Absolute Fold Change Research Articles

BIOM-76. EXPLORING CSF MICRORNA SIGNATURES AS DIAGNOSTIC BIOMARKERS IN IDH-WILDTYPE DIFFUSE GLIOMAS

Abstract The most prevalent malignant tumors of the central nervous system are adult-type diffuse gliomas. Isocitrate dehydrogenase (IDH) mutations are identified in a subset of diffuse gliomas, leading to two distinct subtypes of gliomas: IDH-wildtype and IDH-mutant. In comparison to IDH-mutant gliomas, IDH-wildtype gliomas tend to be associated with a more rapid progression and lower overall survival rates. The analysis of differentially expressed microRNAs (DE-miRNAs) in cerebrospinal fluid (CSF) of IDH-wildtype diffuse gliomas could provide an effective and non-invasive approach to assist in the early diagnosis, classification, and management of the disease. Extracellular vesicle (EV)-derived miRNAs obtained from the CSF of 11 patients with IDH-wildtype diffuse gliomas and 8 controls were analyzed by microRNA sequencing (miRNA-seq). DE-miRNAs were defined using p-value≤0.05 and absolute log2 fold change (log2FC) ≥1. Candidate DE-miRNAs were validated by qRT-PCR in 24 samples (15 IDH-wildtype vs. 9 control) using the 2-ΔΔCT method, followed by receiver operating characteristic (ROC) curve analysis. Hsa-miR-24-3p was used as the internal control. miRNA-seq analysis identified hsa-miR-484, hsa-miR-223-3p, hsa-miR-1246, hsa-miR-323a-3p, hsa-miR-106b-3p, hsa-miR-21-5p, hsa-miR-15a-5p, has-miR-15b-5p, and hsa-miR-92a-3p among the top 20 upregulated miRNAs in the CSF of patients with IDH-wildtype diffuse gliomas. Further analysis by qPCR showed that patients with IDH-wildtype diffuse gliomas had significantly higher levels of has-miR-21-5p (2.28 ± 2.33) compared to control subjects (-2.22e-007 ± 1.08, p=0.012). The ROC curve analysis revealed an area under the curve (AUC) of 0.84 (95% CI, 0.68-1.0) for has-miR-21-5p CSF levels in distinguishing IDH-wildtype diffuse gliomas from control subjects. EV-derived miRNAs in CSF, including has-miR-21-5p, have the potential to distinguish IDH-wildtype diffuse gliomas from control individuals in a minimally-invasive manner and serve as diagnostic biomarkers.

In silico and functional analysis identifies key gene networks and novel gene candidates in obesity-linked human visceral fat.

Visceral adiposity is associated with increased proinflammatory activity, insulin resistance, diabetes risk, and mortality rate. Numerous individual genes have been associated with obesity, but studies investigating gene regulatory networks in human visceral obesity have been lacking. We analyzed gene regulatory networks in human visceral adipose tissue (VAT) from 48 and 11 Chinese patients with and without obesity, respectively, using gene coexpression and gene regulatory network construction from RNA-sequencing data. We also conducted RNA interference-based functional tests on selected genes for effects on adipocyte differentiation. A scale-free gene coexpression network was constructed from 360 differentially expressed genes between VAT samples from patients with and without obesity (absolute log fold change > 1, false discovery rate [FDR] < 0.05), with edge probability > 0.8. Gene regulatory network analysis identified candidate transcription factors associated with differentially expressed genes. A total of 15 subnetworks (communities) displayed altered connectivity patterns between obesity and nonobesity networks. Genes in proinflammatory pathways showed increased network connectivity in VAT samples with obesity, whereas the oxidative phosphorylation pathway displayed reduced connectivity (enrichment FDR < 0.05). Functional screening via RNA interference identified genes such as SOX30, SIRPB1, and OSBPL3 as potential network-derived candidates influencing adipocyte differentiation. This approach highlights the network architecture in human obesity, identifies novel candidate genes, and generates new hypotheses regarding network-assisted gene regulation in VAT.

Proteomic Analysis of Follicular Fluid in Polycystic Ovary Syndrome: Insights into Protein Composition and Metabolic Pathway Alterations.

This study explores the proteomic composition of follicular fluid (FF) from women undergoing oocyte retrieval for in vitro fertilisation (IVF), with a focus on the effects of polycystic ovary syndrome (PCOS). FF samples were collected from 74 patients, including 34 with PCOS and 40 oocyte donors. Proteomic profiling using machine learning identified significant differences in protein abundance between the PCOS and control groups. Of the 484 quantified proteins, 20 showed significantly altered levels in the PCOS group. Functional annotation and pathway enrichment analysis pointed to the involvement of protease inhibitors and immune-related proteins in the pathophysiology of PCOS, suggesting that inflammation and immune dysregulation may play a key role. Additionally, HDL assembly was identified as a significant pathway, with apolipoprotein-AI (APOA1) and alpha-2-macroglobulin (A2M) as the major proteins involved. Notably, myosin light polypeptide 6 was the most downregulated protein, showing the highest absolute fold change, and may serve as a novel independent biomarker for PCOS.

