iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: http://pubmed.ncbi.nlm.nih.gov/39206620/
Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Aug;13(16):e70153.
doi: 10.1002/cam4.70153.

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations

Ahao Wu  1   2 , Tengcheng Hu  1 , Chao Lai  1 , Qingwen Zeng  1 , Lianghua Luo  1 , Xufeng Shu  1 , Pan Huang  1 , Zhonghao Wang  1 , Zongfeng Feng  1 , Yanyan Zhu  3 , Yi Cao  1 , Zhengrong Li  1   4
Affiliations

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations

Ahao Wu et al. Cancer Med. 2024 Aug.

Abstract

Background: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens.

Methods: Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features.

Results: We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages.

Conclusions: The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

Keywords: RAD51D; XRCC2; gastric cancer; homologous recombination; radiomics.

PubMed Disclaimer

Conflict of interest statement

All authors declare that the research was conducted in the absence of any commercial or financial relationships.

Figures

FIGURE 1
FIGURE 1
Flow of the study design.
FIGURE 2
FIGURE 2
Logistic regression, LASSO regression, and SVM were employed to screen for gastric cancer biomarkers. Logistic regression‐screened biomarkers: (A) Predictive model AIC values; (B) ROC curve of the training cohort; (C) ROC curve of the test cohort. SVM‐screened biomarkers: (D) Relationship between the number of features and the model accuracy; (E) ROC curve of the training cohort; (F) ROC curve of the test cohort. LASSO regression‐screened biomarkers: (G) Relationship between the lambda values and bias percentage of features; (H) ROC curve of the training cohort; (I) ROC curve of the test cohort.
FIGURE 3
FIGURE 3
DT and RF were used to screen for gastric cancer biomarkers. DF‐screened biomarkers: (A) The significance of features for the predictive model; (B) Decision tree diagram; (C) The ROC curve of the training cohort; (D) The ROC curve of the test cohort. RF‐screened biomarkers: (E) Importance of features in improving the model accuracy and reducing the Gini coefficient; (F) Relationship between the number of split points and error in the tree; (G) Relationship between the number of trees and OOB error in RF, where “0” represents the normal group, and “1” represents the gastric cancer group; (H) The ROC curve of the training cohort; (I) The ROC curve of the test cohort.
FIGURE 4
FIGURE 4
(A) Venn diagram; (B, C) Differential expression of RAD51D and XRCC2 in 39 pairs of gastric cancer tissues and adjacent tissues; (D, E) Differential expression of RAD51D and XRCC2 in gastric cancer cell lines; (F, G) Differential expression of RAD51D and XRCC2 in gastric cancer tissues and paired normal tissues; (H, I) Relationship between the expression of RAD51D and XRCC2 and prognosis.
FIGURE 5
FIGURE 5
(A–H) Correlations of RAD51D with vascular cancer thrombus status, neurological invasion status, pathological T stage, N stage, number of lymph node metastases, lymph node metastasis rate, TNM stage, and Lauren's classification. (I–P) Correlation of XRCC2 with vascular cancer thrombus, neurological invasion, pathological T stage, N stage, number of lymph node metastases, lymph node metastasis rate, TNM stage, and Lauren's classification.
FIGURE 6–
FIGURE 6–
HGC‐27 cell line: (A, B) Flow cell cycle diagram of treated and control groups; (E) Comparison of cell cycle differences between treated and control groups; (G, H) Flow cell apoptosis diagram of treated and control groups; (K) Comparison of apoptosis percentage differences between treated and control groups; (M) IC50 curve of the HGC‐27 cell line. MKN‐45 cell line: (C, D) Flow cell cycle diagram of the treated and control groups; (F) Comparison of cell cycle differences between the treated and control groups; (I, J) Flow cell apoptosis diagram of the treated and control groups; (L) Comparison of apoptosis percentage differences between the treated and control groups; (N) IC50 curve of the MKN‐45 cell line.
FIGURE 7
FIGURE 7
(A–H) Construction and evaluation of the radiomics model for predicting RAD51D expression. (A) Relationships between the lambda values and radiomics feature coefficients; (B) Relationships between the lambda values and bias percentages of radiomics features; (C) ROC curve of the training cohort; (D) Calibration curve of the predictive model in the training cohort. The abscissa represents the predictive probability of RAD51D expression status, and the ordinate represents the actual probability of RAD51D expression status; (E) DCA of the training cohort, where “All” indicates that all patients were treated with cisplatin‐containing chemotherapy regimens and “None” indicates that all patients were treated with cisplatin‐free chemotherapy regimens; (F) ROC curve of the validation cohort; (G) Calibration curve in the validation cohort; (H) DCA of the validation cohort; (I–P) Construction and evaluation of the radiomics model for predicting XRCC2 expression.
FIGURE 8
FIGURE 8
(A–H) Correlations of RAD51D radiomics score with vascular cancer thrombus status, neurological invasion status, pathological T stage, N stage, number of lymph node metastases, lymph node metastasis rate, TNM stage, and Lauren's classification. (I–P) Correlation of XRCC2 radiomics scores with vascular cancer thrombus, neurological invasion, pathological T stage, N stage, number of lymph node metastases, lymph node metastasis rate, TNM stage, and Lauren's classification.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229‐263. - PubMed
    1. Wang YX, Shao QS, Yang Q, et al. Clinicopathological characteristics and prognosis of early gastric cancer after gastrectomy. Chin Med J. 2012;125:770‐774. - PubMed
    1. Molinari C, Tedaldi G, Rebuzzi F, et al. Early gastric cancer: identification of molecular markers able to distinguish submucosa‐penetrating lesions with different prognosis. Gastric Cancer. 2021;24:392‐401. - PubMed
    1. Shitara K, Yatabe Y, Matsuo K, et al. Prognosis of patients with advanced gastric cancer by HER2 status and trastuzumab treatment. Gastric Cancer. 2013;16:261‐267. - PubMed
    1. Hu C, Chen W, Li F, et al. Deep learning radio‐clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients. Int J Surg. 2023;109:1980‐1992. - PMC - PubMed

MeSH terms