Identification of novel SMN1 subtle mutations using an allelic-specific RT-PCR.

Spinal muscular atrophy (SMA) is caused by homozygous deletions of the SMN1 gene in approximately 95% of patients. The remaining 5% of patients with SMA retain at least one copy of the SMN1 gene carrying insertions, deletions, or point mutations. Although molecular genetic testing for most SMA patients is quite easy, diagnosing "nondeletion" SMA patients is still compromised by the presence of a highly homologous SMN2 gene. In this study, we analyzed the SMN1/SMN2 copy number by quantitative PCR and multiplex ligation-dependent probe amplification (MLPA). Further, common primers for both SMN1 and SMN2 sequences were used to screen DNA intragenic mutations. To confirm whether the identified mutations occurred in SMN1 or SMN2, we improved the traditional RT-PCR method by only amplifying SMN1 transcripts using an allelic-specific PCR (AS-RT-PCR) strategy. We identified six SMN1 point mutations and small indels in 8 families, which included c.683T>A, c.22dupA, c.815A>G, c.19delG, c.551_552insA and c.401_402delAG. To the best of our knowledge, the latter three have never been previously reported. The most common mutation in Chinese patients is c.22dupA, which was identified in three families. In this work, we demonstrated AS-RT-PCR to be reliable for identifying SMN1 subtle mutations, especially the prevalent mutation c.22dupA in Chinese SMA patients. By reviewing published papers and summarizing reported SMN1 mutations, a distinct ethnic specificity was found in SMA patients from China. Our research extends the SMN1 mutation spectrum.

Signaling protein signature predicts clinical outcome of non-small-cell lung cancer.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is characterized by abnormalities of numerous signaling proteins that play pivotal roles in cancer development and progression. Many of these proteins have been reported to be correlated with clinical outcomes of NSCLC. However, none of them could provide adequate accuracy of prognosis prediction in clinical application. METHODS: A total of 384 resected NSCLC specimens from two hospitals in Beijing (BJ) and Chongqing (CQ) were collected. Using immunohistochemistry (IHC) staining on stored formalin-fixed paraffin-embedded (FFPE) surgical samples, we examined the expression levels of 75 critical proteins on BJ samples. Random forest algorithm (RFA) and support vector machines (SVM) computation were applied to identify protein signatures on 2/3 randomly assigned BJ samples. The identified signatures were tested on the remaining BJ samples, and were further validated with CQ independent cohort. RESULTS: A 6-protein signature for adenocarcinoma (ADC) and a 5-protein signature for squamous cell carcinoma (SCC) were identified from training sets and tested in testing sets. In independent validation with CQ cohort, patients can also be divided into high- and low-risk groups with significantly different median overall survivals by Kaplan-Meier analysis, both in ADC (31 months vs. 87 months, HR 2.81; P <  0.001) and SCC patients (27 months vs. not reached, HR 9.97; P <  0.001). Cox regression analysis showed that both signatures are independent prognostic indicators and outperformed TNM staging (ADC: adjusted HR 3.07 vs. 2.43, SCC: adjusted HR 7.84 vs. 2.24). Particularly, we found that only the ADC patients in high-risk group significantly benefited from adjuvant chemotherapy (P = 0.018). CONCLUSIONS: Both ADC and SCC protein signatures could effectively stratify the prognosis of NSCLC patients, and may support patient selection for adjuvant chemotherapy.

Haplotype-based approach for noninvasive prenatal tests of Duchenne muscular dystrophy using cell-free fetal DNA in maternal plasma.

PURPOSE: This study demonstrates noninvasive prenatal testing (NIPT) for Duchenne muscular dystrophy (DMD) using a newly developed haplotype-based approach. METHODS: Eight families at risk for DMD were recruited for this study. Parental haplotypes were constructed using target-region sequencing data from the parents and the probands. Fetal haplotypes were constructed using a hidden Markov model through maternal plasma DNA sequencing. The presence of haplotypes linked to the maternal mutant alleles in males indicated affected fetuses. This method was further validated by comparing the inferred single-nucleotide polymorphism (SNP) genotypes to the direct sequencing results of fetal genomic DNA. Prenatal diagnosis was confirmed with amniocentesis, and those results were interpreted in a blinded fashion. RESULTS: The results showed an average accuracy of 99.98% for the total inferred maternal SNPs. With a mean depth of 30× achieved in the 10-Mb target region of each sample, the noninvasive results were consistent with those of the invasive procedure. CONCLUSION: This is the first report of NIPT for DMD and the first application of a haplotype-based approach in NIPT for X-linked diseases. With further improvements in accuracy, this haplotype-based strategy could be feasible for NIPT for DMD and even other X-linked single-gene disorders.

[Computational approaches to microRNA discovery].

microRNAs (miRNAs) are endogenous non-coding RNAs of ~21 nucleotides in length discovered in recent years. They are involved in diverse pathways and play an important role in gene regulation in plants and animals. There are two main groups of approaches to miRNA discovery, which are cDNA cloning and computational identification. Since some miRNAs are expressed at a low level and the expression of many miRNAs has spatio-temporal specificity, it is difficult to find them through cDNA cloning. However, computational approaches can predict the miRNAs specifically expressed or with low abundance, which is complement to cDNA cloning. Computational approaches have hence gained wide attention. In this review, the computational approaches to miRNA discovery were summarized. According to their intrinsic characteristics, computational approaches were categorized into five classes: (1) homology search; (2) prediction based on comparative genomics; (3) scoring candidates using the sequence and structure characteristics; (4) prediction combined with targets; and (5) prediction with machine learning. The principles of each class of the approaches and their advantages and limitations in miRNA discovery were discussed. Finally, the future direction in miRNA discovery was pointed out.