Potential biomarkers and immune characteristics of small bowel adenocarcinoma

Abstract

Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA. The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein‒protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages. A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E−17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC = 0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA. Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm

“…Violin plots were drawn by the ‘vioplot’ package of R, which was mainly applied to express the variation in the infiltration expression level of the immune cells among the control and FSGS groups. We estimated this link between the two core genes and immune-infiltrating cells with ‘tidyverse’ [ 27 ] and ‘reshape2’ [ 28 ]. Subsequently, we applied ‘ggplot2’ for data visualization.…”

Immune signature of small bowel adenocarcinoma and the role of tumor microenvironment

“…Increased percentages of TILs are also correlated with the infiltration of B cells, dendritic cells, and natural killer cells, enhancing the immune response. Along those lines, T-reg and T-helper cells might also be present, regulating the immune response and leading to a worse prognosis[ 5 , 20 , 21 ]. FoxP3 T-reg cells are associated with deeper depth of invasion, but this association is not significant.…”

“…SBA presents a challenge in terms of early detection, as it is frequently asymptomatic for an extended period (2 to 8 months), leading to late-stage discovery and poor prognosis. Its detection often arises from complications such as intestinal perforation, ileus, and unbridled gastrointestinal hemorrhaging and by the time of the diagnosis, nearly one-third of individuals are presented with distant metastasis and an advances stage[ 1 , 2 , 5 - 7 ]. However, the advanced stage of the diagnosis and the lack of effective chemotherapy lead to a poor prognosis.…”

Low expression of ACOT13 predicts poor prognosis and immunotherapy outcome in ovarian cancer

“…The CIBERSORT analytical tool was used to determine the cell composition of OC tissues based on their gene expression profiles and ACOT13 expression data. To analyze the relationship between ACOT13 and tumor microenvironment or immune cell infiltration, the R packages "ggplot2", "ggpubr", and "ggExtra" were used [27].…”