Category Archives: Transhuman News

Physicians at a Boston hospital adjusted medical management for nearly three-quarters of patients with infantile- or childhood-onset epilepsy who were diagnosed with genetic epilepsy, researchers reported at the annual meeting of the American Epilepsy Society. The findings provide new insight into the usefulness of genetic tests in children with epilepsy of unknown cause.

Genetic testing is significantly impacting medical care in a population of individuals with infantile- or childhood-onset epilepsy. Genetic testing should be included as part of the standard evaluation of individuals with unexplained pediatric epilepsy as a means of achieving diagnostic precision and informing clinical management, study lead author Isabel Haviland, MD, a neurologist with Boston Childrens Hospital/Harvard Medical School, said in an interview.

According to Haviland, the causes of epilepsy are unexplained in an estimated two-thirds of pediatric epilepsy cases. Increasingly, when genetic testing is available, previously unexplained cases of pediatric epilepsy are being found to have single-gene etiologies, she said. Though a genetic diagnosis in this population has implications for medical care, the direct impact on medical management in a clinical setting has not been measured. We aimed to describe the impact of genetic diagnosis on medical management in a cohort of individuals with pediatric epilepsy.

Researchers tracked 602 patients at Boston Childrens Hospital who received next-generation gene sequencing testing from 2012 to 2019. Of those, Haviland said, 152 (25%) had a positive result that indicated genetic epilepsy (46% female, median age of onset = 6 months [2-15 months]). These patients were included in the study.

We documented an impact on medical management in nearly three-fourths of participants (72%), Haviland said. A genetic diagnosis affected at least one of four categories of medical management, including care coordination (48%), treatment (45%), counseling about a change in prognosis (28%), and change in diagnosis for a few individuals who had a prior established diagnosis (1%).

As examples, she mentioned three cases:

Testing revealed that a subject has a disease-causing genetic variant in a gene called PRRT2. This gene is involved in the release of neurotransmitters in the brain, Haviland said. Thanks to his diagnosis, he was treated with the antiseizure medication oxcarbazepine, which is often effective for epilepsy caused by variants in this gene. He had excellent response to the medication and later became seizure free.

A subject had a variation in the SCN1A gene that causes types of epilepsy. At the time of his diagnosis, there was a trial for a medication called fenfluramine being offered for individuals with SCN1A variants, and his family elected to participate, she said. This medication was later approved by the [Food and Drug Administration] for SCN1A-related epilepsy.

Testing identified disease-causing variant in the GRIN2A gene in another subject. This gene is involved in brain cell communication, Haviland said. This individual was treated with memantine, which acts on the specific biological pathway affected by the gene. This treatment would not have been considered without the genetic diagnosis as it is currently only approved for Alzheimers disease.

In addition, Haviland said, researchers found that there was impact on medical management both in those with earlier age of epilepsy onset (under 2 years) and those with later age of onset, as well as both in those with developmental disorders (such as autism spectrum disorder, intellectual disability and developmental delay) and those with normal development.

As for the cost of genetic tests, Haviland pointed toa 2019 studythat she said estimated epilepsy panel testing runs from $1,500 to $7,500, and the whole exome sequencing from $4,500 to $7,000. Insurers sometimes cover testing, but not always, she said. In some cases, insurance will only cover testing if it is documented that results will directly alter medical management, which highlights the importance of our findings.

No study funding was reported. Haviland and several other authors report no disclosures. One author reports consulting fees from Takeda, Zogenix, Marinus, and FOXG1 Research Foundation. Another author reports research support from the International Foundation for CDKL5 Research.

This story originally appeared onMDedge.com, part of the Medscape Professional Network.

Original post:
Genetic Tests Prompt Therapy Adjustments in Epilepsy - Medscape

The Omicron variant of COVID-19 has quickly taken over as the dominant strain of the virus in the United States. Omicron, which was responsible for just 0.7% of COVID-19 cases in the country on December 4, was responsible for a whopping 73.2% of cases on December 18and that percentage is expected to grow.

