Rare and undiagnosed diseases tend to be diverse, misdiagnosed, and difficult to diagnose. In some cases, the disease is progressive and life-threatening. Yet, to date, an estimated 95% of rare diseases have no approved therapy. Therefore, rare and undiagnosed diseases are considered the ultimate challenges for understanding human diseases. Here, we review the research progress, research frontiers, and important scientific issues related to rare and undiagnosed diseases. We mainly focus on five topics: (1) the identification and functional analysis of disease-causing genes; (2) the construction of cells, organoids, and animal models for mechanism validation; (3) subtyping and diagnosis; (4) treatment and drug screening based on causative genes and mutations; and (5) new technologies and methods for studying rare and undiagnosed diseases. In this review, we briefly update and discuss the pathogenic mechanisms and precision medicine for rare and undiagnosed diseases.

The difficulty of converting scientific research findings into novel pharmacological treatments for rare and life-threatening diseases is enormous. Biomarkers related to multiple biological processes involved in cell growth, proliferation, and disease occurrence have been identified in recent years with the development of immunology, molecular biology, and genomics technologies. Biomarkers are capable of reflecting normal physiological processes, pathological processes, and the response to therapeutic intervention; as such, they play vital roles in disease diagnosis, prevention, drug response, and other aspects of biomedicine. The discovery of valuable biomarkers has become a focal point of current research. Numerous studies have identified molecular biomarkers based on the differential expression/concentration of molecules (e.g., genes/proteins) for disease state diagnosis, characterization, and treatment. Although technological breakthroughs in molecular analysis platforms have enabled the identification of a large number of candidate biomarkers for rare diseases, only a small number of these candidates have been properly validated for use in patient treatment. The traditional molecular biomarkers may lose vital information by ignoring molecular associations/interactions, and thus the concept of network biomarkers based on differential associations/correlations of molecule pairs has been established. This approach promises to be more stable and reliable in diagnosing disease states. Furthermore, the newly-emerged dynamic network biomarkers (DNBs) based on differential fluctuations/correlations of molecular groups are able to recognize pre-disease states or critical states instead of disease states, thereby achieving rare disease prediction or predictive/preventative medicine and providing deep insight into the dynamic characteristics of disease initiation and progression.

BACKGROUND: Developmental dysplasia of the hip (DDH) is a common childhood health complaint, whose etiology is multifactorial. The incidence of DDH is variable and higher in Tibet plateau. Here, we collected plasma samples and studied the metabolomics signatures of DDH.
METHODS: Fifty babies were enrolled: 25 with DDH and 25 age-matched non-DDH healthy controls (HC group). We collected plasma samples, laboratory parameters and conducted untargeted metabolomics profiling.
RESULTS: There are many differential metabolites among patients with DDH, including 4-β-hydroxymethyl-4-α-methyl-5-α-cholest-7-en-3-beta-ol, β-cryptoxanthin, α-tocopherol, taurocholic acid, glycocholic acid, 2-(3,4-dihydroxybenzoyloxy)-4,6-dihydroxybenzoate, arabinosylhypoxanthine, leucyl-hydroxyproline, hypoxanthine. The main differential metabolic pathways focused on primary bile acid biosynthesis, arginine and proline metabolism, phenylalanine metabolism, histidine metabolism, purine metabolism.
CONCLUSIONS: To our knowledge, this is the first report of metabolomics profile in babies with DHH. By combining the α-tocopherol and taurocholic acid, we could achieve the differential diagnosis of DDH.

Sarcomatoid renal cell carcinoma (SRCC), a manifestation of sarcomatoid dedifferentiation in renal cell carcinoma, is characterized by elevated invasiveness and a grim prognosis. Typically, SRCC patients present with advanced or metastatic conditions and survival rates rarely extend beyond one year. In this study, we describe a case of SRCC characterized by the patient exhibiting right flank pain without hematuria. Initially, imaging interpretations led to a diagnosis of severe hydronephrosis. Subsequently, an open right nephrectomy post-surgery confirmed the pathology of sarcomatoid renal cell carcinoma.

Epidemiological data on rare diseases in China are currently limited. The objective of this study was to provide a comprehensive understanding of the prevalence and incidence of rare diseases by systematically analyzing the available epidemiological data. We conducted a comprehensive search of English and Chinese databases, the Incidence and Prevalence Database, the Chinese Rare Disease Guideline, and the Taiwan Health Promotion Administration from 2010 to 2023. We identified the top diseases and regions based on epidemiological data and present the maximum, minimum, and median prevalence and incidence values in tables and forest plots. 1,264 prevalence and incidence data were retrieved from 277 studies, guidelines and official websites, covering 110 rare diseases (53.1%) and 32 regions (94.1%). In terms of geographical regions, incidence or prevalence data were available for 32 regions (94.1%), excluding Tibet Hui Autonomous Region and Macao Special Administrative Region. In terms of rate, 60 and 77 out of 207 diseases (29.0% and 37.2%) had available incidence and prevalence data, respectively. Eight diseases had an incidence rate equal to or greater than that of 1,000 patients per million. The present study provides a comprehensive epidemiological analysis and valuable insights into the prevalence and incidence of rare diseases in China. Our findings underscore the pressing need for sustained drug research and medical support for individuals and families impacted by rare diseases.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

