UNASSIGNED: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC).
UNASSIGNED: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362.
UNASSIGNED: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers.
UNASSIGNED: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG).
UNASSIGNED: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

Post-transcriptional modifications in RNAs regulate their biological behaviors and functions. N1-methyladenosine (m1A), which is dynamically regulated by writers, erasers and readers, has been found as a reversible modification in tRNA, mRNA, rRNA and long non-coding RNA (lncRNA). m1A modification has impacts on the RNA processing, structure and functions of targets. Increasing studies reveal the critical roles of m1A modification and its regulators in tumorigenesis. Due to the positive relevance between m1A and cancer development, targeting m1A modification and m1A-related regulators has been of attention. In this review, we summarized the current understanding of m1A in RNAs, covering the modulation of m1A modification in cancer biology, as well as the possibility of targeting m1A modification as a potential target for cancer diagnosis and therapy.

Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman\'s correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score ( S g ) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)-KRAS (mutant gene) pair and CD82-NRAS pair in the DLD-1 colon cancer cell line (Spearman\'s correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein-protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.

Breast cancer (BC) is the most widely studied disease due to its higher prevalence, heterogeneity and mortality.
This study aimed to compare female BC trends among 21 world regions and globally over 28 year of data and to assess the association between sociodemographic transitions and female BC risks.
We used Global burden of disease study data and measure the female BC burden according to 21 world regions and sociodemographic indices (SDI). Age-period-cohort (APC) analysis was used to estimate time and cohort trend of BC in different SDI regions.
By world regions, age-standardised rate of female BC incidence were high in high-income-North America (ASR, 92.9; (95 %UI, 89.2, 96.6)), Western Europe (84.7; (73.4, 97.2)) and Australia (86; (81.7, 90.2)) in 2017. Whereas this rate was significantly increased by 89.5% between 1990 and 2017 in East Asia. We observed negative association between SDI and death, and DALYs in 25th and below percentiles of death and DALYs for the worldwide regions. Further, there was observed a strong negative correlation between SDI and case fatality percent (r2017 = -0.93; r1990 = -0.92) in both 2017 and 1990 worldwide and highest case fatality percentage was observed in Central Sub-Saharan Africa. Overall, the risk of case-fatality rate tends to decrease most noticeably in high middle SDI countries, and the reduction of the risk of case-fatality rate in the recent cohort was the lowest in the low SDI countries.
Remarkable variations exist among various regions in BC burden. There is a need to reduce the health burden from BC in less developed and under developing countries, because under-developed countries are facing higher degree of health-related burden. Public health managers should execute more classified and cost-effective screening and treatment interferences to lessen the deaths caused by BC, predominantly among middle and low SDI countries having inadequate healthcare supplies.

UNASSIGNED: Breast cancer (BC) survivors experience an increased burden of long-term comorbidities, including heart failure (HF). However, there is limited understanding of the risk for the development of HF subtypes, such as HF with preserved ejection fraction (HFpEF), in BC survivors.
UNASSIGNED: This study sought to estimate the incidence of HFpEF and HF with reduced ejection fraction (HFrEF) in postmenopausal BC survivors and to identify lifestyle and cardiovascular risk factors associated with HF subtypes.
UNASSIGNED: Within the Women\'s Health Initiative, participants with an adjudicated diagnosis of invasive BC were followed to determine the incidence of hospitalized HF, for which adjudication procedures determined left ventricular ejection fraction. We calculated cumulative incidences of HF, HFpEF, and HFrEF. We estimated HRs for risk factors in relation to HF, HFpEF, and HFrEF using Cox proportional hazards survival models.
UNASSIGNED: In 2,272 BC survivors (28.6% Black and 64.9% White), the cumulative incidences of hospitalized HFpEF and HFrEF were 6.68% and 3.96%, respectively, over a median of 7.2 years (IQR: 3.6-12.3 years). For HFpEF, prior myocardial infarction (HR: 2.83; 95% CI: 1.28-6.28), greater waist circumference (HR: 1.99; 95% CI: 1.14-3.49), and smoking history (HR: 1.65; 95% CI: 1.01-2.67) were the strongest risk factors in multivariable models. With the exception of waist circumference, similar patterns were observed for HFrEF, although none were significant. In relation to those without HF, the risk of overall mortality in BC survivors with hospitalized HFpEF was 5.65 (95% CI: 4.11-7.76), and in those with hospitalized HFrEF, it was 3.77 (95% CI: 2.51-5.66).
UNASSIGNED: In this population of older, racially diverse BC survivors, the incidence of HFpEF, as defined by HF hospitalizations, was higher than HFrEF. HF was also associated with an increased mortality risk. Risk factors for HF were largely similar to the general population with the exception of prior myocardial infarction for HFpEF. Notably, both waist circumference and smoking represent potentially modifiable factors.

OBJECTIVE: The prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.
METHODS: The gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.
RESULTS: A total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.
CONCLUSIONS: Our study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.