A preliminary study of gene expression changes in Koalas Infected with Koala Retrovirus (KoRV) and identification of potential biomarkers for KoRV pathogenesis

BackgroundKoala retrovirus (KoRV), a major pathogen of koalas, exists in both endogenous (KoRV-A) and exogenous forms (KoRV-A to I and K to M) and causes multiple disease phenotypes, including carcinomas and immunosuppression. However, the direct association between the different KoRV subtypes and carcinogenesis remains unknown. Differentially expressed gene (DEG) analysis of peripheral blood mononuclear cells (PBMCs) of koalas carrying both endogenous (KoRV-A) and exogenous (KoRV-A, B, and C) subtypes was performed using a high-throughput RNA-seq approach. PBMCs were obtained from three healthy koalas: one infected with endogenous (KoRV-A; Group I) and two infected with exogenous (KoRV-B and/or KoRV-C; Group II) subtypes. Additionally, spleen samples (n = 6) from six KoRV-infected deceased koalas (K1- K6) and blood samples (n = 1) from a live koala (K7) were collected and examined to validate the findings.ResultsAll koalas were positive for the endogenous KoRV-A subtype, and eight koalas were positive for KoRV-B and/or KoRV-C. Transcription of KoRV gag, pol, and env genes was detected in all koalas. Upregulation of cytokine and immunosuppressive genes was observed in koalas infected with KoRV-B or KoRV-B and -C subtypes, compared to koalas infected with only KoRV-A. We found 550 DEG signatures with significant (absolute p < 0.05, and absolute log2 Fold Change (FC) > 1.5) dysregulation, out of which 77.6% and 22.4% DEGs were upregulated (log2FC > 1.5) and downregulated (log2FC < − 1.5), and downregulated (log2 FC < − 1), respectively. We identified 17 unique hub genes (82.3% upregulated and 17.7% down-regulated), with KIF23, CCNB2, POLR3F, and RSL24D1 detected as the potential hub genes modified with KoRV infection. Real-time RT-qPCR was performed on seven koalas to ascertain the expression levels of four potential hub genes, which were subsequently normalized to actin copies. Notably, all seven koalas exhibited distinct expression signatures for the hub genes, especially, KIF23 and CCNB2 show the highest expression in healthy koala PBMC, and POLR3F shows the highest expression in koala with lymphoma (K1).ConclusionThus, it can be concluded that multiple KoRV subtypes affect disease progression in koalas and that the predicted hub genes could be promising prognostic biomarkers for pathogenesis.

6828 Exploring Metabolomic Clues in Diabetic Retinopathy

Abstract Disclosure: M.W. Simonson: None. Y. Li: None. J.J. McAnany: None. B. Prasad: None. E.C. Hanlon: None. S. Pannain: None. B.T. Layden: None. E. Van Cauter: None. J.C. Park: None. S.J. Crowley: None. F.Y. Chau: None. K.K. Danielson: None. H. Chen: None. G.E. Chlipala: None. C. Martinez: None. S. Reutrakul: Speaker; Self; Eli Lilly &amp; Company. Background: Diabetic retinopathy (DR) is a common microvascular complication of diabetes. Metabolomics offers the possibility for novel biomarker discovery. The purpose of this study was to determine metabolomic differences that characterized patients with versus without DR. Methods: A total of 62 serum samples were collected from 36 type 2 diabetes (T2D) patients with DR and 26 T2D patients without DR, and analyzed via UPLC-MS. The metabolite data were normalized using median normalization. The differential expression of metabolites was compared among the groups using the limma package in R. The covariates (age, sex, BMI and sleep apnea severity) were accounted for by using a generalized linear model fitted to the data. Significantly different metabolites were filtered by an absolute log2 Fold Change (FC) of 1.5 or greater and Q values &amp;lt; 0.05. Results: Mean (SD) age for DR and no-DR group was 55 (±5.9) versus 54 (±6.7) years, while BMI was 33 (±5.8) versus 33 (±6.3) kg/m2 respectively. Both DR and no-DR groups were 58% female. A1C levels in DR group was marginally higher than in no-DR group (p=0.09). There were significant differences in metabolomic changes between DR versus no-DR, with 166 total differential metabolites after adjusting for covariates and filtering by FC. Notable metabolites included those involved in fatty acid metabolism, acyl carnitines, prostaglandins, and phospholipids. There was an overall upregulation of serum acyl carnitines in DR as compared to no-DR, including 2,6 dimethylheptanoyl carnitine (FC = 3.0) and cis-5-Tetradecenoylcarnitine (FC = 2.0). Additionally, there was a decrease in long chain fatty acids in DR as compared to no-DR, which included 6-hydroxypentadecanedioic acid (FC = -6.6), 10,16-dihydroxy-palmitic acid (FC = -6.0), and tetracosatetraenoyl CoA (FC = -6.1). The 11-beta prostaglandin F2 (11-PGF2) was increased in DR (FC = 1.9). Circulating phospholipids including lysophosphatidylcholine (22:2) (lysoPC) (FC = 1.8) and phosphatidylcholine (24:1/24:1) (PC) (FC = 5.9) were also significantly dysregulated. Conclusions: We identified a subset of metabolites that discriminate T2D patients with or without DR. The changes in acyl carnitines and long chain fatty acids may reflect mitochondrial dysfunction in DR. The presence of certain prostaglandins highlights the inflammatory environment which may facilitate DR progression. Increases in phospholipids such as lysoPC suggest an atherogenic state and higher LDL oxidation, while increases in PC may indicate altered lipogenesis in DR. These findings may serve as new potential serum biomarkers to promote efficient preventive care and provide mechanistic insight into understanding DR pathophysiology. Presentation: 6/2/2024

