| Literature DB >> 35408991 |
Claudia Zagami1, Diana Papp1, Alice Anna Daddi1, Francesco Boccellato1.
Abstract
The adult gastric mucosa is characterised by deep invaginations of the epithelium called glands. These tissue architectural elements are maintained with the contribution of morphogen signals. Morphogens are expressed in specific areas of the tissue, and their diffusion generates gradients in the microenvironment. Cells at different positions in the gland sense a specific combination of signals that instruct them to differentiate, proliferate, regenerate, or migrate. Differentiated cells perform specific functions involved in digestion, such as the production of protective mucus and the secretion of digestive enzymes or gastric acid. Biopsies from gastric precancerous conditions usually display tissue aberrations and change the shape of the glands. Alteration of the morphogen signalling microenvironment is likely to underlie those conditions. Furthermore, genes involved in morphogen signalling pathways are found to be frequently mutated in gastric cancer. We summarise the most recent findings regarding alterations of morphogen signalling during gastric carcinogenesis, and we highlight the new stem cell technologies that are improving our understanding of the regulation of human tissue shape.Entities:
Keywords: Helicobacter pylori; atrophic gastritis; carcinogenesis; gastric glands; intestinal metaplasia; morphogen signalling; mucosoids; organoids; stem cells; stomach
Mesh:
Year: 2022 PMID: 35408991 PMCID: PMC8998987 DOI: 10.3390/ijms23073632
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1(A) Schematic illustration of the gastric glands from the distal and proximal part of the healthy human stomach. (B) Morphological changes of stomach glands in consecutive pre-cancerous conditions of the Correas’ cascade: chronic gastritis, atrophic gastritis, and intestinal metaplasia.
Signalling pathways and their morphogenic roles in healthy tissue, in precancerous conditions, and in cancer. ↑ = increase ↓ = decreased.
| Signalling Pathway | Healthy Tissue | Precancerous Conditions | Cancer |
|---|---|---|---|
| WNT | ↑ Proliferation [ | Stomach hyperplasia in chronic gastritis [ | ↑ Aberrant signalling and nuclear β-catenin accumulation (APC and CDH1 loss of function) [ |
| BMPs | ↓ Stem cell proliferation [ | ↓ Hyperproliferation and polyp formation [ | ↓ BMP4 proliferation regulation (SMAD4 and TGFBR2 mutations) [ |
| NOTCH | ↑ Stem cell maintenance and proliferation [ | ↑ Proliferation (gene amplifications) [ | |
| EGFR | ↑ Foveolar differentiation and homeostasis [ | EGF hyperexpression foveolar differentiation (EGFR-MEK) [ | ↑ EGFR activation (KRAS and PIK3 mutation) (FGFR2 and MET amplification) [ |
| HH | Control of proliferation [ | ||
| Hippo | Involved in proliferation and differentiation [ | ↓ Polarity regulation [ |
Figure 2Oncogenic pathway analysis of the Stomach Adenoma (STAD) project of the TCGA biobank. Data were retrieved from the TCGA biobank using the “TCGAbiolinks” package; analysis was performed with the “maftools” package with the function “OncogenicPathways”. The pathways were ranked based on the frequency with which their member genes were found mutated in the cohort. The left part (blue bars) of the graphic represents the percentage of patients in which at least one of the genes belonging to the pathway has been found mutated. The right part (red bars) represents the percentage of genes listed in the pathway that were found mutated.
Frequency of mutations found in specific genes in samples from the Stomach Adenocarcinoma (STAD) project of the TCGA biobank. Data were retrieved from the TCGA biobank using the “TCGAbiolinks” package. Genes are listed in the different pathways according to the “OncogenicPathways” provided by the “maftools” package of the TGCA.
