KRAS expression is a prognostic indicator and associated with immune infiltration in breast cancer

The relationship of KRAS expression with KRAS status, prognosis, and tumor-infiltrated T lymphocytes in colorectal cancer

Background

The significance of Kirsten rat sarcoma viral oncogene (KRAS) mutation in colorectal cancer (CRC) is well established; yet, its association with KRAS expression and prognosis warrants further investigation. While high KRAS expression is commonly linked with poorer prognosis in other cancers, its role in CRC remains relatively understudied.

Objective

To explore the correlation between KRAS expression, KRAS status, prognosis, and tumor-infiltrating T lymphocyte density in CRC.

Design

Single-center retrospective study.

Methods

Conducted between 2010 and 2020, this study utilized tumor samples to assess KRAS expression and quantify CD3+/CD8+ T lymphocytes. The Cox proportional hazards model and linear regression analysis were employed to examine the relationship between KRAS expression, prognosis, and tumor-infiltrating T lymphocytes.

Results

This study included 265 CRC patients who underwent radical surgery. No significant association was observed between KRAS expression and KRAS status (p > 0.05). High KRAS expression was associated with poorer overall survival and disease-free survival (p < 0.05). Subgroup analysis revealed that high KRAS expression remained indicative of a worse prognosis in the group with mismatch repair-deficient (dMMR) and KRAS mutant type (p < 0.05). Multivariate analysis confirmed KRAS expression as an unfavorable prognostic factor (p < 0.05). However, the significance of KRAS expression was lost in the dMMR and KRAS mutant-type group regarding overall survival (p > 0.05). Notably, KRAS expression showed a negative correlation with the density of CD8+ T lymphocytes in tumor tissue (p < 0.05), a finding also observed in the dMMR group (p < 0.05).

Conclusion

No association was found between KRAS expression and KRAS mutation status in CRC. Higher KRAS expression was indicative of poorer prognosis for CRC patients, except for those with proficient mismatch repair and KRAS wild type. In addition, in patients with dMMR, KRAS expression was associated with a lower density of CD8+ T lymphocytes in tumor tissue.

Exploring the molecular mechanisms network of breast cancer by multi-omics analysis

BACKGROUND

Breast cancer (BC), the most prevalent malignancy in women globally, still lacks comprehensive research on its molecular targets and necessitates further investigation into the underlying molecular mechanisms driving its initiation and progression.

METHODS

The GSE20685 Series Matrix File downloaded from the Gene Expression Omnibus database was divided into a high-risk group (n = 49) and a low-risk group (n = 278) to construct the co-expression network.

RESULTS

Four hub genes were identified based on the Weighted Gene Co-expression Network Analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed. Hub gene immune infiltration was investigated using the Tumor Immune Estimation Resource database, and CD4+ T cell expression levels were substantially correlated with hub gene expression. Based on the CancerRxGene database (Genomics of Drug Sensitivity in Cancer database), it was found that the hub genes were highly sensitive to common chemotherapy drugs such as AKT inhibitor VIII and Erlotinib. The expression of Secreted Frizzled-Related Protein 1, melanoma-inhibiting activity (MIA), and Keratin 14 was related to tumor mutation burden, and the expression of MIA also affected the microsatellite instability of the tumor. This study employs multi-omics analysis to investigate the molecular network associated with the prognosis of BC, highlighting its intricate connection with the immune microenvironment.

CONCLUSION

These findings pinpoint four crucial genes in BC progression, offering targets for further research and therapy. Their connections to immune infiltration and chemotherapy sensitivity underscore complex interactions in the tumor microenvironment.

Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Germline Mutations in 32 Cancer Susceptibility Genes by Next-Generation Sequencing among Breast Cancer Patients

INTRODUCTION

BRCA1/2 germline mutations are the most well-known genetic determinants for breast cancer. However, the distribution of germline mutations in non-BRCA1/2 cancer susceptibility genes in Chinese breast cancer patients is unclear. The association between clinical characteristics and germline mutations remains to be explored.

METHODS

Consecutive breast cancer patients from Peking University People's Hospital were enrolled. Clinical characteristics were collected, and next-generation sequencing was performed using blood samples of participants to identify pathogenic/likely pathogenic (P/LP) germline mutations in 32 cancer susceptibility genes including homologous recombination repair (HRR) genes.