ALK-positive Histiocytosis (ALK-HSs) is a recently identified rare clinical entity characterized by tissue histiocytic alterations associated with ALK gene rearrangement. Clinical presentations can be solitary, multifocal, or systemic (involving multiple sites and organs). Due to limited reported cases, there is inadequate understanding of this disease. This report presents a case of ALK-HSs in a 71-year-old male patient who presented with hematuria for one week. Imaging studies conducted at an external hospital showed multiple lesions in the penis, bilateral testes, back skin, and the third lumbar vertebra. Histopathological findings included spindle and histiocytic cell proliferation with mild or indistinct cellular atypia, interstitial infiltration of lymphocytes, plasma cells, foamy histiocytes, and fibrous tissue proliferation. Immunohistochemistry of the lesion cells revealed positivity for CD68, CD163, ALK1, ALK (D5F3), and Vimentin. FISH testing indicated ALK gene separation in the lesion cells. NGS testing identified the fusion genes KIF5B(NM_004521) and ALK(NM_004304) in the lesion cells. We combined the characteristics of this case with a review of the literature to enhance our understanding of this rare clinical entity.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average \"diagnostic odyssey\" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting.
METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values.
RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency.
CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

BACKGROUND: Peutz-Jeghers syndrome (PJS) is a rare hereditary neoplastic disorder mainly associated with serine/threonine kinase 11 (STK11/LKB1) gene mutations. Preimplantation genetic testing can protect a patient\'s offspring from mutated genes; however, some variations in this gene have been interpreted as variants of uncertain significance (VUS), which complicate reproductive decision-making in genetic counseling.
OBJECTIVE: To identify the pathogenicity of two missense variants and provide clinical guidance.
METHODS: Whole exome gene sequencing and Sanger sequencing were performed on the peripheral blood of patients with PJS treated at the Reproductive and Genetic Hospital of Citic-Xiangya. Software was employed to predict the protein structure, conservation, and pathogenicity of the two missense variation sites in patients with PJS. Additionally, plasmids were constructed and transfected into HeLa cells to observe cell growth. The differences in signal pathway expression between the variant group and the wild-type group were compared using western blot and immunohistochemistry. Statistical analysis was performed using one-way analysis of variance. P < 0.05 was considered statistically significant.
RESULTS: We identified two missense STK11 gene VUS [c.889A>G (p.Arg297Gly) and c.733C>T (p.Leu245Phe)] in 9 unrelated PJS families who were seeking reproductive assistance. The two missense VUS were located in the catalytic domain of serine/threonine kinase, which is a key structure of the liver kinase B1 (LKB1) protein. In vitro experiments showed that the phosphorylation levels of adenosine monophosphate-activated protein kinase (AMPK) at Thr172 and LKB1 at Ser428 were significantly higher in transfected variation-type cells than in wild-type cells. In addition, the two missense STK11 variants promoted the proliferation of HeLa cells. Subsequent immunohistochemical analysis showed that phosphorylated-AMPK (Thr172) expression was significantly lower in gastric, colonic, and uterine polyps from PJS patients with missense variations than in non-PJS patients. Our findings indicate that these two missense STK11 variants are likely pathogenic and inactivate the STK11 gene, causing it to lose its function of regulating downstream phosphorylated-AMPK (Thr172), which may lead to the development of PJS. The identification of the pathogenic mutations in these two clinically characterized PJS patients has been helpful in guiding them toward the most appropriate mode of pregnancy assistance.
CONCLUSIONS: These two missense variants can be interpreted as likely pathogenic variants that mediated the onset of PJS in the two patients. These findings not only offer insights for clinical decision-making, but also serve as a foundation for further research and reanalysis of missense VUS in rare diseases.

Genetic diseases are currently diagnosed by functional mutations. However, only some mutations are associated with disease. It is necessary to establish a quick prediction model for clinical screening. Pathogenic mutations in NGLY1 cause a rare autosomal recessive disease known as congenital disorder of deglycosylation (NGLY1-CDDG). Although NGLY1-CDDG can be diagnosed through gene sequencing, clinical relevance of a detected mutation in NGLY1 needs to be further confirmed. In this study, taken NGLY1-CDDG as an example, a comprehensive and practical predictive model for pathogenic mutations on NGLY1 through an NGLY1/Glycopeptide complex model was constructed, the binding sites of NGLY1 and glycopeptides were simulated, and an in vitro enzymatic assay system was established to facilitate quick clinical decisions for NGLY1-CDDG patients. The docking model covers 42 % of reported NGLY1-CDDG missense mutations (5/12). All reported mutations were subjected to in vitro enzymatic assay in which 18 mutations were dysfunctional (18/30). In addition, a full spectrum of functional R328 mutations was assayed and 11 mutations were dysfunctional (11/19). In this study, a model of NGLY1 and glycopeptides was built for potential functional mutations in NGLY1. In addition, the effect of potential regulatory compounds, including N-acetyl-l-cysteine and dithiothreitol, on NGLY1 was examined. The established in vitro assay may serve as a standard protocol to facilitate rapid diagnosis of all mutations in NGLY1-CDDG. This method could also be applied as a comprehensive and practical predictive model for the other rare genetic diseases.