Potential Cancer Research Articles

Determination of Acetylamantadine by γ-Cyclodextrin-Assisted α-HL Nanopore for Potential Cancer Prediagnosis.

The high expression of Spermidine/spermine N1-acetyltransferase (SSAT-1) is an important indicator in early cancer diagnosis. Here, we developed a nanopore-based methodology with γ-cyclodextrin as an adaptor to detect and quantify acetylamantadine, the specific SSAT-1-catalyzed product from amantadine, to accordingly reflect the activity of SSAT-1. We employ γ-cyclodextrin and report that amantadine cannot cause any secondary signals in γ-cyclodextrin-assisted α-HL nanopore, while its acetylation product, acetylamantadine, does. This allows γ-cyclodextrin to practically detect acetylamantadine in the interference of excessive amantadine, superior to the previously reported β-cyclodextrin. The quantification of acetylamantadine was not interfered with even a 50-fold amantadine and displayed no interference in artificial urine sample analysis, which indicates the good feasibility of this nanopore-based methodology in painless cancer prediagnosis. In addition, the discrimination mechanism is also explored by 2-D nuclear magnetic resonance (NMR) and nanopore experiments with a series of adamantane derivatives with different hydrophilic and hydrophobic groups. We found that both the hydrophobic region matching effect and hydrophilic interactions play a synergistic effect in forming a host-guest complex to further generate the characteristic signals, which may provide insights for the subsequent design and study of drug-cyclodextrin complexes.

Dual ionotropic crosslinked biopolymer magnetic nanocomposites as 5‐Fluorouracil nanocarrier for potential colorectal cancer treatment

AbstractThis study aimed to synthesize dual‐crosslinked chitosan‐alginate nanocomposites (Cs‐Alg/IO NCs) using green‐synthesized iron oxide nanoparticles (IO, Fe3O4 NPs) for drug delivery application. The NCs, formed via ionotropic gelation with sodium tripolyphosphate (TPP) and calcium chloride (CaCl2) as crosslinking agents for Cs and Alg, respectively, and further characterized for physiochemical properties. The presence of Fe3O4 NPs and 5‐Fluorouracil (5FU) increased hydrodynamic size (153.12 ± 0.57 to 207.64 ± 3.59 nm). Morphological studies revealed NC agglomeration within nanoscales. 5FU‐loaded Cs‐Alg/IO NCs (Cs‐Alg/IO 5FU NCs) exhibited superparamagnetic behavior and sustained drug release under various pH conditions. Cytotoxicity assays on colon cancer cells (HCT116 and HT‐29) and normal colon cells (CCD‐112) indicated higher cancer cell selectivity (selectivity index, SI = 1.91) in 2D models. Elevated IC50 values in 3D models were linked to lower drug loading capacity (LC) and encapsulation efficiency (EE). Overall, the study highlights the potential of dual‐crosslinked biopolymer NCs with green‐synthesized Fe3O4 NPs for developing an anticancer drug delivery system in colorectal cancer treatment.Highlights Synthesis of 5FU‐loaded dual crosslinked Cs‐Alg/IO NCs and Cs‐Alg IPN. Physicochemical characterization of cross‐linked magnetic nanocarriers. Investigation of sustained drug release using superparamagnetic nanocarriers. Evaluation of selective cytotoxicity for Cs‐Alg/IO 5FU NCs.

MyD88 and Its Inhibitors in Cancer: Prospects and Challenges

The interplay between the immune system and cancer underscores the central role of immunotherapy in cancer treatment. In this context, the innate immune system plays a critical role in preventing tumor invasion. Myeloid differentiation factor 88 (MyD88) is crucial for innate immunity, and activation of MyD88 promotes the production of inflammatory cytokines and induces infiltration, polarization, and immune escape of immune cells in the tumor microenvironment. Additionally, abnormal MyD88 signaling induces tumor cell proliferation and metastasis, which are closely associated with poor prognosis. Therefore, MyD88 could serve as a novel tumor biomarker and is a promising target for cancer therapy. Current strategies targeting MyD88 including inhibition of signaling pathways and protein multimerization, have made substantial progress, especially in inflammatory diseases and chronic inflammation-induced cancers. However, the specific role of MyD88 in regulating tumor immunity and tumorigenic mechanisms remains unclear. Therefore, this review describes the involvement of MyD88 in tumor immune escape and disease therapy. In addition, classical and non-classical MyD88 inhibitors were collated to provide insights into potential cancer treatment strategies. Despite several challenges and complexities, targeting MyD88 is a promising avenue for improving cancer treatment and has the potential to revolutionize patient outcomes.