Global Transcriptomic Analysis of Topical Sodium Alginate Protection against Peptic Damage in an In Vitro Model of Treatment-Resistant Gastroesophageal Reflux Disease.

Breakthrough symptoms are thought to occur in roughly half of all gastroesophageal reflux disease (GERD) patients despite maximal acid suppression (proton pump inhibitor, PPI) therapy. Topical alginates have recently been shown to enhance mucosal defense against acid-pepsin insult during GERD. We aimed to examine potential alginate protection of transcriptomic changes in a cell culture model of PPI-recalcitrant GERD. Immortalized normal-derived human esophageal epithelial cells underwent pretreatment with commercial alginate-based anti-reflux medications (Gaviscon Advance or Gaviscon Double Action), a matched-viscosity placebo control, or pH 7.4 buffer (sham) alone for 1 min, followed by exposure to pH 6.0 + pepsin or buffer alone for 3 min. RNA sequencing was conducted, and Ingenuity Pathway Analysis was performed with a false discovery rate of ≤0.01 and absolute fold-change of ≥1.3. Pepsin-acid exposure disrupted gene expressions associated with epithelial barrier function, chromatin structure, carcinogenesis, and inflammation. Alginate formulations demonstrated protection by mitigating these changes and promoting extracellular matrix repair, downregulating proto-oncogenes, and enhancing tumor suppressor expression. These data suggest molecular mechanisms by which alginates provide topical protection against injury during weakly acidic reflux and support a potential role for alginates in the prevention of GERD-related carcinogenesis.

Abstract A019: Black race and CA 19-9 nonproduction is associated with limited pathologic response to neoadjuvant chemotherapy in patients with localized pancreatic cancer

