Alternative splicing is an important mechanism behind KAI1 loss of function in breast cancer patients from Saudi Arabia

The untold story of CD82: Exploring its non-canonical roles in cancer

CD82, traditionally recognized as a metastasis suppressor within the tetraspanin family, has emerged as a key player in diverse cancer-related processes beyond its canonical functions. This review highlights recent research on the non-canonical roles of CD82 in cancer progression, with a particular focus on its regulation of immune cell interactions, its impact on tumor microenvironment modulation, and its potential as both a therapeutic target and a biomarker. By examining the novel functions of CD82 in immune modulation and its influence on key signaling pathways, we propose that CD82 offers promising avenues for therapeutic interventions in cancer. This paper provides a comprehensive synthesis of the current understanding of CD82's expanded roles, underscoring its potential in improving cancer diagnosis and therapy.

Apoptosis-inducing proteins with reduced expression in breast cancer: A review article

Breast cancer is considered one of the most abundant malignancies with high morbidity and mortality. Traditional cancer treatments possess various weaknesses, including a lack of specificity and numerous side effects. Novel cancer therapies aim to overcome the shortcomings of traditional therapies and offer more efficient and safe treatments. Designing and identifying novel proteins that can practically and specifically inhibit tumors by inducing apoptosis can interest researchers in this field. This review discusses recent studies on the use of four promising proteins, KAI1, Apoptin, BIF-1, and DFF-40, for breast cancer treatment. This study also focuses on alterations in the expression of the androgenic proteins and novel strategies for better penetration and delivery of these proteins. Lastly, prospects for protein-based targeted cancer therapy and future studies in this field are highlighted.

Tetraspanin CD82 Correlates with and May Regulate S100A7 Expression in Oral Cancer

Many metastatic cancers with poor prognoses correlate to downregulated CD82, but exceptions exist. Understanding the context of this correlation is essential to CD82 as a prognostic biomarker and therapeutic target. Oral squamous cell carcinoma (OSCC) constitutes over 90% of oral cancer. We aimed to uncover the function and mechanism of CD82 in OSCC. We investigated CD82 in human OSCC cell lines, tissues, and healthy controls using the CRISPR-Cas9 gene knockout, transcriptomics, proteomics, etc. CD82 expression is elevated in CAL 27 cells. Knockout CD82 altered over 300 genes and proteins and inhibited cell migration. Furthermore, CD82 expression correlates with S100 proteins in CAL 27, CD82KO, SCC-25, and S-G cells and some OSCC tissues. The 37-50 kDa CD82 protein in CAL 27 cells is upregulated, glycosylated, and truncated. CD82 correlates with S100 proteins and may regulate their expression and cell migration. The truncated CD82 explains the invasive metastasis and poor outcome of the CAL 27 donor. OSCC with upregulated truncated CD82 and S100A7 may represent a distinct subtype with a poor prognosis. Differing alternatives from wild-type CD82 may elucidate the contradictory functions and pave the way for CD82 as a prognostic biomarker and therapeutic target.

Role of a metastatic suppressor gene KAI1/CD82 in the diagnosis and prognosis of breast cancer

Globally, breast cancer is the most common type of cancer in females and is one of the leading causes of cancer death in women. The advancement in the targeted therapies and the slight understanding of the molecular cascades of the disease have led to small improvement in the rate of survival of breast cancer patients. However, metastasis and resistance to the current drugs still remain as challenges in the management of breast cancer patients. Metastasis, potentially, leads to failure of the available treatment, and thereby, makes the research on metastatic suppressors a high priority. Tumor metastasis suppressors are several genes and their protein products that have the capability of arresting the metastatic process without affecting the tumor formation. The metastasis suppressors KAI1 (also known as CD82) has been found to inhibit tumor metastasis in various types of solid cancers, including breast cancer. KAI1 was identified as a metastasis suppressor that inhibits the process of metastasis by regulating several mechanisms, including cell motility and invasion, induction of cell senescence, cell–cell adhesion and apoptosis. KAI1 is a member of tetraspanin membrane protein family. It interacts with other tetraspanins, chemokines and integrins to control diverse signaling pathways, which are crucial for protein trafficking and intracellular communication. It follows that better understanding of the molecular events of such genes is needed to develop prognostic biomarkers, and to identify specific therapies for breast cancer patients. This review aims to discuss the role of KAI1/CD82 as a prognosticator in breast cancer.

Investigations of a Possible Role of SNPs in KAI1 Gene on Its Down-Regulation in Breast Cancer

Objective:

KAI1 (CD82) is a metastasis suppressor gene known to be down-regulated in carcinomas of breast, prostate and many other organs. The mechanism of KAI1 down-regulation is complex and not well understood. Here, we investigate the role of 8 SNPs (not previously studied) in KAI1 gene that could influence its expression in tumor tissue samples of breast cancer patients from the Eastern province of Saudi Arabia.

Methods:

Single nucleotide polymorphisms (SNPs) in KAI1 gene were selected from the NCBI website (dbSNP) and were then filtered for those SNPs causing stop codon mutations (rs139889503 and rs150533529) or nonsynonymous mutation in the 5’-UTR (rs11541048, rs77359459, rs115500759, rs182579675, rs200238062, and rs372733853). SNPs genotyping was performed using TaqMan SNP Genotyping Assay and the results were correlated with KAI1 protein expression profile by immunohistochemistry (IHC) on formalin-fixed paraffin-embedded (FFPE) samples of breast cancer and control none-neoplastic tissues.

Results:

KAI1 expression by IHC was observed in all none-neoplastic breast tissue samples and only in 35% out of the 59 breast cancer tissue samples. None of the samples was homozygous for the stop codon allele A in the SNP rs139889503 or allele T in the SNP rs150533529. The SNPs in the 5-UTR, rs11541048, rs115500759, and rs182579675, were only present in the homozygous state for the G and C alleles respectively in both cancer and control samples. The other SNPs in the 5’-UTR (rs77359459, rs200238062, and rs372733853) had no significant difference in the allele distribution between KAI1 expressing or none-expressing tissue samples.

Conclusion:

Our findings showed no significant effect of the studied SNPs on down-regulation of KAI1 expression.

Systematic Profile Analysis of Prognostic Alternative Messenger RNA Splicing Signatures and Splicing Factors in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSC) is a common malignancy with high mortality and poor prognosis. Alternative splicing (AS) is a transcriptional regulation mechanism that generates multiple transcripts from same genes, and aberrant AS signatures of cancers can be predictive for prognosis. We identified the survival-related AS events and splicing factors (SFs) from the RNA sequencing data and the corresponding clinical information of an HNSC cohort downloaded from The Cancer Genome Atlas (TCGA) and SpliceSeq. The independent prognostic predictors were assessed by Cox proportional regression analysis, and the regulatory network of SFs and AS events was analyzed by Spearman's test and constructed. A total of 4626 survival-related AS events in 3280 genes were identified, and most were protective factors. Among the different types of splicing events, exon skip was the most frequent. The prognostic models were constructed for each type of AS, and the area under the curve of the receiver operating characteristic curve of the combined prognostic model was 0.765, indicating good predictive performance. Finally, a correlation network between SF and AS events was constructed. We identified prognostic predictors based on AS events that stratified HNSC patients into the high- and low-risk groups, and revealed splicing networks that provide insights into the underlying mechanisms.