Electrospun medicated gelatin/polycaprolactone Janus fibers for photothermal-chem combined therapy of liver cancer

Liver cancer is a common cancer in the world, and core-shell nanoparticles as a commonly used combination therapy for local tumor ablation, have many shortcomings. In this study, photothermal Janus nanofibers were prepared using a electrospinning technology for tumor treatment, and the products were characterized and in vitro photothermal performance investigated. The micromorphology analysis showed that the photothermic agent CuS and electrospun fibers (loaded with CuS and anticancer drug dihydromyricetin) were successfully prepared, with diameters of 11.58 ± 0.27 μm and 1.19 ± 0.01 μm, respectively. Water contact angle and tensile test indicated that the fiber membranes has a certain hydrophilic adhesion and excellent mechanical strength. The fiber membranes has 808 nm near-infrared laser photothermal heating performance and photothermal stability, and it also has a strong response to the laser that penetrates biological tissue. In addition, in vitro cell culture and in vivo implantation study showed that the fiber membranes could kill HepG2 hepatocellular carcinoma cells combined with photothermal-chem and could be enriched in the implantation area, respectively. Hence, the Janus membranes may be a potential cancer treatment material.

Abstract PO1-24-07: LYSOSOMAL-ASSOCIATED TRANSMENBRANE 4B (LAPTM4B) AS POTENTIAL BIOMARKER IN BREAST CANCER

Abstract LAPTM4B is a transmembrane that promotes autophagy and renders tumor cells resistant to metabolic and genotoxic stress. Increased expression of LAPTM4B has been found in breast, liver, lung, ovarian, uterine and gastric cancers. Overexpression of LAPTM4B contributes to chemotherapy resistance, proliferation and metastases. We analyze the expression of LAPTM4B in serum by ELISA method in a series of breast cancer patients in different clinical situations to assess its possible potential as a biomarker. A total of 135 patients were analyzed, 95 patients were in early breast cancer and 50 in metastatic disease. Of the 95 patients with early disease, 52 had a luminal A phenotype, 19 luminal B, 15 HER2 positive and 9 triple negative. And of the other 50 they were luminal in 34, HER2 positive in 11 and triple negative in 5. The attached table specifies the values of the LAPTM4B expression in the different clinical situations. GLOBAL LUMINAL A LUMNAL B HER2 TRIPLE NEGATIVE SITUATIONS Laptm4b n Laptm4b n Laptm4b n Laptm4b n Laptm4b n EARLY 9,6 ng/ml 95 7,7 ng/ml 52 12,8 ng/ml 19 13,4 ng/ml 15 7,1 ng/ml 9 METASTATIC 16,8 ng/ml 50 13,7 ng/ml 34 7,2 ng/ml 11 58,9 ng/ml 5 We found a statistic difference in the expression of LAPTM4B in patients with early and metastatic disease (ANOVA test p:0,028 ). LAPTM4B was higher in the luminal B and HER2 subtypes in early breast cancer while in metastatic disease was overexpressed in triple negative. An interesting finding was that in metastatic disease, LAPTM4B was overexpressed in patients with progression compared to patients in response. 30 ng/ml vs 10 ng/ml (ANOVA test p:0,011) LAPTM4B expression may be a potential biomarker in breast cancer since its expression is statistically different in early and metastatic disease (9,6 vs 16,8 ng/ml).In addition, patients with metastatic breast cancer who re in response also have lower levels of LAPTM4B compared with patients in progression. Monitoring the levels of LAPTM4B in serum can help us to predict the treatment response. Citation Format: Serafin Morales Murillo, Ariadna Gasol Cudós, Noemí Tuset Der-Abrain, Izaskun Urdanibia, Ana Velasco Sánchez. LYSOSOMAL-ASSOCIATED TRANSMENBRANE 4B (LAPTM4B) AS POTENTIAL BIOMARKER IN BREAST CANCER [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-24-07.