Abstract Introduction: Black patients with pancreatic ductal adenocarcinoma (PDAC) are less likely to have major pathologic response (MPR) following induction chemotherapy. Our previous study suggested a lower baseline CA 19-9 among Black patients. We aimed to determine if CA 19-9 nonproduction contributes to racial differences in neoadjuvant chemotherapy (NAC) response. We then investigated the biological mechanisms underlying reduced response in CAnonprod with multi-omic analysis. Methods: Black and White patients with PDAC receiving ≥ 2 cycles of NAC followed by pancreatectomy at 7 high-volume centers were reviewed. Patients were categorized as CAproducers (CAprod, CA 19-9 &amp;gt; 5 U/mL) or CAnonprod (CA 19-9 ≤ 5 U/mL). Uni- and multi-variable models evaluated differences in rates of CAnonprod by race, and the association of CAnonprod with MPR (CAP 0/1). The Pancreatic Cancer Action Network SPARK platform was queried for patients with CA 19-9 and genomic data. Fisher’s exact test was used to compare differentially mutated genes between CAnonprod and CAprod. Differential gene expression (DEG) analysis identified significantly differentially expressed genes using an absolute fold change of ≥ 2 and p-adj &amp;lt; 0.05 as a cutoff. quanTIseq computational pipeline was used for immune cell deconvolution analysis of the two groups. Results: Our cohort included 385 CAprod and 30 CAnonprod. CAnonprod had a significantly higher rate of Black patients (50% vs 13%, p&amp;lt;0.01). Compared with CAprod, CAnonprod received similar rates of FOLFIRINOX (60% vs. 57%, p=0.27) and duration of NAC (4 vs 5 cycles, p=0.83). Rate of MPR was higher among CAprod compared to CAnonprod (26% vs 8%, p=0.03). No CAnonprod patient had a complete pathologic response vs 28 (7%) of CAprod (p=0.12). CAnonprod was independently associated with decreased odds of MPR (OR 0.21, CI [0.04-0.99]). Black race was independently associated with increased odds of CAnonprod (OR 8.66, CI [3.81 – 19.7]). When CA 19-9 production status was matched by race, there was no difference between rate of MPR between White and Black CAnonprod; both experienced low rates of MPR (12% and 9%, respectively, (p=0.68)). CAnonprod had higher rates of SWI/SNF alterations (50% CAnonprod vs 33% CAprod, p&amp;lt; 0.01; P-adj=ns), and ARID1B was the most frequently mutated gene in CAnonprod (28% vs 11%, p&amp;lt; 0.01; P-adj=ns). DEG analysis showed the LIPF gene was significantly downregulated in CAnonprod compared to CAprod. There was no difference in immune cell distribution between CAprod and CAnonprod. Conclusion: CA 19-9 non-production is more prevalent in Black patients and potentially mediates the lower rates of MPR following NAC. CA 19-9 non-production is associated with higher rates of SWI/SNF alterations and a downregulation of the LIPF gene encoding gastric lipase, unveiling a potential biologic or metabolomic difference in response to chemotherapy between races. Citation Format: Mary P. Martos, Erin M. Dickey, Kawther Abdilleh, Syed A. Ahmad, Shishir K. Maithel, Chet W. Hammill, Hong Jin Kim, Daniel E. Abbott, David A. Kooby, Alexander A. Parikh, Peter J. Hosein, Nipun B. Merchant, Jashodeep Datta, Caitlin A. Hester. Black race and CA 19-9 nonproduction is associated with limited pathologic response to neoadjuvant chemotherapy in patients with localized pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A019.

197 Hepatic mRNA expression of innate and adaptive immune genes in beef steers with divergent residual body weight gain

Abstract Immune function has a pivotal role in dictating the overall health and productivity of cattle. In a proficient immune system, the liver assumes an integral function in detoxification and metabolic processes and contributes substantially to overall production and immunity. In this study, we evaluated the hepatic mRNA expression of genes involved in innate and adaptive immunity in crossbred beef steers with positive or negative residual body weight (BW) gain (RADG). Positive-RADG beef steers (n = 8; RADG = 0.76 kg/d) and negative-RADG beef steers (n = 8; RADG = -0.64 kg/d) were identified from a group of 108 growing crossbred beef steers (average BW = 286 ± 380kg) after a 56-d performance testing period. At the end of the 56-d period, liver tissue samples were collected from the beef steers for RNA extraction and cDNA synthesis. The mRNA expression of 84 genes involved in innate and adaptive immunity were analyzed using pathway-focused PCR-based arrays. The mRNA expression of genes with false discovery rate-adjusted P-values (FDR) ≤ 0.05 and absolute fold change (FC) ≥ 1.2 were determined to be differentially expressed. Out of the 84 genes analyzed, four genes [interleukin (IL)-2, MYD88, CD-80, NFkB-1] were differentially expressed and were all upregulated in positive compared with negative-RADG beef steers. Gene ontology pathway enrichment analysis of the differentially expressed genes revealed that pathways related to positive regulation of IL-17 production, cellular response to lipopolysaccharide, and positive regulation of lymphocyte activation were enriched in the positive-RADG steers (P ≤ 0.05). In conclusion, our study revealed that positive-RADG beef steers had increased expression of genes and pathways involved in pathogen recognition and initiation of responses to effectively fight off infections while mitigating excess inflammatory reactions compared with negative-RADG steers, which might explain, in part, the improved feed efficiency of these beef animals

403 Impact of lysine, methionine, and histidine deficiencies on gene expression and upstream transcriptional regulators in bovine mammary epithelial cells: An RNA-sequencing analysis

