and demonstrate their adverse prognostic value for human breast cancer. Given the relevance of canine mCA as a model for the human disease, our analysis substantiates these targets as disease-promoting stromal components with implications for breast cancer in both species.

Identification of Core Prognosis-Related Candidate Genes in Chinese Gastric Cancer Population Based on Integrated Bioinformatics

Background

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. There are great geographical differences in the incidence of GC, and somatic mutation rates of driver genes are also different. The present study is aimed at screening core prognosis-related candidate genes in Chinese gastric cancer population based on integrated bioinformatics for the early diagnosis and prognosis of GC.

Methods

In the present study, the differentially expressed genes (DEGs) in GC were identified using four microarray datasets from the Gene Expression Omnibus (GEO) database. The samples of these datasets were all from China. Functional enrichment analysis of DEGs was conducted to evaluate the underlying molecular mechanisms involved in GC. Protein-protein interaction (PPI) network and cytoHubba were performed to determine hub genes associated with GC. Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) were performed to validate the hub genes.

Results

A total of 240 DEGs were obtained through the RRA method, including 80 upregulated genes and 160 downregulated genes. Upregulated genes were mainly enriched in extracellular matrix organization, extracellular matrix, and extracellular matrix structural constituent. The downregulated genes were mainly enriched in digestion, extracellular space, and oxidoreductase activity. The KEGG pathway enrichment analysis showed that the upregulated genes were mainly associated with ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway. And downregulated genes were mainly associated with the metabolism of xenobiotics by cytochrome P450, metabolic pathways, and gastric acid secretion. The transcriptional and translational expression levels of the genes including COL1A1, COL5A2, COL12A1, and VCAN were higher in GC tissues than normal tissues.

Conclusion

A total of four genes including COL1A1, COL5A2, COL12A1, and VCAN were considered potential GC biomarkers in the Chinese population. And ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway were revealed to be important mechanisms of GC. Our findings provide novel insights into the occurrence and progression of GC in the Chinese population.

The Extracellular Matrix: An Accomplice in Gastric Cancer Development and Progression

The extracellular matrix (ECM) is a dynamic and highly organized tissue structure, providing support and maintaining normal epithelial architecture. In the last decade, increasing evidence has emerged demonstrating that alterations in ECM composition and assembly strongly affect cellular function and behavior. Even though the detailed mechanisms underlying cell-ECM crosstalk are yet to unravel, it is well established that ECM deregulation accompanies the development of many pathological conditions, such as gastric cancer. Notably, gastric cancer remains a worldwide concern, representing the third most frequent cause of cancer-associated deaths. Despite increased surveillance protocols, patients are usually diagnosed at advanced disease stages, urging the identification of novel diagnostic biomarkers and efficient therapeutic strategies. In this review, we provide a comprehensive overview regarding expression patterns of ECM components and cognate receptors described in normal gastric epithelium, pre-malignant lesions, and gastric carcinomas. Important insights are also discussed for the use of ECM-associated molecules as predictive biomarkers of the disease or as potential targets in gastric cancer.