BACKGROUND: A deeper knowledge of the functional versatility and dynamic nature of the ECM has improved the understanding of cancer biology. Translational Significance: This work provides an in-depth view of the importance of the ECM to develop more mimetic breast cancer models, which aim to recreate the components and architecture of tumor microenvironment. Special focus is placed on decellularized matrices derived from tissue and cell culture, both in procurement and applications, as they have achieved great success in cancer research and pharmaceutical sector. Abstract: The extracellular matrix (ECM) is increasingly recognized as a master regulator of cell behavior and response to breast cancer (BC) treatment. During BC progression, the mammary gland ECM is remodeled and altered in the composition and organization. Accumulated evidence suggests that changes in the composition and mechanics of ECM, orchestrated by tumor-stromal interactions along with ECM remodeling enzymes, are actively involved in BC progression and metastasis. Understanding how specific ECM components modulate the tumorigenic process has led to an increased interest in the development of biomaterial-based biomimetic ECM models to recapitulate key tumor characteristics. The decellularized ECMs (dECMs) have emerged as a promising in vitro 3D tumor model, whose recent advances in the processing and application could become the biomaterial by excellence for BC research and the pharmaceutical industry. This review offers a detailed view of the contribution of ECM in BC progression, and highlights the application of dECM-based biomaterials as promising personalized tumor models that more accurately mimic the tumorigenic mechanisms of BC and the response to treatment. This will allow the design of targeted therapeutic approaches adapted to the specific characteristics of each tumor that will have a great impact on the precision medicine applied to BC patients.

According Global Cancer Statistics 2020 GLOBOCAN estimates female breast cancer was found as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), and the fourth leading cause (6.9%) of cancer death among women worldwide. Identification of new diagnostic marker sharply characterize the tumor feature is intensive need. The present work was performed to investigate the involvement of the INF-γ + 874 T/A gene polymorphism in different breast cancer prognostic factors. Polymorphism detection analysis was performed on 163 subjects from breast cancer patients, 79 with inflamed cells of breast patients and 144 controls. The gene polymorphism was detected using the amplification refractory mutation system- polymerase chain reaction method (ARMS-PCR). The distribution of INF-γ T + 874A gene polymorphism shows strong significant association between INF-γ + 874 T/A genotypes TT in BC patients (ORTT: 6.41 [95% CI = 2.72-15.1] P < 0.0001) as well as strong significant association regarding T allele (ORT: 1.99 [95% CI = 1.43-2.76] P < 0.0001) when compared to the healthy control. In ICB group the strong association was noted with INF-γ + 874 T/A genotypes AT genotype (ORAT: 2.28 [95% CI = 1.22-4.29] P = 0.007). From the different histological BC hormonal markers the human epidermal growth factor receptor 2 (HER2) was showing significant association in INF-γ + 874 T/A genotypes TT (P = 0.03) and recessive model (TT versus AA + AT P = 0.03). Concerning different BC prognostic models, the poor prognostic one of luminal B, (ER+ve PR+ve Her2+ve) show significant association in the host INF-γ + 874 T/A genotype (TT, P = 0.03) and recessive model (TT versus AA + AT P = 0.02) when compared to the good prognostic hormonal status luminal A model, (ER+ve PR+ve Her2-ve). It seems that this is the first study that interested in correlate the INF-γ + 874 T/A gene polymorphisms in Egyptian BC patients. T allele, TT genotype and recessive model of the INF-γ + 874 T/A gene variants were documented as risk factors for BC pathogenesis. It may be used as practical biomarker to guide the BC carcinogenesis and risk process.

Breast cancer arises as a result of multiple interactions between environmental and genetic factors. Conventionally, breast cancer is treated based on histopathological and clinical features. DNA technologies like the human genome microarray are now partially integrated into clinical practice and are used for developing new \"personalized medicines\" and \"pharmacogenetics\" for improving the efficiency and safety of cancer medications. We investigated the effects of four established therapies-for ER+ ductal breast cancer-on the differential gene expression. The therapies included single agent tamoxifen, two-agent docetaxel and capecitabine, or combined three-agents CAF (cyclophosphamide, doxorubicin, and fluorouracil) and CMF (cyclophosphamide, methotrexate, and fluorouracil). Genevestigator 8.1.0 was used to compare five datasets from patients with infiltrating ductal carcinoma, untreated or treated with selected drugs, to those from the healthy control. We identified 74 differentially expressed genes involved in three pathways, i.e., apoptosis (extrinsic and intrinsic), oxidative signaling, and PI3K/Akt signaling. The treatments affected the expression of apoptotic genes (TNFRSF10B [TRAIL], FAS, CASP3/6/7/8, PMAIP1 [NOXA], BNIP3L, BNIP3, BCL2A1, and BCL2), the oxidative stress-related genes (NOX4, XDH, MAOA, GSR, GPX3, and SOD3), and the PI3K/Akt pathway gene (ERBB2 [HER2]). Breast cancer treatments are complex with varying drug responses and efficacy among patients. This necessitates identifying novel biomarkers for predicting the drug response, using available data and new technologies. GSR, NOX4, CASP3, and ERBB2 are potential biomarkers for predicting the treatment response in primary ER+ ductal breast carcinoma.