Abstract PO4-07-03: Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study

Abstract Background and Aim Some studies have reported the use of mammogram AI deep learning algorithms to accurately predict the risk of future breast cancer development in women. In these studies, random and unrelated mammograms were independently used for training of the AI model. We propose a novel deep learning system using sequential past mammograms instead of random studies and aimed to determine if it can further improve risk prediction in a pilot study. Methods Pre-training of the backbone of a Swin-transformer machine learning model was performed by applying region-of-interest lesional masks of benign and malignant on a training set of 1644 mammograms derived from the open-access Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset. To further develop the model, deep learning training and validation were performed on the Cohort of Screen-age Women (CSAW) open-access screening mammogram set which was derived from the long term data of 873 women who developed breast cancer for the first time and 7850 non-cancer women. We then compared the diagnostic accuracy of a model using sequential past mammograms for training against the standard, non-sequential model. For the sequential model, imaging segmentation of the potential cancer site on mammography was performed on all past, pre-cancer mammograms obtained within 60 to 2555 days of cancer diagnosis in the pre-processing stage for breast cancer cases. AI training was then carried out by combining the pre-cancer mammograms belonging to the same woman in a temporal sequence using a transformer encoder. The learning rate started from 1e-6 and decayed 10 times every 10 epochs. For the non-sequential model, we removed the temporal encoder and used random, unrelated mammogram studies for training. Validation was performed on a dataset consisting of 75 pre-cancer mammograms from breast cancer cases and 69 mammograms from non-cancer cases. Results Sensitivity, specificity and overall accuracy on the validation set were 77.3% (58/75), 68.1% (47/69) and (105/144) 72.9% respectively for the sequential model compared to 69.3% (52/75), 69.6% (48/69) and 69.4% (100/144) respectively for the non-sequential model. Conclusion The study showed that deep learning breast cancer risk prediction can be further improved by using sequential past mammograms instead of random mammograms for AI model training. This can also potentially enhance other AI risk prediction models that employ combined mammogram and traditional breast cancer clinical risk factors. A larger validation study will be useful. Contact information: email Lester Leong at lester.leong.c.h@singhealth.com.sg ROC curve for breast cancer risk prediction using AI deep learning training with sequential past mammograms. Citation Format: Lester Leong, Mingjie Xu, Weimin Huang, Erli Zhang, Engracia Loh, Sze Yiun Teo, Geok Hoon Lim, Veronique Kiak Mien Tan, Yirong Sim, Fredrik Strand, Ryan Tan. Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-03.

Identification of ROS and KEAP1-related genes and verified targets of α-hederin induce cell death for CRC.

In this study, we analyzed and verified differentially expressed genes (DEGs) in ROS and KEAP1 crosstalk in oncogenic signatures using GEO data sets (GSE4107andGSE41328). Multiple pathway enrichment analyses were finished based on DEGs. The genetic signature for colorectal adenocarcinoma (COAD) was identified by using the Cox regression analysis. Kaplan-Meiersurvival and receiver operating characteristiccurve analysis were used to explore the prognosis value of specific genes in COAD. The potential immune signatures and drug sensitivity prediction were also analyzed. Promising small-molecule agents were identified and predicted targets of α-hederin in SuperPred were validated by molecular docking. Also, expression levels of genes and Western blot analysis were conducted. In total, 48 genes were identified as DEGs, and the hub genes such as COL1A1, CXCL12, COL1A2, FN1, CAV1, TIMP3, andIGFBP7 were identified. The ROS and KEAP1-associated gene signatures comprised of hub key genes were developed for predicting the prognosis and evaluating the immune cell responses and immune infiltration in COAD. α-hederin, a potential anti-colorectal cancer(CRC) agent, was found to enhance the sensitivity of HCT116 cells, regulate CAV1 and COL1A1, and decrease KEAP1, Nrf2, and HO-1 expression significantly. KEAP1-related genes could be an essential mediator of ROS in CRC, and KEAP1-associated genes were effective in predicting prognosis and evaluating individualized CRC treatment. Therefore, α-hederin may be an effective chemosensitizer for CRC treatments in clinical settings.

Colon cancer inhibitory properties of Caulerpa lentillifera polysaccharide and its molecular mechanisms based on three-dimensional cell culture model

Caulerpa lentillifera is rich in polysaccharides, and its polysaccharides show a significant effect in different biological activities including anti-cancer activity. As an edible algae-derived polysaccharide, exploring the role of colon cancer can better develop the application from a dietary therapy perspective. However, more in-depth studies of C. lentillifera polysaccharide on anti-colon cancer activity and mechanism are needed. In this study, we found that Caulerpa lentillifera polysaccharides (CLP) showed potential anti-colon cancer effect on human colon cancer cell HT29 in monolayer (IC50 = 1.954 mg/mL) and spheroid (IC50 = 0.402 mg/mL). Transcriptomics and metabolomics analyses revealed that CLP had an inhibitory effect on HT29 3D spheroid cells by activating aminoacyl-tRNA biosynthesis as well as arginine and proline metabolism pathways. Furthermore, the anti-colon cancer effects of CLP were confirmed through other human colon cancer cell HCT116 and LoVo in monolayer cells (IC50 = 1.890 mg/mL and 1.437 mg/mL, respectively) and 3D spheroid cells (IC50 = 0.344 mg/mL and 0.975 mg/mL, respectively), and three patient-derived organoids with IC50 values of 6.333–8.780 mg/mL. This study provided basic data for the potential application of CLP in adjuvant therapeutic food for colon cancer on multiple levels, while further investigation of detailed mechanism in vivo was still required.