Abstract Essential (E) amino acids (AA) may alter bovine mammary epithelial cell functions by impacting mRNA expression of genes through their upstream transcription factors. The objective of this research was to study the impact of EAA deficiencies on gene expression and to identify upstream transcriptional factors mediating their effects in bovine mammary epithelial cells. Epithelial cells were collected from the mammary glands of lactating dairy cows and cultured in combined media of DMEM/F12 and Medium 170, differentiated for 5 d, and subsequently treated in triplicate for 60 h with a control medium containing all EAA at normal plasma concentrations for cows (CTL), or a medium with 20 μM Lys (LY), 5 μM Met (LM), or 10 μM His (LH), which are each 25% of normal concentration. RNA was isolated from the cells and sequenced on an Illumina NovaSeq 6000 producing 150 base pair paired-end reads with a minimum sequencing depth of 150 million reads. Reads were cleaned with ‘fastp’ (Chen et al., 2018), aligned to the bovine reference genome (ARS-UCD1.2) using ‘STAR’ aligner (Dobin et al., 2013), and genes counted using ‘featureCounts’ (Liao et al., 2014). Differential expression of genes between treatment pairs was determined using the ‘DESeq2’ package (Love et al., 2014) in R. Genes with an absolute log2 fold change ≥ 1 and a false discovery rate of &amp;lt; 0.05 were considered differentially expressed. The number of differentially expressed genes relative to CTL was 780 for LY, 383 for LH, and 536 for LM. QIAGEN Ingenuity Pathway Analysis (IPA) identified four pathways consistently affected across LY, LM, and LH: kinetochore metaphase signaling, mitotic roles of polo-like kinase, cell cycle: G2/M DNA damage checkpoint regulation, and interferon signaling (Figure 1-3). Notably, the kinetochore metaphase signaling pathway was identified as the most significant across all treatments, suggesting a strong link to cell proliferation and apoptosis during EAA deficiency. The interferon signaling might be related to the Janus kinase 2/signal transducer and activator of transcription 5 (Jak2/STAT5) pathway which can promote the expression of genes encoding milk proteins such as β-casein and β-lactoglobulin. Furthermore, the IPA identified upstream transcription factors related to cell proliferation, apoptosis, autophagy, protein translation, endoplasmic reticulum biogenesis, and amino acid metabolism including NUPR1, FOXM1, MYC, CCND1, TP53, DDIT3, and ATF4 across all treatments. The findings suggest that AA deprivation regulates bovine mammary epithelial cell function by instigating amino acid response pathways and altering secretory cell number.

MRNA expression insights: Unraveling the relationship between COPD and lung cancer.

Lung cancer is a prevalent form of cancer worldwide. A possible link between lung cancer and chronic obstructive pulmonary disease (COPD) has been suggested by recent studies. The objective of our research was to analyze the mRNA expression patterns in both situations, with a specific emphasis on their biological functions and the pathways they are linked to. Data on COPD mRNA expression was collected from the NCBI-GEO database, while information regarding lung cancer mRNA was acquired from The Cancer Genome Atlas database. To examine the association of COPD-related scores in lung cancer patients, we utilized the ssGSEA algorithm for single sample gene set enrichment analysis. The possible routes were examined through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Risk models were developed using Coxand least absolute shrinkage and selection operator (LASSO) regression analyses. Moreover, a GSEA was performed to investigate significant pathways among various risk groups. After identifying 17 genes that were differentially expressed and linked to COPD, we found that they met the criteria of having a false discovery rate < 0.05 and an absolute log2 fold change > 0.585. By utilizing the ssGSEA algorithm, it became possible to classify individuals with lung cancer into two distinct groups based on their COPD status. Consequently, a seven-gene risk model was developed specifically for these patients. The risk score was determined by applying the given formula: risk score = AC022784.1 × 0.0423737993775888 + CRISP3× 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × -0.309489953363417 + ITGA6 × 0.01813978449589. The risk model associated with COPD showed a notable connection with different immune cells found in the lung cancer sample, including macrophages of M0/M1/M2 types, hematopoietic stem cells, mast cells, NK T cells and regulatory T cells. Overexpression of crucial genes was seen to enhance cell proliferation and invasive potential in the lung cancer sample. In the lung cancer sample, it was observed that an increase in ZNF506 expression enhanced both cell proliferation and invasion. In conclusion, this study effectively examines the potential correlation between COPD and lung cancer. A prognostic model based on seven COPD-associated genes demonstrated robust predictive potential in the lung cancer sample. Our analysis offers comprehensive insights for lung cancer patients.

Comprehensive serum proteomics profiles and potential protein biomarkers for the early detection of advanced adenoma and colorectal cancer.