| RTK-RAS pathway | RAPGEF2 | 1.4 | AXIN1 | 2.1 | TGF-ꞵ pathway | DLL4 | 0.7 | ||
| Gene name | mutated/ | RASGRP1 | 1.4 | FZD1 | 2.1 | Gene name | Mutated/ | FHL1 | 0.7 |
| total (%) | IRS2 | 1.2 | FZD2 | 2.1 | total (%) | HDAC2 | 0.7 | ||
| ERBB4 | 9.7 | MAP2K1 | 1.2 | LGR4 | 2.1 | SMAD4 | 5.5 | NOV | 0.7 |
| ERBB3 | 8.3 | MET | 1.2 | DKK1 | 1.8 | TGFBR2 | 2.8 | NUMBL | 0.7 |
| KRAS | 6.7 | RASA2 | 1.2 | DKK2 | 1.8 | ACVR1B | 2.5 | PSEN1 | 0.7 |
| NF1 | 5.1 | RASAL3 | 1.2 | DVL2 | 1.8 | ACVR2A | 1.8 | PSEN2 | 0.7 |
| PLXNB1 | 4.2 | RASGRP2 | 1.2 | FZD7 | 1.8 | SMAD2 | 1.8 | SNW1 | 0.7 |
| ERBB2 | 3.9 | SHOC2 | 1.2 | LGR5 | 1.8 | TGFBR1 | 1.6 | ADAM10 | 0.5 |
| SCRIB | 3.9 | FNTB | 0.9 | LRP6 | 1.8 | SMAD3 | 1.4 | CIR1 | 0.5 |
| IGF1R | 3.7 | RASAL1 | 0.9 | WNT3A | 1.8 | NOTCH pathway | DTX3L | 0.5 | |
| ROS1 | 3.7 | RASGRP3 | 0.9 | WNT5A | 1.8 | Gene name | Mutated/ | EGFL7 | 0.5 |
| EGFR | 3.5 | SHC4 | 0.9 | PORCN | 1.6 | total (%) | HEY2 | 0.5 | |
| KSR2 | 3.5 | SPRED3 | 0.9 | DVL1 | 1.4 | FBXW7 | 6.2 | HEYL | 0.5 |
| RASA1 | 3.5 | CBL | 0.7 | FZD4 | 1.4 | CREBBP | 6 | PSENEN | 0.5 |
| FGFR1 | 3.2 | ERRFI1 | 0.7 | GSK3B | 1.4 | CNTN6 | 5.5 | APH1A | 0.2 |
| RASGRF2 | 3.2 | KSR1 | 0.7 | LEF1 | 1.4 | NCOR1 | 5.3 | DTX3 | 0.2 |
| IRS1 | 3 | NRAS | 0.7 | WNT7A | 1.4 | NOTCH2 | 5.3 | HES3 | 0.2 |
| NTRK3 | 3 | SHC1 | 0.7 | WNT7B | 1.4 | SPEN | 5.3 | HEY1 | 0.2 |
| RASAL2 | 3 | GRB2 | 0.5 | FZD6 | 1.2 | NCOR2 | 5.1 | LFNG | 0.2 |
| RET | 3 | MAPK3 | 0.5 | SFRP2 | 1.2 | NOTCH1 | 4.8 | NUMB | 0.2 |
| ARHGAP35 | 2.8 | MRAS | 0.5 | SFRP4 | 1.2 | CNTN1 | 4.4 | RBX1 | 0.2 |
| BRAF | 2.8 | PPP1CA | 0.5 | TLE3 | 1.2 | EP300 | 4.2 | RFNG | 0.2 |
| FGFR4 | 2.8 | RASGRP4 | 0.5 | WNT10A | 1.2 | KDM5A | 3.9 | SAP30 | 0.2 |
| INSR | 2.8 | RCE1 | 0.5 | WNT4 | 1.2 | NOTCH4 | 3.9 | HIPPO pathway | |
| SHC3 | 2.8 | SHC2 | 0.5 | WNT9A | 1.2 | NOTCH3 | 3.5 | Gene name | Mutated/ |
| ALK | 2.5 | CBLB | 0.2 | AXIN2 | 0.9 | CUL1 | 3.2 | total (%) | |
| FGFR2 | 2.5 | FNTA | 0.2 | DKK3 | 0.9 | JAG1 | 3 | FAT4 | 19.6 |
| NTRK2 | 2.5 | HRAS | 0.2 | SFRP1 | 0.9 | DLL1 | 2.5 | FAT3 | 14.8 |
