Association of cyclin-dependent kinase inhibitor 2A/B with increased risk of developing breast cancer

Breast cancer prediction using different machine learning methods applying multi factors

OBJECTIVE

Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.

METHODS

The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated.

RESULT

Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54).

CONCLUSION

Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

Evaluation of rs10811661 polymorphism in CDKN2A / B in colon and gastric cancer

One of the causes of colon and gastric cancer is the dysregulation of carcinogenic genes, tumor inhibitors, and micro-RNA. The purpose of this study is to apply rs10811661 polymorphism in CDKN2A /B gene as an effective biomarker of colon cancer and early detection of gastric cancer. As a result,400 blood samples, inclusive of 200 samples from healthy individuals and 200 samples (100 samples from intestinal cancer,100 samples from stomach cancer) from the blood of someone with these cancers, to determine the genotype of genes in healthful and ill people through PCR-RFLP approach and Allelic and genotypic tests of SPSS software. To observe the connection between gastric cancer and bowel cancer risk and genotypes, the t-student test for quantitative variables and Pearson distribution for qualitative variables have been tested and the results have been evaluated using the Chi-square test. The effects confirmed that the highest frequency of TT genotypes is in affected individuals and CC genotype is in healthful individuals. In addition, it confirmed that women were more inclined than men to T3 tumor invasion and most grade II and III colon cancers, and in older sufferers with gastric cancer, the grade of tumor tended to be grade I. Among genetic variety and rs10811661, with invasiveness, there is a tumor size and degree in the affected person. In summary, our findings suggest that the rs10811661 polymorphism of the CDKN2A / B gene is strongly associated with the occurrence of intestinal cancer and stomach is linked to its potential role as a prognostic biomarker for the management of bowel cancer and stomach.

The Long Non-Coding RNA ANRIL in Cancers

ANRIL (Antisense Noncoding RNA in the INK4 Locus), a long non-coding RNA encoded in the human chromosome 9p21 region, is a critical factor for regulating gene expression by interacting with multiple proteins and miRNAs. It has been found to play important roles in various cellular processes, including cell cycle control and proliferation. Dysregulation of ANRIL has been associated with several diseases like cancers and cardiovascular diseases, for instance. Understanding the oncogenic role of ANRIL and its potential as a diagnostic and prognostic biomarker in cancer is crucial. This review provides insights into the regulatory mechanisms and oncogenic significance of the 9p21 locus and ANRIL in cancer.

Non-Coding RNA Polymorphisms (rs2910164 and rs1333049) Associated With Prognosis of Lung Cancer Under Platinum-Based Chemotherapy

Purpose: Lung cancer is the largest cause of cancer deaths in the world. Platinum-based chemotherapy is a foundation of first-line chemotherapy. However, the prognosis of lung cancer treated with platinum-based chemotherapy is still a challenge. Single nucleotide polymorphism of non-coding RNA has the potential to be a biomarker, but its effectiveness has yet to be comprehensively assessed. In this study, we explored the association between polymorphisms of non-coding RNA and prognosis of lung cancer patients receiving platinum-based chemotherapy.

Materials and Methods: For 446 lung cancer patients receiving platinum-based chemotherapy, 22 single nucleotide polymorphisms of microRNA and long noncoding RNA were genotyped by MALDI-TOF mass spectrometry. Cox regression analysis, Kaplan-Meier method, and long-rank test have been performed to assess the association of overall and progression-free survival with polymorphisms.

Results: In the additive and dominant models, genetic polymorphism of ANRIL rs1333049 (G > C) was significantly associated with progression-free survival. Additive model: CC vs GC vs GG [HR = 0.84, p = 0.021, 95% CI (0.73–0.97)]; Recessive model: CC vs GG + GC [HR = 0.77, p = 0.026, 95% CI (0.61–0.97)]. In the dominant model, compared with the CC genotype patients, lower risk of death [HR = 0.81, p = 0.036, 95% CI (0.66–0.99)] and lower risk of progression [HR = 0.81, p = 0.040, 95% CI (0.67–0.99)] have been observed on the patients with CG or GG genotype in miR-146A rs2910164.

Conclusion: Our research demonstrated the potential of using ANRIL rs1333049 (G > C) and miR-146A rs2910164 (C > G) as biomarkers to support the prediction of a better prognosis for lung cancer patients receiving platinum-based chemotherapy.

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

The association of a genetic variant in CDKN2A/B gene and the risk of colorectal cancer

Colorectal cancer is among the most aggressive tumors, and its development involves an interplay between various genetic and environmental familial risk factors. Several genetic polymorphisms have been reported to be associated with colorectal cancer in recent studies. In this current study, we aimed to evaluate the possible relationship between a CDKN2A/B, single nucleotide polymorphisms (SNP) (rs10811661), with the risk of colorectal cancer. A total of 541 individuals with, or without cancer were recruited. DNA was extracted, and genotyped using a Taq-Man based real-time PCR method. The rs10811661 SNP was associated with an increased risk of colorectal cancer (additive model: OR=3.46, CI= 1.79-6.69, p<0.0001 and recessive model: 5.72, CI= 3.12-10.49, p<0.0001). The distribution of minor alleles in the total population for homozygote allele was 9.2 %, while this was 20.1 % for heterozygotes. In summary, our findings indicate that the rs10811661 polymorphism of the CDKN2A/B gene was strongly related to the occurrence of colorectal cancer suggesting its potential role as a prognostic biomarker for the management of colorectal cancer.