A Novel Tetramethylpyrazine Chalcone Hybrid- HCTMPPK, as a Potential Anti-Lung Cancer Agent by Downregulating MELK.

Lung adenocarcinoma currently ranks the leading causes of cancer-related mortality worldwide. Many anti-inflammation herbs, like tetramethylpyrazine, have shown their anti-tumor potentials. Here, we evaluated the role of a novel chalcone derivative of tetramethylpyrazine ((E) -1- (E) -1- (2-hydroxy-5-chlorophenyl) -3- (3,5,6-trimethylpyrazin-2-yl) -2-propen-1, HCTMPPK) in lung adenocarcinoma. The effects of HCTMPPK on cell proliferation, apoptosis, and invasion were investigated by in-vitro assays, including CCK-8, colony formation assay, flow cytometry, transwell assay, and wound-healing assay. The therapeutic potential of HCTMPPK in vivo was evaluated in xenograft mice. To figure out the target molecules of HCTMPPK, a network pharmacology approach and molecular docking studies were employed, and subsequent experiments were conducted to confirm these candidate molecules. HCTMPPK effectively suppressed the proliferative activity and migration, as well as enhanced the apoptosis of A549 cells in a concentration-dependent manner. Consistent with this, tumor growth was inhibited by HCTMPPK significantly in vivo. Regarding the mechanisms, HCTMPPK down-regulated Bcl-2 and MMP-9 and up-regulating Bax and cleaved-caspase-3. Subsequently, we identified 601 overlapping DEGs from LUAD patients in TCGA and GEO database. Then, 15 hub genes were identified by PPI network and CytoHubba. Finally, MELK was verified to be the HCTMPPK targeted site, through the molecular docking studies and validation experiments. Overall, our study indicates HCTMPPK as a potential MELK inhibitor and may be a promising candidate for the therapy of lung cancer.

Novel artesunate-metformin conjugate inhibits bladder cancer cell growth associated with Clusterin/SREBP1/FASN signaling pathway.

Bladder cancer remains the 10th most common cancer worldwide. In recent years, metformin has been found to have potential anti-bladder cancer activity while high concentration of IC50 at millimolar level is needed, which could not be reached by regular oral administration route. Thus, higher efficient agent is urgently demanded for clinically treating bladder cancer. Here, by conjugating artesunate to metformin, a novel artesunate-metformin dimer triazine derivative AM2 was designed and synthesized. The inhibitory effect of AM2 on bladder cancer cell line T24 and the mechanism underlying was determined. Anti-tumor activity of AM2 was assessed by MTT, cloning formation and wound healing assays. Decreasing effect of AM2 on lipogenesis was determined by oil red O staining. The protein expressions of Clusterin, SREBP1 and FASN in T24 cells were evaluated by Western blotting. The results show that AM2 significantly inhibited cell proliferation and migration at micromolar level, much higher than parental metformin. AM2 reduced lipogenesis and down-regulated the expressions of Clusterin, SREBP1 and FASN. These results suggest that AM2 inhibits the growth of bladder cancer cells T24 by inhibiting cellular lipogenesis associated with the Clusterin/SREBP1/FASN signaling pathway.

Toxicity Evaluation with Brine Shrimp Lethality Test and Phytochemical Analysis of Some Indonesian Plant Extracts As Potential Anti-Colon Cancer Agents

Toxicity Evaluation with Brine Shrimp Lethality Test and Phytochemical Analysis of Some Indonesian Plant Extracts As Potential Anti-Colon Cancer Agents

Cuproptosis: unveiling a new frontier in cancer biology and therapeutics.