With this, Omicron has unseated the Delta variant as the dominant strain of SARS-CoV-2, the virus that causes COVID-19, in the country. If youre diagnosed with COVID-19, its more than understandable to want to know what strain of COVID you have. While doctors say this isnt easy information to obtaineven for themthere may be a way to learn what strain of COVID-19 you have. Heres what you need to know about how to know what strain of COVID you have.

When the Centers for Disease Control and Prevention (CDC) releases information on percentages of variants in the U.S., theyre not analyzing every single positive test in the country to get those numbers, explains Thomas Russo, M.D., professor and chief of infectious disease at the University at Buffalo in New York.

Instead, the CDC conducts genetic sequencing on a percentage of positive tests across the country. For example, to get the latest results, the CDC analyzed 160 test results from Alabama, 4,157 from Arizona, 18,424 from California, and so on, to get a breakdown of what percentage of those positive tests matched up with a particular strain of COVID-19.

CDC officials and lab technicians dont even know whose test results theyre analyzingfor privacy purposes, theyre de-identified before testing. Meaning, your name is taken out of the equation. As a result, the CDC couldnt call you or your doctor and say what strain of COVID-19 you had, even if they had the manpower to do it, Dr. Russo points out.

Not usually, and there are a few reasons for this. The tests that are run by doctors in offices and hospitals dont sequence and specify the variant, says infectious disease expert Amesh A. Adalja, M.D., a senior scholar at the Johns Hopkins Center for Health Security. The rapid antigen tests that are often used to get quick results instead just tell if the test result is positive or negative for COVID-19.

Even a PCR test, which is considered the gold standard of COVID-19 testing, is not going to tell youits not going to sequence the test, Dr. Adalja says. PCR testing can also take days to give you results, which is why your doctor will typically just do a rapid test in their office to help you know quickly whether you have COVID-19 or not, Dr. Russo explains.

Heres where things get a little tricky. The Omicron variant has a particular genetic sequencing that can show up differently on PCR test results that you wouldnt see with other strains of COVID-19, like Delta. Its called S-gene target failure and can show up as a band drop-out on the test results, Dr. Russo says.

This can give you a hint that its Omicron, but its not definitive, Dr. Adalja says. Right now, if you got a PCR test, the lab technician could tell you that this looks consistent with Omicron.

That said, Dr. Adalja points out that most family medicine doctors arent going to be asking about thisthey typically just want to know if you're positive or negative. Its more something that infectious disease experts and microbiologists would ask about. Still, he says, If you wanted to know and your doctor gave you a PCR test, you could ask them if there was S-gene target failure. If there is, for all intents and purposes, thats an Omicron.

Theres a slight caveat with all of this, though: Dr. Russo points out that not every case of Omicron shows up with S-gene target failure on a PCR test result. Were still trying to understand why that is, he says. So, its possible to have Omicron and not have that show up on a PCR test resultor you could simply be infected with the Delta strain.

It wont be a slam-dunk diagnosis, but there may be some indication if you have one variant or the other, depending on your COVID-19 symptoms and vaccination status, Dr. Russo says.

If youre fully vaccinated, its been at least two weeks since youve had a booster shot, and you still contract COVID-19, the odds are high that you have the Omicron variant, Dr. Russo says. Statistically speaking, youre more likely to be infected with Omicron than Delta anyway, he says. But thats particularly true if youve been boostedOmicron seems to be more resistant to vaccination.

According to a recent CDC study, those symptoms may include:

But if you havent been vaccinated against COVID-19, Omicron and Delta are likely to cause similar symptoms of COVID-19. Per the CDC, those include:

For the average-risk patient, it doesnt really matter, Dr. Russo says. At the end of the day, if youre infected, you want to go ahead and monitor for more serious symptoms like shortness of breath and present to your healthcare provider, regardless of if youre infected with Delta or Omicron, he says. For healthcare providers, symptoms and severity of illness usually drive treatment.