RESULTS

A total of 885 breast cancer patients underwent the detection of germline mutations. 107 P/LP germline mutations of 17 genes were identified in 116 breast cancer patients including 79 (8.9%) in BRCA1/2 and 40 (4.5%) in 15 non-BRCA1/2 genes. PALB2 was the most frequently mutated gene other than BRCA1/2 but still relatively rare (1.1%). There were 38 novel P/LP germline variants detected. P/LP germline mutations in BRCA1/2 were significantly associated with onset age (p < 0.001), the family history of breast/ovarian cancer (p = 0.010), and molecular subtype (p < 0.001), while being correlated with onset age (p < 0.001), site of breast tumor (p = 0.028), and molecular subtype (p < 0.001) in HRR genes.

CONCLUSIONS

The multiple-gene panel prominently increased the detection rate of P/LP germline mutations in 32 cancer susceptibility genes compared to BRCA1/2 alone. Onset younger than or equal to 45 years of age, bilateral and triple-negative breast cancer patients may be more likely to be recommended for detecting P/LP germline mutations in HRR genes.

Case report: long-term sustained remission in a case of metastatic colon cancer with high microsatellite instability and KRAS exon 2 p.G12D mutation treated with fruquintinib after local radiotherapy: a case report and literature review

To demonstrate the efficacy of fruquintinib administration after local radiotherapy in a patient with metastatic colon cancer with high microsatellite instability and the KRAS exon 2 p. G12D mutation. The patient was administered four cycles of pembrolizumab intravenous infusion and achieved stable disease as the best outcome. He was then underwent follow-up concurrent radiochemical therapy (local DT4600cGy/23f/32d radiotherapy, and S-1 to increase sensitivity to radiotherapy), but this had little efficacy. Following this, he was administered fruquintinib and achieved sustained partial remission. At the time of last follow-up, the patient was in continuous remission for 30 months. Administration of fruquintinib after local radiotherapy may be an effective treatment for specific populations with metastatic colorectal cancer.

K-Ras4A Plays a More Significant Role than K-Ras4B in Ductal Carcinoma of Breast

Background

K-Ras mutations rarely occur in breast cancer. However, studies have supported that K-Ras upregulation is involved in breast cancer pathogenesis. Two main K-Ras transcript variants; K-Ras4A and K-Ras4B, originate from the alternative splicing of exon 4. In this study, we aimed to evaluate variations in the expression of K-Ras4A and K-Ras4B and their role in breast ductal carcinoma.

Methods

Total RNA was extracted from breast tumors, and the NATs obtained via mastectomy. Patients were selected from new cases of breast cancer with no prior history of chemotherapy. Relative mRNA expression was calculated based on a pairwise comparison between the tumors and the NATs following normalization to the internal control gene. Predictive values of the transcript variants were examined by ROC curve analysis.

Results

A statistically significant increase was found in the K-Ras4A and K-Ras4B expression with the mean fold changes of 7.58 (p = 0.01) and 2.47 (p = 0.001), respectively. The K-Ras4A/K-Ras4B ratio was lower in the tumors than that of the normal tissues. ROC curve analysis revealed the potential of K-Ras4A (AUC: 0.769) and K-Ras4B (AUC: 0.688) in breast cancer prediction. There was also a significant association between K-Ras4B expression and HER2 statues (p = 0.04). Furthermore, a significant link was detected between K-Ras4A expression and pathological prognostic stages (p = 0.04).

Conclusion

Our findings revealed that expression levels of K-Ras4A and K-Ras4B is higher in the tumor compared to the normal breast tissues. Increase in K-Ras4A expression was more significant than that of K-Ras4B.

ARL2 is required for homologous recombination repair and colon cancer stem cell survival

ARL2 regulates the dynamics of cytological components and is highly expressed in colon cancer tissues. Here, we report novel roles of ARL2 in the cell nucleus and colon cancer stem cells (CSCs). ARL2 is expressed at relatively low levels in K‐RAS active colon cancer cells, but its expression is induced in CSCs. Depletion of ARL2 results in M phase arrest exclusively in non‐CSC cultured cells; in addition, DNA break stress accumulates in CSCs leading to apoptosis. ARL2 expression is positively associated with the expression of all six RAD51 family genes, which are essential for homologous recombination repair (HRR). Furthermore, ARL2 is required for HRR and detected within chromatin compartments. These results demonstrate the requirement of ARL2 in colon CSC maintenance, which possibly occurs through mediating double‐strand break DNA repair in the nucleus.