Copper plays vital roles in numerous cellular processes and its imbalance can lead to oxidative stress and dysfunction. Recent research has unveiled a unique form of copper-induced cell death, termed cuproptosis, which differs from known cell death mechanisms. This process involves the interaction of copper with lipoylated tricarboxylic acid cycle enzymes, causing protein aggregation and cell death. Recently, a growing number of studies have explored the link between cuproptosis and cancer development. This review comprehensively examines the systemic and cellular metabolism of copper, including tumor-related signaling pathways influenced by copper. It delves into the discovery and mechanisms of cuproptosis and its connection to various cancers. Additionally, the review suggests potential cancer treatments using copper ionophores that induce cuproptosis, in combination with small molecule drugs, for precision therapy in specific cancer types.

Chemical and genotoxic characterization of bioaccessible fractions as a comprehensive in vitro tool in assessing the health risk due to dust-bound contaminant ingestion.

In the last two decades, awareness grew on the matter of the impact of environment on human health. Contaminants sorbed onto soil and settled dust can be ingested and thus represent a hazard, particularly to young children, who play on the ground and bring their hands and objects to their mouth. Metal(loid)s and polycyclic aromatic hydrocarbons (PAHs) are of concern as they are both carcinogenic to humans and ubiquitous in outdoor environments. The present study aims to assess the total and bioaccessible fractions of PAHs and metal(loid)s present in settled dust of four preschools located in industrial, urban, and suburban areas. On the one hand, children's incremental life cancer risks (ILCR) were calculated according to ingestion pathway. On the other hand, the genotoxicities of the bioaccessible dust-bonded contaminants were determined on gastric cells. PAH concentrations ranged from 50.9 to 2267.3 ng/g, and the bioaccessible fraction represented 10.7% of the total in average. Metal(loid) concentration ranged from 12,430 to 38,941 µg/g, and the mean bioaccessibility was of 40.1%. Cancer risk ranged from 2.8.105 to 8.6.105, indicating that there is a potential cancer risk for children linked to the ingestion of settled dust. The inorganic bioaccessible fraction induced little DNA (< 20%TailDNA) and chromosomal damages (30% increase in micronuclei), whereas the organic bioaccessible fraction induced higher DNA (17-63%TailDNA) and chromosomal damages (88% increase in micronuclei). Such experimental approach needs to be deepen, as a tool complementary to cancer risk calculation, since the latter only lays on a set of targeted contaminants with known toxicity values.

Design, synthesis, and evaluation of anti-breast cancer activity of colchicine - combretastatin A-4 analogues containing quinoline as microtubule-targeting agents

Research on compounds derived from, or inspired by, natural compounds as novel anticancer agents has been a significantly focus in recent years. In this study, we reported the design, synthesis, in vitro, and in silico evaluation of novel colchicine - combretastatin A-4 analogues fused with the quinoline moiety for their potential anti-breast cancer activities. A series of 21 desired analogues (2a-4g) was synthesized from quinoline-3-carbaldehydes (2–4) and anilines (a-g) via reductive amination. In vitro cytotoxicity evaluations of these analogues on breast cancer MDA-MB-231 cells revealed significant cell growth inhibition in 13 out of 21 compounds. Among them, compounds 2b (IC50 30.40 µM), 2e (IC50 18.98 µM), 4b (IC50 34.97 µM), and 4e (IC50 14.54 µM) demonstrated remarkable anti-breast cancer activities compared to colchicine (IC50 9.08 µM). Furthermore, these potential analogues were further characterized via several ligand-based computational models including SwissADME, ADMETlab2.0, and SEA Search Server, thereby suggesting their favorable physicochemical and pharmacokinetic-toxicological properties, as well as their abilities to target the β-tubulin chain. A structure-based computational approach such as molecular docking was also applied to investigate their binding characteristics with the colchicine-binding site inside α,β-tubulin, indicating that these compounds could exhibit a binding mode similar to that of colchicine or combretastatin A-4. These findings suggested the potential of these analogues as anti-breast cancer agents that could inhibit microtubule assembly. In conclusion, our study has provided a comprehensive understanding of the anticancer activities of the desired analogues, serving as the basis for future research on novel anti-breast cancer agents that target microtubules.