The majority of colorectal cancer (CRC) cases develop from precursor advanced adenoma (AA). With the development of proteomics technologies, blood protein biomarkers have potential applications in the early screening of AA and CRC in the general population. To identify serum protein biomarkers for the early screening of AA and CRC. We collected 43 serum samples from 8 normal controls (NCs), 19 AA patients and 16 CRC patients at China-Japan Friendship Hospital. Quantitative proteomic analysis was performed using liquid chromatography-mass spectrometry/mass spectrometry and data independent acquisition, and differentially expressed proteins (DEPs) with P-values < 0.05 and absolute fold changes > 1.5 were screened out, followed by bioinformatics analysis. Prognosis was further analyzed based on public databases, and proteins expression in tissues were validated by immunohistochemistry. A total of 2132 proteins and 17365 peptides were identified in the serum samples. There were 459 upregulated proteins and 118 downregulated proteins in the NC vs AA group, 289 and 180 in the NC vs CRC group, and 52 and 248 in the AA vs CRC group, respectively. Bioinformatic analysis revealed that these DEPs had different functions and participated in extensive signaling pathways. We also identified DIAPH1, VASP, RAB11B, LBP, SAR1A, TUBGCP5, and DOK3 as important proteins for the progression of AA and CRC. Furthermore, VASP (P < 0.01), LBP (P = 0.01), TUBGCP5 (P < 0.01), and DOK3 (P < 0.01) were associated with a poor prognosis. In addition, we propose that LBP and VASP may be more promising protein biomarkers for the early screening of colorectal tumors. Our study elucidated the serum proteomic profiles of AA and CRC patients, and the identified proteins, such as LBP and VASP, may contribute to the early detection of AA and CRC.

Use of integrated multi-omics analysis to determine antitumor efficacy of donafenib in combination with FADS2 inhibition in hepatocellular carcinoma.

e15083 Background: Pharmacotherapy remains a pivotal treatment strategy for patients with advanced hepatocellular carcinoma (HCC). Donafenib, globally the first targeted therapy proven to offer superior survival benefits and safety over sorafenib in a large-scale phase III clinical trial with head-to-head comparison, has been adopted for first-line treatment of advanced HCC. Given the limited basic research on Donafenib both domestically and internationally, and its constrained clinical efficacy, this study aims to explore combination drug therapy for HCC in an attempt to achieve further breakthroughs and enhance the anticancer activity of Donafenib. Methods: A xenograft tumor model in nude mice was established to observe the in vivo antitumor effects of Donafenib. Transcriptomics and proteomics were employed to characterize the gene expression changes in HCC cell lines after Donafenib intervention and to screen for key molecules. A series of assays, including the Cell Counting Kit-8 (CCK8), flow cytometry, scratch assays, Transwell assays, Western blotting, and in vivo tumorigenicity in nude mice, were conducted to determine the impact of the hub gene on the malignant behavior of HCC and to evaluate the combined antitumor effects of the molecular inhibition and Donafenib treatment in HCC. Results: Treatment with Donafenib significantly slowed tumor growth in a nude mouse xenograft model, resulting in a marked reduction in final tumor volumes compared to control subjects. Proteomic analyses post-Donafenib treatment identified FADS2 as the protein with the highest absolute log2 fold change among all differentially expressed proteins in HCC cells. This finding was corroborated by transcriptomic data, which indicated a concurrent downregulation of FADS2 at the mRNA level. Subsequent in vitro and in vivo assays validated the inhibitory effect of FADS2 blockade on the malignant biological behaviors of HCC cells. Importantly, the concomitant administration of Donafenib and the FADS2 inhibitor SC-26196 exhibited a synergistic antitumor action, significantly enhancing therapeutic efficacy in both HCC cell lines and xenografted tumors in nude mice. Conclusions: Combination therapy, a prevalent approach in clinical oncology, has the potential to augment treatment efficacy while simultaneously diminishing toxicity. This study employed an extensive multi-omics approach complemented by a range of both in vitro and in vivo experiments. It revealed that the synergistic use of Donafenib and SC-26196 markedly enhances Donafenib-induced the inhibition of cancer cell growth and metastasis, demonstrating a significant synergistic antitumor effect. These findings from our preclinical investigations may offer a promising new combinational drug regimen for the therapeutic management of HCC patients.

Highly prognostic signature for stage II colon cancer from the TOSCA trial: The PROSIT study.

