Editorial: Chronology of gastrointestinal cancers and gastrointestinal microbiota
Bibliographic record
Abstract
cancers have been reported that have brought awareness to the masses and helped to develop cancer prevention strategies. Analyzing tumor growth helps to estimate the time at which cancer or metastasis occurred and to predict the survival of cancer patients (Murakami and Matsubara, 2018, LaCourse et al., 2021, Bordry et al., 2021. These chronologies vary from case to case and may even differ for the cancers derived from the same organ (Murakami andMatsubara, 2018, Krueger et al., 2008). Gut microbiota maintains a healthy body and preserves symbiosis with the host immune system that generates protective responses against pathogens and regulatory pathways that sustain tolerance to commensal microbes (Engstrand andGraham, 2020, Stewart et al., 2020). Recently, the gut microbiota came into the limelight due to their association with multiple cancers, especially gastrointestinal cancers (Wang et al., 2022, Bordry et al., 2021, Engstrand and Graham, 2020. It is already an established fact that chronic Helicobacter pylori infection is an essential risk factor for atrophic gastritis, intestinal metaplasia and gastric cancer (Wang et al., 2014, Guo et al., 2020. Recently, it has been highlighted that the gut resident microbiota influences the response to cancer-related therapies ( Liu et al., 2022, Bessede and Megraud, 2022, Seeneevassen et al., 2021. However, gaps still exist regarding the functional activity and the microbiome changes in the gut during cancer development and progression. Thus, the alterations in the gut microbiota are being monitored to assess the antitumor response of the drugs and the modulation of the intestinal immune system (Singhal et al., 2023, Xu andYing, 2022).It has been indicated that dysbiosis of gut microbiota and associated metabolites contribute to carcinogenesis through multiple pathways, such as inducing inflammation, immune dysregulation, and genetic instability (Meng et al., 2018, Dai et al., 2021, Bakhti and Latifi-Navid, 2021. The current research topic aims to report the data related to the influence of gut microbiota on the development of gastrointestinal (GI) cancers (including esophageal, gastric, colorectal, liver, and pancreatic cancers) and new strategies for the prevention and treatment of GI cancers.After rigorous screening and reviewing, only four articles exploring new dimensions in this research field were included in the current topic. Gastrointestinal (GI) cancers are ranked among the most frequently reported cancer type worldwide and rated as the third leading cause of cancer-associated deaths. Chemotherapy, immunotherapy, radiation, and surgery are treatment options for various cancers, including GI. Since the chronology of gastrointestinal cancer is not fully explored, we provide some evidence to support the advancements in clinical and basic research, especially in gastroenterology. In the current topic, we selected studies describing the different aspects of GI cancers, including gut microbiota, the role of lncRNAs, Lymph node Metastasis (LNM), and NOSE therapy. Further and advanced studies are required to explain gastrointestinal cancers' chronology and underlying mechanisms for effective treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.035 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".