Assessment of Ambient Water Quality Deterioration in Proximity to Municipal Solid Waste Dumpsites in Urban Areas of Chattogram, Bangladesh

This study aims to assess the impact of waste dumping on groundwater quality within the Chattogram City Corporation area. Monitoring eight groundwater sampling points over four years, various physical and chemical parameters were analyzed, utilizing the APHA method. Parameters assessed include pH, temperature, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), salinity, biological oxygen demand (BOD), chemical oxygen demand (COD), Turbidity, Total Hardness, Ca-Hardness, Alkalinity, TSS, Chloride, Phosphate, Sulphate, Nitrite, Nitrate, Fluoride, Iron, Arsenic, Zinc, Copper, and Chromium. The findings were compared to the Department of Environment's (DoE) recommended values, as well as the Bangladesh standard and World Health Organization (WHO) values. During sample collection, deep tube wells near the dumping site points were prioritized. According to the investigation CNB, Ananda Bazar Halishahar and Arefin Nagar, deep pump water carries too many irons in their groundwater. Iron levels exceed both WHO and Bangladesh standards across all samples. Specifically, Arefin Nagar and Ananda Bazar Halishahar area sampling points S6, S7, and S8 surpass standards in TDS, Total Hardness, Turbidity, TSS, Chloride, and Iron. Water Quality Index (WQI) calculations suggest unsuitability for drinking purposes in all sampled water, with S5 and S8 demonstrating particularly high values, indicating their unsuitability for human consumption. Heavy Metal Pollution Index (HPI) calculations reveal a decrease at CNB sampling points S1 and S2, where waste dumping ceased in 2017. However, HPI values at other points show an increasing trend, indicating the leaching of heavy metals from solid waste into groundwater. S5 and S8 exhibit notably high HPI values (Average 464.99 and 319.59), suggesting an accumulation of heavy metals in the groundwater. Carcinogenic Risk Analysis of Arsenic highlights the failure of most sampled water to meet Carcinogenic Risk (CR) standards, signalling a potential cancer risk with prolonged use of this water.

Evaluating The Cytotoxic Activity of Lactobacillus plantarum IIA-1A5 Against MCF-7 Human Breast Cancer Cells and Identifying Its Surface Layer Protein Gene

Breast cancer is a serious global health concern, with a high mortality rate worldwide. Using natural compounds as potential cancer therapies is a promising approach to address this issue. Previous research has shown that probiotic lactic acid bacteria (LAB) and their metabolites, such as surface layer protein (slp), have a positive impact on a variety of health disorders, including cancer. The purpose of this study was to evaluate the ability of Lactobacillus plantarum IIA-1A5 to suppress the growth of the MCF-7 breast cancer cell line and detect the presence of the slp gene. Intracellular and extracellular protein fractions were isolated from L. plantarum IIA-1A5 cultures. The protein concentrations and molecular weights of the extracts were measured. The anticancer activity of the extracts was assessed using the MTT cytotoxicity test, and IC50 values were calculated. The slp gene was identified through polymerase chain reaction (PCR) amplification and nucleotide sequencing. The results demonstrated that L. plantarum IIA-1A5 had a concentration-dependent inhibitory effect on MCF-7 breast cancer cells, with IC50 values of 6.831 and 12.35 μg/mL for intracellular extracts and extracellular extracts, respectively. Additionally, PCR amplification and nucleotide sequencing confirmed the presence of the slp gene, which may contribute to the strain’s anticancer abilities. These findings suggest the potential of L. plantarum IIA-1A5 as a natural anticancer agent against MCF-7 breast cancer cells. Further research is warranted to elucidate the underlying mechanisms of L. plantarum IIA-1A5 in breast cancer treatment.

Colorectal Cancer Detection via Metabolites and Machine Learning

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient’s body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0–2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3–4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0–4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines.

Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments.

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.

High-throughput quantitative assessments of the chemical complementarity of celiac disease related IGH CDR3s and a gliadin epitope.

The long-term value of efficient antigen discovery includes gaining insights into the variety of potential cancer neoantigens, effective vaccines lacking adverse effects, and adaptive immune receptor (IR) targets for blocking adaptive IR-antigen interactions in autoimmunity. While the preceding goals have been partially addressed via big data approaches to HLA-epitope binding, there has been little such progress in the big data setting for adaptive IR-epitope binding. This delay in progress for the latter is likely due to, among other things, the much more complicated adaptive IR repertoire in an individual compared to individual HLA alleles. Thus, results described here represent the application of an algorithm for efficient assessment of IGH CDR3-gliadin epitope interactions, with a focus on epitopes known to be associated with an immune response in celiac disease. The hydrophobic, chemical complementarity between celiac case IGH CDR3s and known celiac epitopes was found to be greater in comparison to the hydrophobic, chemical complementarity between the same celiac case IGH CDR3s and a series of control epitopes. Thus, the approaches indicated here likely offer guidance for the development of conveniently applied algorithms for antigen verification and discovery.