There is one exception, though. Certain monoclonal antibody treatments dont work against Omicron, Dr. Adalja says. He specifically cites the Eli Lilly monoclonal antibodies as being expected to be less effective against the variant, while GSKs monoclonal antibody treatment is expected to do well against Omicron. Its important from a clinical perspective for someone who is high risk to know that information, Dr. Adalja says.

Still, getting that information within the needed timeframe may be challenging unless the diagnosis was via PCR and S-gene target failure occurred and that information was readily available to the provider, Dr. Russo says. Basically, there are a lot of ifs involved. Given that Omicron is now the dominant strain of COVID-19, Dr. Adalja says that resources are likely to shift to allow for more production of GSK's monoclonal antibody treatment, just to be safe.

Overall, doctors say its not vital for you or most other COVID-19 patients to know what strain of the virus you have. However, it doesnt hurt to ask.

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How to Know What Strain of COVID-19 You Have - Do Doctors Know What Variant You Have? - Prevention.com

Introduction

Breast cancer (BC) accounts for the highest incidence of tumors globally and is the leading cause of death of female cancer patients.1 In recent years, the application of precise surgery and adjuvant systemic treatments (chemotherapy, radiotherapy, endocrine therapy, and molecular targeting drugs) dramatically enhances the therapeutic effect. The prognosis of BC patients remains unsatisfactory, however. Screening and diagnosis are essential to reduce BC patients incidence rate and mortality rate in the early stage.2 Although clinical, pathological, and molecular features are commonly used to predict prognosis, the underlying pathogenesis of BC invasiveness is still poorly understood. Minimally invasive biomarkers to detect early BC and gauge treatment response are crucial in BC research.3

Tumor microenvironment (TME) plays a vital role in tumor progression and the overall efficacy of cancer treatments, such as immunotherapy, chemotherapy, and radiotherapy.4,5 The abundance of immune infiltrating cells in TME affects tumor initiation, progression, metastasis, and treatment resistance.6

Tissue type plasminogen activator (PLAT) and urokinase type plasminogen activator (PLAU), two essential proteins of the plasminplasminogen system, not only participated in the intravascular dissolution of blood clots but also played a significant role in tumor progression and metastasis.7 PLAU has been involved in different steps of cancer progression, such as tumorigenesis,8 angiogenesis,9 cell invasion, and metastasis.10 PLAT can affect the development of many tumors. BC patients with low PLAT levels often have a poor prognosis, while PLAT levels are higher in melanoma, neuroblastoma, acute myeloid leukemia, and pancreatic cancer.11,12

Our study evaluated the expression level of PLAT in BC and the relationships between PLAT expression and clinical pathological characteristics, together with prognosis, through publicly available data from The Cancer Genome Atlas (TCGA). PLAT protein expression was assessed with UALCAN databases. Moreover, the possible molecular functions of PLAT were screened by using GSEA software (version 4.1.0). Furthermore, the relationships between PLAT and immune infiltrating cells were explored using TIMER, TISIDB database, and ssGSEA algorithm. We investigated the correlation between PLAT expression level, clinicopathological characteristics, and DNA methylation with TCGA data. Our findings reveal the predictive value of PLAT in BC and show a potential correlation between PLAT and immune infiltrating cells, which will help clarify a possible mechanism for BC.

Transcriptional data of mRNA-sequencing, DNA methylation data, phenotype, and survival profiles of BC patients were accessed from UCSC Xena (https://xenabrowser.net/),13 which analyzes data from TCGA. The pre-processing steps were conducted by R (version 4.1.0) and Perl (version 5.30.2) software.

