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The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data., (© 2024. The Author(s).)

The effect of urea-loaded cellulose hydrogel, a controlled-release fertilizer (CRF) on growth and yield of upland rice were investigated in upland rice. As with the initial research, nitrogen (N) treatments were applied as CRF treatments; T2H (30 kg N ha -1 ), T3H (60 kg N ha -1 ), T4H (90 kg N ha -1 ), T5H (120 kg N ha -1 ) and recommended dose of fertilizer (RDF) at 120 kg N ha -1 RDF (T6U) in split application and T1 (0 N) as control. Results from this study indicated that applying CRF at the optimum N rate, T4H resulted in maximum grain yield, increasing by 71%. The analysis of yield components revealed that higher grain yield in T4H CRF was associated with an increase in panicle number and number of grains per panicle. Maximum grain N uptake of 0.25 g kg -1 was also observed in T4H CRF. In addition, T4H CRF recorded the highest harvest index (HI) and N harvest index (NHI) of 45.5% and 67.9%, respectively. Application of T4H CRF also recorded the highest N use efficiency (NUE) and N agronomic efficiency (NAE), 52.6% and 12.8 kg kg -1 , respectively. Observations show that CRF with only 75% N applied (T4H) in soil improved grain yield when compared to CRF with 100% N and 100% RDF in farmers' conventional split application. This suggested that CRF with a moderate N application might produce the highest potential yield and improved N efficiencies while enhancing crop production and further increase in N supply did not increase yield and N efficiencies. The results suggest that the application of T4H CRF for upland rice would enhance HI, N efficiencies and improve the yield of upland rice. Also, all growth parameters and yield were positively influenced by the application of CRF as a basal dose compared to split application of conventional urea fertilizers., (© 2023. The Author(s).)

Dysbiosis of the gut microbiome has been associated with the pathogenesis of colorectal cancer (CRC). We profiled the microbiome of gut mucosal tissues from 18 CRC patients and 18 non-CRC controls of the UKM Medical Centre (UKMMC), Kuala Lumpur, Malaysia. The results were then validated using a species-specific quantitative PCR in 40 CRC and 20 non-CRC tissues samples from the UMBI-UKMMC Biobank. Parvimonas micra, Fusobacterium nucleatum, Peptostreptococcus stomatis and Akkermansia muciniphila were found to be over-represented in our CRC patients compared to non-CRC controls. These four bacteria markers distinguished CRC from controls (AUROC = 0.925) in our validation cohort. We identified bacteria species significantly associated (cut-off value of > 5 fold abundance) with various CRC demographics such as ethnicity, gender and CRC staging; however, due to small sample size of the discovery cohort, these results could not be further verified in our validation cohort. In summary, Parvimonas micra, Fusobacterium nucleatum, Peptostreptococcus stomatis and Akkermansia muciniphila were enriched in our local CRC patients. Nevertheless, the roles of these bacteria in CRC initiation and progression remains to be investigated.

The burden of colorectal cancer (CRC) is increasing worldwide especially in developing countries. This phenomenon may be attributable to lifestyle, dietary and environmental risk factors. We aimed to determine the level of 25 trace elements, their interaction with environmental risk factors, and subsequently develop a risk prediction model for CRC (RPM CRC). For the discovery phase, we used a hospital-based case-control study (CRC and non-CRC patients) and in the validation phase we analysed pre-symptomatic samples of CRC patients from The Malaysian Cohort Biobank. Information on the environmental risk factors were obtained and level of 25 trace elements measured using the ICP-MS method. CRC patients had lower Zn and Se levels but higher Li, Be, Al, Co, Cu, As, Cd, Rb, Ba, Hg, Tl, and Pb levels compared to non-CRC patients. The positive interaction between red meat intake ≥ 50 g/day and Co ≥ 4.77 µg/L (AP 0.97; 95% CI 0.91, 1.03) doubled the risk of CRC. A panel of 24 trace elements can predict simultaneously and accurate of high, moderate, and low risk of CRC (accuracy 100%, AUC 1.00). This study provides a new input on possible roles for various trace elements in CRC as well as using a panel of trace elements as a screening approach to CRC.

Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the most frequent molecular alterations in ILC. We identified two main subtypes of ILCs: (i) an immune related subtype with mRNA up-regulation of PD-L1, PD-1 and CTLA-4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone related subtype, associated with Epithelial to Mesenchymal Transition (EMT), and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using the somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization may help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies.