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Background Schizophrenia is likely to be a consequence of DNA alterations that, together with environmental factors, will lead to protein expression differences and the ultimate establishment of the illness. The superior temporal gyrus is implicated in schizophrenia and executes functions such as the processing of speech, language skills and sound processing. Methods We performed an individual comparative proteome analysis using two-dimensional gel electrophoresis of 9 schizophrenia and 6 healthy control patients' left posterior superior temporal gyrus (Wernicke's area – BA22p) identifying by mass spectrometry several protein expression alterations that could be related to the disease. Results Our analysis revealed 11 downregulated and 14 upregulated proteins, most of them related to energy metabolism. Whereas many of the identified proteins have been previously implicated in schizophrenia, such as fructose-bisphosphate aldolase C, creatine kinase and neuron-specific enolase, new putative disease markers were also identified such as dihydrolipoyl dehydrogenase, tropomyosin 3, breast cancer metastasis-suppressor 1, heterogeneous nuclear ribonucleoproteins C1/C2 and phosphate carrier protein, mitochondrial precursor. Besides, the differential expression of peroxiredoxin 6 (PRDX6) and glial fibrillary acidic protein (GFAP) were confirmed by western blot in schizophrenia prefrontal cortex. Conclusion Our data supports a dysregulation of energy metabolism in schizophrenia as well as suggests new markers that may contribute to a better understanding of this complex disease.

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography–mass spectrometry (GC–MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

Previously thought to be nothing more than cellular debris, extracellular vesicles (EVs) are now known to mediate physiological and pathological functions throughout the body. We now understand more about their capacity to transfer nucleic acids and proteins between distant organs, the interaction of their surface proteins with target cells, and the role of vesicle‐bound lipids in health and disease. To date, most observations have been made in reductionist cell culture systems, or as snapshots from patient cohorts. The heterogenous population of vesicles produced in vivo likely act in concert to mediate both beneficial and detrimental effects. EVs play crucial roles in both the pathogenesis of diseases, from cancer to neurodegenerative disease, as well as in the maintenance of system and organ homeostasis. This two‐part review draws on the expertise of researchers working in the field of EV biology and aims to cover the functional role of EVs in physiology and pathology. Part I will outline the role of EVs in normal physiology.

It is clear from Part I of this series that extracellular vesicles (EVs) play a critical role in maintaining the homeostasis of most, if not all, normal physiological systems. However, the majority of our knowledge about EV signalling has come from studying them in disease. Indeed, EVs have consistently been associated with propagating disease pathophysiology. The analysis of EVs in biofluids, obtained in the clinic, has been an essential of the work to improve our understanding of their role in disease. However, to interfere with EV signalling for therapeutic gain, a more fundamental understanding of the mechanisms by which they contribute to pathogenic processes is required. Only by discovering how the EV populations in different biofluids change—size, number, and physicochemical composition—in clinical samples, may we then begin to unravel their functional roles in translational models in vitro and in vivo, which can then feedback to the clinic. In Part II of this review series, the functional role of EVs in pathology and disease will be discussed, with a focus on in vivo evidence and their potential to be used as both biomarkers and points of therapeutic intervention.

Graphical Abstract The hypothesis is discussed that prostate cancer marker lncRNA PCA3 was introduced into the human genome by an oncogenic virus, and that viral infection‐related mechanisms might underlie its overexpression and prostate cancer initiation and/or progression.

Background Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody–drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable. Methods We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based model was used to comprehend the decision-making process aimed at relevant tissue regions. Results Our results indicate that the effectiveness of classification models hinges on the consistency and dependability of assay-based tests for HER2, as the outcomes from these tests are utilized as the baseline truth for training our models. Through the use of explainable AI, we reveal histologic patterns associated with the HER2 subtypes. Conclusion Our findings offer a demonstration of how deep learning technologies can be applied to identify HER2 subgroup statuses, potentially enriching the toolkit available for clinical decision-making in oncology.

Background Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. Results Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. Conclusion signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Background Intestinal and diffuse gastric adenocarcinomas differ in clinical, epidemiological and molecular features. However, most of the concepts related to the intestinal-type are translated to gastric adenocarcinoma in general; thus, the peculiarities of the diffuse-type are underappreciated. Results Besides its growing importance, there are many gaps about the diffuse-type carcinogenesis and, as a result, its epidemiologic and pathogenetic features remain poorly understood. Conclusions Alternative hypotheses to explain these features are discussed, including the role of the gastric microbiota, medical therapies, and modifications in the stomach’s microenvironment.

Sporadic and inflammatory forms of colorectal cancer (CRC) account for more than 80% of cases. Recent publications have shown mechanistic evidence for the involvement of gut bacteria in the development of both CRC-forms. Whereas, colon and rectal cancer have been routinely studied together as CRC, increasing evidence show these to be distinct diseases. Also, the common use of fecal samples to study microbial communities may reflect disease state but possibly not the tumor microenvironment. We performed this study to evaluate differences in bacterial communities found in tissue samples of 18 rectal-cancer subjects when compared to 18 non-cancer controls. Samples were collected during exploratory colonoscopy (non-cancer group) or during surgery for tumor excision (rectal-cancer group). High throughput 16S rRNA amplicon sequencing of the V4-V5 region was conducted on the Ion PGM platform, reads were filtered using and clustered using . We observed significant increases in species richness and diversity in rectal cancer samples, evidenced by the total number of OTUs and the Shannon and Simpson indexes. Enterotyping analysis divided our cohort into two groups, with the majority of rectal cancer samples clustering into one enterotype, characterized by a greater abundance of and . At the phylum level, rectal-cancer samples had increased abundance of candidate phylum (also known as ) whilst non-cancer samples had increased abundance of . At the genera level, rectal-cancer samples had higher abundances of , and whereas non-cancer samples had higher abundances of , and . Two OTUs were more abundant among rectal-cancer patients seen through 16S rRNA amplicon sequencing, whose presence was confirmed by immunohistochemistry and enrichment verified by digital droplet PCR. Our findings point to increased bacterial richness and diversity in rectal cancer, along with several differences in microbial community composition. Our work is the first to present evidence for a possible role of bacteria such as and the phylum in rectal cancer, emphasizing the need to study tissue-associated bacteria and specific regions of the gastrointestinal tract in order to better understand the possible links between the microbiota and rectal cancer.

Gastric cancer is among the most common cancer and cause of cancer death. We conducted a meta-analysis of 25 case–control studies from the Stomach cancer Pooling Project to assess the association between fish or canned fish consumption and the risk of gastric cancer. 10,431 cases and 24,903 controls were available. We found no association between fish consumption and risk of gastric cancer (pooled odds ratios (OR) = 0.99; 95% confidence interval (CI) 0.86–1.13, for at least one serving/week vs none). Geographical differences were found: in Asia an increased intake of fish was associated with a lower stomach cancer risk. In the sensitivity analyses, fish consumption was associated to a lower risk of gastric cancer in models adjusted for family history of gastric cancer (OR = 0.80, 95% CI 0.72–0.89) and Helicobacter Pylori infection (OR = 0.72, 95% CI 0.60–0.88), but not for body mass index or energy intake. Seven studies collected information on canned fish (4525 cases and 8073 controls). No association was found for canned fish (OR = 0.96, 95% CI 0.82–1.13). In conclusion, our results provide evidence that fish and canned fish intake are not associated with gastric cancer risk, although geographical differences have been highlighted, with a lower risk of gastric cancer in Asia.