We selected 1071 primary BC samples, and 96 paired normal samples with clinicopathological profiles such as age, sex, menopausal status, histological type, TNM stage, and PAM50 subtype14 for further analysis. PAM50 algorithm using the 50-gene classifier divides BC into five intrinsic subtypes: luminal A, luminal B, HER2-enriched, basal-like, and normal-like. We explored the different expressions of PLAT in all clinicopathologic features. PLAT methylation data of 890 samples and its correlated gene expression levels were applied to calculate the methylation score of PLAT and the relationships between gene expression, DNA methylation, and clinicopathologic profiles.

Profiles of overall survival (OS) and progression-free survival (PFS) were downloaded from TCGA survival data. From this data, 1071 samples were assigned to high and low expression groups based on the median score of PLAT. R survival package was employed to generate KaplanMeier (KM) curves, while the optimal cut-off point for survival curves was generated through the res. cut function in the survminer package. Univariable and multivariable Cox regression models were established through the coxph function in the survival package, which was used to confirm whether PLAT is an independent prognostic factor of BC.

The clusterProfiler R package15 was employed to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the basis of differentially expressed genes (DEGs_ (| log2FC | 1, FDR < 0.05) between two different expressed groups. P-values were adjusted by the BH method. We used Spearman correlation coefficient analysis to show the relationship between PLAT and co-expression genes. GSEA was conducted based on KEGG (v7.4) gene-set collections using GSEA software (v4.1.0 6)16,17 to uncover pathways in the DEGs groups. Gene sets with NOM p-value < 0.05 were considered significant.

The gsva package18 was utilized to conduct the ssGSEA,19 which can be applied to evaluate the scores of immune infiltrating cells and calculate the activity of immune-related pathways. TIMER (https://cistrome.shinyapps.io/timer/)20 contains an abundant resource for systematically exploring the immune infiltrates between various tumors. We employed TIMER 2.0 gene module to calculate the relationships between PLAT mRNA expression and the infiltration of immune cells. TISIDB database (http://cis.hku.hk/TISIDB)21 is a portal web containing a variety of tumor and immune system resources. In this study, TISIDB providers the correlations between PLAT and the immune system.

Gene expression profiles, methylation degree, and clinical characteristics were compared through Wilcoxon signed rank tests and displayed by box plot. Survival rates were assessed by applying KM curves and the log rank test. The Pearson chi-square test was employed to analyze the correlation between PLAT expression and DNA methylation level. R version 4.1.0 was used in all tests conducted. A statistically significant difference was considered when p < 0.05.

We achieved PLAT expression and BC-related clinical profiles from the TCGA database. The detailed data are summarized in Supplementary Table 1. PLAT had a lower expression in BC tissues than in normal tissues both in TCGA-BRCA all samples (Figure 1A) and paired samples (Figure 1B). The CPTAC dataset of the UALCAN database was used to analyze the PLAT protein expression. PLAT in BC tissues was significantly lower than in normal tissues, which was consistent with the gene expression level (Figure 1C). After that, PLAT expression in BC with different clinical characteristics such as gender, menopausal status, histological type, molecular subtype, N stage, and T stage displayed statistical differences. The expression level of PLAT in women was lower than that in men (Figure 1D), and it was lower in postmenopausal women than premenopausal women (Figure 1E). Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two main histological types of BC. The expression level of PLAT in IDC was lower than in other types, including ILC. But there was no significant difference between ILC and other types (Figure 1F). Among the five PAM50 molecular subtypes of BC, luminal A, luminal B, HER2-enriched, basal-like, and normal-like, the expression level of PLAT was statistically different, in which luminal A was the highest and basal-like was the lowest (Figure 1G). The expression level was lower in the advanced stage in the T stage (Figure 1H); however, it was higher in the advanced N stage (Figure 1I).