3524 Background: The predictive and prognostic value of current biomarkers for guiding the treatment of stage II colon cancer (CC) is very limited. Here, we aimed to develop a prognostic transcriptional signature for high-risk stage II CC patients undergoing adjuvant treatment with either 3 or 6 months of FOLFOX4/CAPOX within the framework of the phase III TOSCA Italian trial. Methods: We conducted an observational retrospective study based on the TOSCA trial database, matching 62 patients with high-risk stage II CC who relapsed with 62 who did not. The matching criteria included the center of recruitment, pT stage, adjuvant treatment type and duration (3 or 6 months). Transcriptome sequencing was performed on material from centrally assessed FFPE blocks on an Illumina Novaseq 6000 instrument. Generated data were processed using the nf-core/rnaseq (v 3.14.0) pipeline, aligned to the human genome GRCh37/hg19, and analyzed for differential gene expression, requiring a minimum absolute log fold change of 1 and false discovery rate below 0.1. Enrichment analysis was performed using GSVA package with the z-score method. Results: Out of the 124 stage II CC cases considered for the present analysis, 60% received 3 months and 40% 6 months of adjuvant chemotherapy. The median age was 64 years (IQR 55.2-71.1), with 40% of the cancers being right-sided. Fifty-seven percent of the patients received FOLFOX, while the remaining were treated with XELOX. Of the 124 matched samples, 72 were of sufficient quality for RNA-seq.We identified a signature among the top 17 differentially expressed genes (absolute log fold-change &gt; 1, p-value &lt; 0.001, FDR &lt; 0.1, see Table). Patients exhibiting a lower prognostic signature (PROSIT-L) showed worse disease-free survival (DFS) compared to those with a high prognostic signature (PROSIT-H) (HR 3.5; 95% CI 1.53 – 7.9; p = 0.002), indicating its strong and independent prognostic value, regardless of the treatment received. Conclusions: The new transcriptomic signature may help in identifying stage II CC patients who are likely to relapse despite receiving adjuvant oxaliplatin-based treatment. [Table: see text]

Using machine learning approach based on gene expression profiling to predict the risk of bone metastasis in lung cancer.

e20522 Background: Bone metastasis (BM) occurs in about 30% of patients with advanced lung cancer, and the prognosis of BM patients is very poor, with a median survival of 6-10 months. Therefore, we attempted to use machine learning methods based on gene expression profiles to construct a model to predict BM. Methods: Gene Expression Omnibus (GEO) database were searched, then these datasets (GSE10096, GSE29367, GSE29391, GSE76194, GSE175601) were included for analysis. Batch effects were removed using the ComBat function from the R package sva (v3.44.0). edgeR was used for differential expression analysis, and genes that had an absolute log2 fold change &gt; 1 and adjusted p-value &lt; 0.05, were selected as differentially expressed genes (DEGs) for modeling. Random forest (RF) modeling was performed using the R package random Forest (v4.7-1.1), and support vector machine (SVM) modeling and SVM Recursive Feature Elimination (SVM-RFE) were performed using the R package e1071 (v1.7-13). Survival curves were generated using the GEPIA2 R packages. Models performance was evaluated using the area under the receiver operating characteristics curve (AUC). Results: A total of 48 qualified cases were subjected to analysis, including 22 BM and 26 non-BM patients. Differential expression analysis revealed that a total of 327 DEGs were identified in the BM group. We divided the sample dataset into a training set and a test set. In the first approach, we attempted to use all 327 DEGs features to train the models. Among the evaluated models, the RF algorithm performed best with an AUC of 0.753, while the SVM algorithm had an AUC of 0.938. The second approach, we applied the SVM-RFE algorithm for feature selection, which ranked the features according to their importance, and the top 10 genes/features (TMC5, S100A4, EEF1A2, CLDN1, RALY, MYH10, MAGEC2, ALDH1A1, PI3, SALL1) were used to build a classification model (in the test set, sensitivity was100%, specificity: 81.8%, AUC:0.909). In addition, TCGA survival analysis of 10 genes was performed and found that the expression levels of five genes, including ALDH1A1, MYH10, RALY, CLDN1 and MAGEC2, were significantly associated with prognosis (p &lt; 0.1). Conclusions: Our study suggests that we have identified ten key genes associated with BM of lung cancer which could be potential therapeutic targets.

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient’s breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2 fold change ⩾1 and q-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z −242.18428 and m/z −117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.

Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid

The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, collagenase 3 and interleukin-6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and mild degeneration groups. To those, we applied Gaussian Graphical Models (GGMs) to analyze the conditional dependence of proteins and to identify key proteins and subnetworks involved in early OA pathogenesis. After regularization and stability selection, we identified 102 proteins involved in GGM networks. Notably, network complexity was lost in the protein graph for mild degeneration when compared to controls, suggesting a disruption in the regular protein interplay. Furthermore, among our main findings were several downregulated (in mild degeneration versus healthy) proteins with unique interactions in the healthy group, one of which, SLCO5A1, has not previously been associated with OA. Our results suggest that this protein is important for healthy joint function. Further, our data suggests that SF proteomics, combined with GGMs, can reveal novel insights into the molecular pathogenesis and identification of biomarker candidates for early-stage OA.

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches.

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Prediagnostic plasma proteomics profile for hepatocellular carcinoma.