| PDGFRB | 2.5 | ICMT | 0.2 | TCF7L1 | 0.9 | DTX1 | 2.5 | HMCN1 | 14.8 |
| RAPGEF1 | 2.5 | MAP2K2 | 0.2 | TLE1 | 0.9 | JAG2 | 2.5 | FAT2 | 11.1 |
| RASGRF1 | 2.5 | PIN1 | 0.2 | TLE2 | 0.9 | THBS2 | 2.5 | DCHS2 | 8.1 |
| DAB2IP | 2.3 | RAC1 | 0.2 | WNT16 | 0.9 | KAT2B | 1.8 | FAT1 | 6.5 |
| ERF | 2.3 | WNT pathway | WNT9B | 0.9 | NCSTN | 1.8 | DCHS1 | 5.3 | |
| INSRR | 2.3 | Gene name | mutated/ | FZD5 | 0.7 | RBPJ | 1.8 | TAOK2 | 4.6 |
| FLT3 | 2.1 | total (%) | RSPO1 | 0.7 | CTBP2 | 1.6 | SCRIB | 3.9 | |
| JAK2 | 2.1 | APC | 7.9 | TCF7 | 0.7 | DNER | 1.6 | CRB1 | 3.7 |
| KIT | 2.1 | CHD4 | 6.2 | WNT10B | 0.7 | MAML1 | 1.4 | CRB2 | 3 |
| NTRK1 | 2.1 | CTNNB1 | 4.6 | WNT11 | 0.7 | MAML3 | 1.4 | LATS1 | 3 |
| PDGFRA | 2.1 | LRP5 | 4.4 | WNT2 | 0.7 | RBPJL | 1.4 | TAOK3 | 3 |
| SOS1 | 2.1 | FZD10 | 3.7 | WNT5B | 0.7 | ADAM17 | 1.2 | LATS2 | 2.8 |
| RAF1 | 1.8 | TLE4 | 3.7 | DKK4 | 0.5 | CTBP1 | 1.2 | WWC1 | 2.8 |
| RASA3 | 1.8 | AMER1 | 3 | FRAT1 | 0.5 | DLL3 | 1.2 | PTPN14 | 2.1 |
| SOS2 | 1.8 | LTZR1 | 3 | FZD3 | 0.5 | DTX2 | 1.2 | TAOK1 | 2.1 |
| SPRED1 | 1.6 | CHD8 | 2.8 | FZD9 | 0.5 | DTX4 | 1.2 | HIPK2 | 1.8 |
| SPRED2 | 1.6 | DVL3 | 2.8 | SFRP5 | 0.5 | MAML2 | 1.2 | LLGL1 | 1.8 |
| ABL1 | 1.4 | FZD8 | 2.8 | WIF1 | 0.2 | ARRDC1 | 0.9 | TEAD4 | 1.8 |
| ARAF | 1.4 | RNF43 | 2.8 | WNT1 | 0.2 | HDAC1 | 0.9 | TEAD2 | 1.6 |
| CBLC | 1.4 | WNT8B | 0.5 | SOST | 0.5 | ITCH | 0.9 | LLGL2 | 1.4 |
| FGFR3 | 1.4 | ZNRF3 | 2.8 | WNT6 | 0.5 | APH1B | 0.7 | CSNK1D | 0.9 |
| PTPN11 | 1.4 | RCF7L2 | 2.5 | WNT8A/B | 0.5 | DLK1 | 0.7 | CSNK1E | 0.9 |
Summary of stem cell-driven in vitro models with their advantages depending on their application. “-” = none. “+, ++, +++” = minor, medium, great advantage for the specific application.
| Method | Regeneration | Differentiation | Tissue Architecture | Micro-Environment | Cross-Tissue Interaction | Host-Microbe Interaction |
|---|---|---|---|---|---|---|
| Organoids [ |
|
|
| + | + | + |
| Mucosoid cultures [ |
|
| - | + | ++ | +++ |
| Scaffold-supported stem cell driven models [ | + | +++ | +++ | ++ | + | +++ |