Figure 1 Associations between PLAT expression and clinicalpathological parameters in BC. Low expression of PLAT was observed in tumor tissues both in all samples (A) and paired samples (B). Protein expression of PLAT in normal and primary tumor tissues with CPTAC samples (C). PLAT expression was analyzed in female and male (D), postmenopausal and premenopausal (E) patients, with different histological types (F), including invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and all other specific types, different PAM50 molecular subtypes (G), different tumor size (H) (T1 versus T2, T3, and T4), and different lymph node metastasis status (I) (N0 versus N1, N2, and N3). T1, tumor 20 mm in greatest dimension; T2, Tumor >20 mm but 50 mm in greatest dimension; T3, Tumor >50 mm in greatest dimension; T4, Tumor of any size with direct extension to the chest wall or the skin (ulceration or macroscopic nodules); invasion of the dermis alone does not qualify as T4; N0, no regional lymph node metastasis; N1, metastases in 1 to 3 axillary lymph nodes; N2, metastases in 4 to 9 axillary lymph nodes; and N3, metastases in 10 or more axillary lymph nodes. The asterisks represent the statistical p-value (ns: p > 0.05, *p 0.05, **p 0.01, ***p 0.001, ****p 0.0001).

We explored whether PLAT expression was correlated with the prognosis of BC patients. KM curves were utilized to evaluate the influence of PLAT expression on OS and DFS. The high PLAT expression group has a longer OS than low expression patients (Figure 2A) but there was no statistical significance in DFS (Figure 2B). We further investigated OS in the different molecular subtypes. Statistical significance of OS difference was found in all luminal samples, especially in luminal B subtypes (Figure 2C).

Figure 2 Prognostic value of PLAT expression in BC. (A) Low PLAT expression was associated with a poor OS in BC patients using KaplanMeier plotter. (B) DFS had no statistically significant difference in high and low PLAT expression groups. (C) Low PLAT expression was inferred as a poor OS in all luminal samples and all luminal B samples. (D) Forest plots show the association between PLAT expression and clinicopathological features using univariate and multivariate COX hazard analysis.

Univariate and multivariate regression analyses were applied to evaluate whether PLAT is an independent prognostic factor for BC. Univariate Cox hazard analysis identified that age (HR = 1.033, 95% CI:1.0201.046, p < 0.001), menopausal status (HR = 2.025, 95% CI:1.3473.045, p < 0.001), T stage (HR = 1.459, 95% CI:1.1971.779, p < 0.001), N stage (HR = 1.600, 95% CI:1.3471.901, p <0.001), M stage (HR = 4.860, 95% CI:2.9008.144, p < 0.001), Stage (HR = 2.187, 95% CI:1.7442.742, p < 0.001), and PLAT (HR = 0.886, 95% CI:0.7970.984, p = 0.024) were prognostic factors of BRCA. Multivariate analysis revealed that, after excluding other confounding factors, age (HR = 1.044, 95% CI:1.0231.067, p < 0.001), N stage (HR = 1.451, 95% CI:1.0522.000, p = 0.023), and PLAT (HR = 0.850, 95% CI:0.7420.974, p = 0.020) were still prognostic factors of BCRA (Figure 2D). The above results show that PLAT is an independent prognostic factor for BC and is a protective factor.

All TCGABRCA samples were assigned to two groups in the light of PLAT expression to gain insight into PLAT biological clues in BC. The volcano plot and heatmap show DEGs between the two groups (Figure 3A and B). PLAT and its co-expression genes are shown in Figure 3C. GO, and KEGG enrichment analyses were conducted based on 93 genes that had a positive correlation with PLAT to identify PLAT-related pathways and biological functions. The top 30 enriched GO terms are shown in Figure 3D. We can see that PLAT was mainly enriched in the pathway related to response to steroid hormone, humoral immune response, intracellular steroid hormone receptor signaling pathway, steroid hormone mediated signaling pathway, and response to progesterone. KEGG enrichment showed that PLAT was closely related to the pathways that correlate with BC and the immune response, such as the estrogen signaling pathway, apelin signaling pathway, IL-17 signaling pathway, and BC (Figure 3E).