Proteomics may discover pathophysiological changes related to hepatocellular carcinoma, an aggressive and lethal type of cancer with low sensitivity for early stage diagnosis. We measured 1305 prediagnostic (median = 12.7 years) SomaScan proteins from 54 pairs of healthy individuals who subsequently developed hepatocellular carcinoma and matched non-hepatocellular carcinoma control individuals from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). Candidate proteins were validated in the independent, prospective UK Biobank Pharma Proteomics Project (UKB-PPP). In NHS and HPFS, we identified 56 elevated proteins in hepatocellular carcinoma with an absolute fold change of more than 1.2 and a Wald test P value less than .05 in conditional logistic regression analysis. Ingenuity pathway analysis identified enrichment of pathways associated with cell viability, adhesion, proteolysis, apoptosis, and inflammatory response. Four proteins-chitinase-3-like protein 1, growth differentiation factor 15, interleukin-1 receptor antagonist protein, and E-selectin-showed strong positive associations with hepatocellular carcinoma and were thus validated by enzyme-linked immunosorbent assay (odds ratio = 2.48-14.7, all P < .05) in the NHS and HPFS and by Olink platform (hazard ratio = 1.90-3.93, all P < .05) in the UKB-PPP. Adding these 4 proteins to a logistic regression model of traditional hepatocellular carcinoma risk factors increased the area under the curve from 0.67 to 0.87 in the NHS and HPFS. Consistently, model area under the curve was 0.88 for hepatocellular carcinoma risk prediction in the UKB-PPP. However, the limited number of hepatocellular carcinoma patients in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

Sex dimorphic response to osteocyte miR21 deletion in murine calvaria bone as determined by RNAseq analysis.

Low levels of microRNA (miR) 21 may explain the higher osteocyte apoptosis with Cx43-deficient and aged female mice. However, miR21 exerts a sex-divergent role in osteocytes, regulating bone mass and architecture through non-cell autonomous effects on osteoblasts and osteoclasts, via sex-specific regulation of osteocyte cytokine production. miR21 deficiency improves bone strength in females, and, to a higher extent, in male miR21-deficient mice. To understand the molecular basis for the effects of miR21 deletion, mRNA was isolated from miR21fl/fl (controls) or miR21-deficient (by deletion in cells expressing Cre recombinase under the control of the 8kb fragment of the DMP1 promoter: miR21ΔOt mice). miR21 was 50% lower in miR21ΔOt whole calvaria bone compared to control mice of the corresponding sex. RNAseq was performed in 4 samples/sex and genotype. There were 152 genes with <.05 P-value and >1 absolute log2 fold change in the male data analysis, and expression of most genes was higher in the miR21fl/fl group. Two of the genes, Actn3 and Myh4, had a false discovery rate < 0.1. Gene enrichment analysis of significant genes on both KEGG pathways and gene ontology (GO) gene sets shows that the significant genes were enriched in muscle contraction. Some muscle-related genes like Actn3 were included in multiple significant pathways. For females, only 65 genes had P-value <.05 and >1 absolute log2 fold change. Yet, no significant KEGG or GO pathways, including ≥5 significant genes, were seen, and no overlap of significant genes was found between male and female samples. Therefore, deletion of miR21 has a stronger effect on male transcriptome in calvaria, compared to females. Further, no enrichment of any pathway was detected in female samples. Thus, either there are no differences between 2 groups in female or the effect size is small, and a larger sample size is needed to uncover miR21-dependent differences.

Machine learning to identify endometrial biomarkers predictive of pregnancy success following artificial insemination in dairy cows†.

The objective was to identify a set of genes whose transcript abundance is predictive of a cow's ability to become pregnant following artificial insemination. Endometrial epithelial cells from the uterine body were collected for RNA sequencing using the cytobrush method from 193 first-service Holstein cows at estrus prior to artificial insemination (day 0). A group of 253 first-service cows not used for cytobrush collection were controls. There was no effect of cytobrush collection on pregnancy outcomes at day 30 or 70 or on pregnancy loss between days 30 and 70. There were 2 upregulated and 214 downregulated genes (false discovery rate < 0.05, absolute fold change >2-fold) for cows pregnant at day 30 versus those that were not pregnant. Functional terms overrepresented in the downregulated genes included those related to immune and inflammatory responses. Machine learning for fertility biomarkers with the R package BORUTA resulted in identification of 57 biomarkers that predicted pregnancy outcome at day 30 with an average accuracy of 77%. Thus, machine learning can identify predictive biomarkers of pregnancy in endometrium with high accuracy. Moreover, sampling of endometrial epithelium using the cytobrush can help understand functional characteristics of the endometrium at artificial insemination without compromising cow fertility. Functional characteristics of the genes comprising the set of biomarkers is indicative that a major determinant of cow fertility, at least for first insemination after calving, is immune status of the uterus, which, in turn, is likely to reflect the previous history of uterine disease.