8q24 Polymorphisms and Diabetes Mellitus Regulate Apolipoprotein A-IV in Colorectal Carcinogenesis

Histone modification-based functional characterization and genetic association of polymorphisms in LRRC6 and MTMR10 within CRC susceptibility regions 8q24 and 15q13.3

BACKGROUND

Genome-wide association studies (GWAS) have identified susceptibility loci for colorectal cancer (CRC), but the underlying mechanisms remain unclear. This study investigates functional genetic variants in promoter regions of Leucine Rich Repeat Containing 6 (LRRC6) at 8q24 and Myotubularin Related Protein 10 (MTMR10) at 15q13.3 and their association with CRC susceptibility.

METHODS

Bioinformatics and ChIP-seq data for H3K4me3 were used to identify SNPs in CRC risk regions 8q24 and 15q13.3 that might affect transcription factor binding, gene expression, or prognosis. These variants were validated in a case-control study of 840 CRC patients and 840 healthy controls from China. SNP functionality was evaluated using luciferase assays.

RESULTS

Two significant SNPs, LRRC6 rs79600483 (8q24) and MTMR10 rs3743231 (15q13.3), were identified. Expression analysis revealed higher LRRC6 mRNA levels in CRC tissues, correlating with improved survival, while lower MTMR10 expression was linked to better outcomes. Case-control analysis showed that the LRRC6 rs79600483 GG genotype (OR = 2.43, 95 % CI = 1.04-5.67, P = 0.040) and AG genotype (OR = 1.26, 95 % CI = 1.01-1.57, P = 0.045), and the MTMR10 rs3743231 CC genotype (OR = 2.83, 95 % CI = 1.55-5.19, P = 0.001), significantly increased CRC risk. Luciferase assays demonstrated that the G allele of LRRC6 rs79600483 and C allele of MTMR10 rs3743231 increased promoter activity.

CONCLUSIONS

Polymorphisms in LRRC6 and MTMR10 genes contribute to CRC susceptibility by modulating gene expression and transcription. These findings enhance understanding of CRC genetic susceptibility and may guide future therapeutic strategies.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Comparison of laparoscopic surgery with open standard surgery for advanced gastric carcinoma in a single institute: a propensity score matching analysis

BACKGROUND

Compared with open standard gastrectomy (OG), laparoscopic gastrectomy (LG) did not result in inferior disease-free survival for early-stage and locally advanced gastric cancer (AGC). However, whether LG for AGC in elderly patients is more beneficial than OG is unclear.

METHODS

This study examined 458 patients with AGC. The mortality, morbidity, and prognosis were compared by age, gender, T and N factors, and pathological stage in the LG and OG groups using propensity score matching analysis. For the final analysis, 151 pairs of patients were selected from at each group.

RESULTS

The results showed that no significant difference in mortality and morbidity existed between the two groups. The 5-year relapse-free survival (RFS) rates were 70% and 62% in the LG and OG groups, respectively (p = 0.104). The 5-year RFS rates in patients with pathological stages I, II, and III who had undergone LG were 84%, 80%, and 55%, respectively, and 78%, 70%, and 45%, respectively, in those who had undergone OG (p < 0.005). The 5-year RFS rates in nonelderly patients who underwent LG or OG were 75% and 68%, respectively, and 58% and 40%, respectively, in elderly patients who underwent LG or OG (p < 0.005).

CONCLUSION

The 5-year RFS rates in patients with AGC at each stage did not significantly differ between LG and OG. However, the benefits at 5-year RFS in patients who underwent LG compared with OG were larger in elderly patients than those in nonelderly patients.

Correlation between Expression Profiles of Key Signaling Genes in Colorectal Cancer Samples from Type 2 Diabetic and Non-Diabetic Patients

Several lines of epidemiological and biochemical evidence support the association of type 2 diabetes mellitus (T2DM) and colorectal cancer (CRC). T2DM has been shown to impinge on the transcriptome of colon tumor cells, promoting their proliferation and invasion. In order to gain insight into diabetes-specific modulation of colon cancer signaling, we analyzed gene expression patterns of more than five hundred genes encoding signaling proteins on TaqMan OpenArray panels from colonoscopic colorectal tumor samples of type 2 diabetic and non-diabetic patients. In total, 48 transcripts were found to be differentially expressed in tumors of T2DM patients as compared to healthy colon samples. Enrichment analysis with the g:GOSt (Gene Ontology Statistics) functional profiling tool revealed that the underlying genes can be classified into five signaling pathways (in decreasing order of significance: Wnt (wingless-type)/β-catenin; Hippo; TNF (tumor necrosis factor); PI3K/Akt (phosphoinositide-3 kinase/protein kinase B), and platelet activation), implying that targeted downregulation of these signaling cascades might help combat CRC in diabetic patients. Transcript levels of some of the differentially expressed genes were also measured from surgically removed diabetic and non-diabetic CRC specimens by individual qPCR (quantitative real-time PCR) assays using the adjacent normal tissue mRNA levels as an internal control. The most significantly altered genes in diabetic tumor samples were largely different from those in non-diabetic ones, implying that T2DM profoundly alters the expression of signaling genes and presumably the biological characteristics of CRC.

Systemic Proteome Alterations Linked to Early Stage Pancreatic Cancer in Diabetic Patients

BACKGROUND

Diabetes is a risk factor associated with pancreatic ductal adenocarcinoma (PDAC), and new adult-onset diabetes can be an early sign of pancreatic malignancy. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population.

METHODS

A spectral library-based proteomic platform was applied to interrogate biomarker candidates in plasma samples from clinically well-defined diabetic cohorts with and without PDAC. Random forest algorithm was used for prediction model building and receiver operating characteristic (ROC) curve analysis was applied to evaluate the prediction probability of potential biomarker panels.

RESULTS

Several biomarker panels were cross-validated in the context of detection of PDAC within a diabetic background. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14 (CD14), tetranectin (CLEC3B), gelsolin (GSN), histidine-rich glycoprotein (HRG), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), plasma kallikrein (KLKB1), leucine-rich alpha-2-glycoprotein (LRG1), pigment epithelium-derived factor (SERPINF1), plasma protease C1 inhibitor (SERPING1), and metalloproteinase inhibitor 1 (TIMP1), demonstrated an area under curve (AUC) of 0.85 and a two-fold increase in detection accuracy compared to CA19-9 alone. The study further evaluated the correlations of protein candidates and their influences on the performance of biomarker panels.

CONCLUSIONS

Proteomics-based multiplex biomarker panels improved the detection accuracy for diagnosis of early stage PDAC in diabetic patients.

Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel

BACKGROUND

One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin).

METHODS

A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets.

RESULTS

Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC.

CONCLUSION

MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.