Figure 3 DEGs and enrichment analyses of PLAT in BC. (A) Volcano plot and (B) heatmap show the DEGs in high and low PLAT expression patients. (C) Correlation of PLAT and the top 40 coexpressed genes. (D and E) GO enrichment and KEGG pathway analysis of PLAT in BC. The red box highlights the pathways correlated with BC and immune infiltrates. (F) The GSEA results showed that the terms antigen processing and presentation, natural killer cell mediated cytotoxicity, homologous recombination, and nucleotide excision repair were differentially enriched in BC samples with high PLAT.

We further used GSEA to predict PLAT-related pathways between patients with different PLAT expressions; 70 of 178 gene sets were up-regulated in the low PLAT group, and 15 pathways were enriched at NOM p < 0.05 and FDR < 0.25. The presenting figures include antigen processing and presentation, natural killer cell mediated cytotoxicity, homologous recombination, and nucleotide excision repair (Figure 3F). These results show that PLAT may affect immune cell infiltration.

Immune characteristics are closely related to tumor progression. Through the functional analyses, we evaluated the enrichment scores of 16 immune cells and the activity of 13 immune-related pathways between the two expressed groups by employing ssGSEA. The low expression subgroup of PLAT generally had higher levels of infiltration of immune cells, especially of aDCs, CD8 + T cells, DCs, iDCs, macrophages, pDCs, Tfh, Th1 cells, Th2 cells, TILs, and Treg cells. Except for the parainflammation pathway, the other 12 immune pathways performed higher activity in the low expression group than in the high expression group (Figure 4A). After that, we evaluated the relationship between the PLAT expression in BRCA and immune cell infiltration in TIMER. The finding revealed that the expression level of PLAT had a negative correlation with the infiltration of neutrophil (Rho = 0.175, p = 2.59e-08), T cell cd4+ (Rho = 0.158, p = 5.16e-07), B cell (Rho = 0.117, p = 2.12e-04) and dendritic cell (Rho = 0.098, p = 2.08e-03). Meanwhile it had a positive correlation with the infiltration level of T cell CD8+ (Rho = 0.2, p = 2.13e-10) and macrophage (Rho = 0.266, p = 1.45e-17). Not surprisingly, tumor purity was negatively correlated with PLAT expression level (Figure 4B). Then, to understand the association between PLAT and immune infiltration, TISIDB was employed to evaluate the association between PLAT expression and various immune characteristics. Figure 4C shows the relationship between PLAT and tumor-infiltrating lymphocytes (TILs) like CD4, CD8, DC, and B cell and Figure 4D the relationship between PLAT and immunostimulators like IL2RA, ICOS, TNFRSF13C, and PVR. Figure 4E shows the relationship between PLAT and immunoinhibitors like CD274, PDCD1, PDCD1LG2, and CTLA4 and Figure 4F the relationships between PLAT and MHC molecules like TAP2, HLA-DOB, TAP1, and HLA-F. Figure 4G shows the relationship between PLAT and chemokines like CXCL10, CCL8, CCL18, and CCL7. Figure 4H shows the relationship between PLAT and chemokine receptors like CCR8, CXCR6, CCR1, and CCR5. The results mentioned above indicate that PLAT widely participates in adjusting many immune molecules in BC and influences the immune infiltration in TME.

Figure 4 Associations of the PLAT expression level with tumor immune infiltration in BC. (A) Comparison of the enrichment scores of 16 immune cells and 13 immune-related pathways between low- and high-expression groups. (B) The correlation of PLAT expression with infiltration levels of neutrophil, CD4+T cell, B cell, and dendritic cell in BC is available on the TIMER 2.0 database. (C) Correlations between the abundance of tumor-infiltrating lymphocytes (TILs) and PLAT (plus the four TILs with the highest correlation) in the TISIDB database. (DF) Correlations between immunomodulators and PLAT (plus the four immunomodulators with the highest correlation, respectively) in the TISIDB database. (G and H) Correlations between chemokines (or receptors) and PLAT (plus the four chemokines (or receptors) with the highest correlation, respectively) in the TISIDB database. The asterisks represent the statistical p-value (*p 0.05, **p 0.01, ***p 0.001).

After the studies mentioned above, we continued to explore the possible reasons for the difference in PLAT expression. The box plot shows the methylation degree of each methylation site of PLAT DNA. Sites cg03960326 and cg18460120 had a higher degree of methylation and sites cg22038738 and cg04563438 had lower methylation degree (Figure 5A). Subsequently, the association between DNA methylation level and gene expression was calculated. Figure 5B shows that there is a negative association between DNA methylation and gene expression on the whole. When analyzing a single site, it was found that the methylation levels of cg13880167, cg12091331, cg04563438, cg03960326, cg00491021, and cg22038738 were negatively correlated with gene expression (Figure 5C). After that, survival analysis was further conducted according to the methylation degree of each site. It was found that, among the six DNA methylation sites of PLAT that had a negative correlation with gene expression, the methylation degree of the cg03960326 site was significantly related to OS (Figure 5D). We further assessed the methylation differences of the cg03960326 site of PLAT DNA in different clinical feature groups. We can see that there are no distinctions in methylation levels between different TNM and stages. However, there existed differences in methylation levels of cg03960326 site in people with different menopause status, tumor histological types, and PAM50 molecular types (Figure 5E). These differences are contrary to the previous differences of PLAT gene in cases with different clinical features, which also verifies the negative relationship between DNA methylation and gene expression.

Figure 5 Correlation of DNA methylation level with PLAT expression and clinical features. (A) Methylation level of 8 methylation site in PLAT. (B) Correlation of PLAT expression with gross methylation level. (C) Correlation of PLAT expression with different methylation sites (cg13880167, cg12091331, cg04563438, cg03960326, cg00491021, and cg22038738). (D) High methylation level of cg03960326 site was associated with a poor OS in BC patient. (E) Methylation level of cg03960326 site was analyzed with different menopausal status, histological types and PAM50 molecular subtypes. The asterisks represent the statistical p-value (ns: p > 0.05, **p 0.01, ***p 0.001, ****p 0.0001).

PLAT, a serine protease, mainly takes part in the intravascular dissolution of blood clots.22 Previous studies have shown that PLAT promoter hypermethylation can down-regulate the expression of nasal polyps gene, resulting in excessive fibrin deposition, which can be used as a therapeutic target of NP.23 PLAT can also promote angiogenesis by mobilizing CD11 + cells.24 It can be applied to the treatment of acute stroke25,26 and non-small cell lung cancer.27 It is also an estrogen-induced protein in the human BC cell line.11 BC is one of the highest incidence rate diseases in the world, which has a poor prognosis. Early detection and timely treatment are the most effective methods to improve the survival rate of BC patients. Traditional prognostic factors include tumor type, grade, size, and lymph node status. They can provide some course information, but they are not very accurate. Tumor immunotherapy provides a new remedy option for BC. Therefore, we assessed the role of PLAT in BRCA.

We analyzed BC cases in the context of RNA-seq data, methylation profiles, and clinical characteristics achieved from TCGA. The findings revealed that the expression level of PLAT was lower in BC patients. And patients in an advanced stage or with a more aggressive histological type may have lower expression levels in general. Consistent with that, lower expression of PLAT in TCGABRCA profiles was related to shorter overall survival, especially in the luminal B subtype. So, we deem that higher PLAT expression is related to a better prognosis of BC. After that, univariate and multivariate Cox regression analyses showed that PLAT was an independent prognostic factor affecting the prognosis of BC patients and could be used as a predictive marker.

The enrichment analysis showed that PLAT was related to the immune and tumor-associated pathway. Infiltrating immune cells are an essential component of TME.28 Recently, immunotherapy for the interaction between immune cells and BC cells has been developed as an alternative to classical anticancer therapy.29,30 PLAT has been affirmed to predict the prognosis of multiple tumor types such as colon cancer, ovarian cancer, lung cancer, and BC.31,32 There is evidence proving that tumor-infiltrating lymphocytes (TIL) that existed before the treatment of BC can predict treatment response and improve prognosis.33 TIL plays a vital role in chemotherapy response and clinical prognosis of all subtypes of BC.34 In our study, the low expression subgroup of PLAT generally had higher levels of infiltration of immune cells and higher activity of immune-related pathways. PLAT expressed level had a negative correlation with the infiltration level of neutrophil, T cell CD4+, dendritic cell, and B cell. Meanwhile, it had a positive correlation with the infiltration level of T cell CD8+ and macrophage. PLAT expression is closely related to TILs, immunomodulatory factors, and chemokines. Our results verified the association between PLAT and the prognosis and immune infiltration of BC, suggesting that PLAT can help predict treatment response and improve prognosis.

DNA methylation plays a vital role in gene-expressed regulation, mainly related to transcriptional inhibition.35 Therefore, we continue to search for the possible causes of different gene expressions. Our results showed a negative relationship between the degree of DNA methylation and gene expression level. By further analyzing the methylation level of PLAT DNA in the cg03960326 site, which is significantly related to OS, methylation differences were observed in patients with different tumor histological types, molecular subtypes, and menopause. These differences are opposite to the previous differences of the PLAT gene in other clinical feature groups. This also verified that DNA methylation was negatively related to PLAT expression, which might cause PLAT expression differences.

Immune infiltration in the TME has been shown to play a critical role in cancer progression and occurrence and to affect clinical outcomes of cancer patients.36 In triple-negative BC, tumor-associated macrophages derived from peripheral blood monocytes promote tumor growth and progression by several mechanisms that include the secretion of inhibitory cytokines, the reduction of effector functions of TIL, and the promotion of Treg cells. Besides, tumor-associated macrophages have been shown to directly and indirectly modulate PD-1/PD-L1 expression in tumor environment.37 Immunotherapy has raised significant concern in the treatment of cancer, which aims to eliminate cancer cells by enhancing natural defenses. The blockade of PD-1/ PD-L1 and CTLA4 has achieved remarkable therapeutic success in multiple kinds of cancer, including BC. An in-depth understanding of immune infiltration cells in the TME is essential to uncover the underlying molecular mechanisms and provide new strategies to improve immunotherapeutic efficacy.

Previous analysis showed that PLAT, a tumor suppressor gene, has differential expression levels and independent prognostic value and is significantly correlated with tumor-related pathways and immune infiltrating cells in TME. However, one study has shown that PLAT cannot foresee the response to systemic adjuvant therapy.11 The presented research only shows the results of online databases. To further confirm this conclusion, we will conduct PCR and IHC on our clinical samples and further analyze the different clinical characteristics and survival information. Finally, the internal molecular mechanism of PLAT and tumor immune cell infiltration will be further explored. Therefore, further research is needed to improve the proof of the biological impact of PLAT.

PLAT can be considered a prognostic factor and biomarker to provide personalized treatment, improve the survival rate, and contribute to developing immunological treatment strategies.

BC, breast cancer; TME, tumor microenvironment; PLAT, tissue-type plasminogen activator; PLAU, urokinase type plasminogen activator; TCGA, The Cancer Genome Atlas; OS, overall survival; PFS, progression-free survival; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; TIL, tumor-infiltrating lymphocytes.

This study was approved by the institutional ethical committee of the First Affiliated Hospital with Nanjing Medical University.

We would like to acknowledge the TCGA, TIMER, and TISIDB databases for free use.

This study is supported by the Natural Science Foundation of Jiangsu Province (BK20171506).

The authors report no conflicts of interest in this work.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209249. doi:10.3322/caac.21660

2. Mayor S. Screening for early breast cancer reduces invasive cancer, study finds. BMJ. 2015;351:h6576. doi:10.1136/bmj.h6576

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