Monoclonal gammopathy of clinical significance (MGCS) is a recently recognized clinical-pathological entity. Symptoms are caused by the presence of a monoclonal protein leading to high comorbidity. The affected organs vary according to the target antigen However, as most of the knowledge relies on case reports or short series; there is a lack of consensus regarding treatment approach. Here, we discuss MGCS other than renal . We provide insights into the pathophysiology, diagnosis, treatment, and follow-up based on clinical cases. Finally, we discuss future directions in this field, such as potential novel therapeutic targets and prognosis of patients with MGCS. Monoclonal gammopathy of undetermined significance (MGUS) is defined as the presence of a monoclonal protein (M-protein) produced by a small amount of plasma cells. The majority of patients remain asymptomatic; however, a fraction of them develop clinical manifestations related to the monoclonal gammopathy despite not fulfilling criteria of multiple myeloma or other lymphoproliferative disorder. These patients constitute an emerging clinical issue coined as monoclonal gammopathy of clinical significance (MGCS). The mechanisms involved are poorly understood, and literature is scarce regarding management. The clinical spectrum involves symptoms related to renal, neurologic, skin, ocular, or bleeding manifestations, requiring a multidisciplinary approach. Treatment strategies rely on the basis of symptomatic disease and the M-protein isotype. In this review, we focus on MGCS other than renal, as the latter was earliest recognized and better known. We review the literature and discuss management from diagnosis to treatment based on illustrative cases from daily practice.  Monoclonal gammopathy of undetermined significance (MGUS) is defined by the presence of a monoclonal protein (M-protein) produced by a small B-cell/plasma cell clone in persons without features of symptomatic disease related to malignant disorders, such as multiple myeloma (MM), Waldenström macroglobulinemia (WM), AL amyloidosis, or other lymphoproliferative disorder ,2. PrevaDespite the above mentioned, the majority of these patients will not progress to overt MM or other lymphoproliferative neoplasm and die from unrelated disorders. Apart from the potential malignant evolution, in some instances, the presence of a small B-cell/plasma cell clone producing a monoclonal immunoglobulin may cause a wide variety of clinical manifestations, leading to a significant comorbidity and need for treatment ,9. GivenIn many instances, the mechanisms underlying MGCS are unclear. Given the diversity of clinical manifestations, they are divided by organ involvement to better understand the pathophysiology, diagnosis, and treatment. In this sense and because of the high comorbidity and relative frequency, the most affected and well-studied organ is the kidney. So far, kidney disease related to the presence of an M-protein was already categorized as monoclonal gammopathy of renal significance (MGRS) ,12,13.Other MGCS are those involving the eyes, skin, peripheral nerves, and coagulation system, among others. Clinical manifestations may overlap with other unrelated M-protein diseases, making them hard to diagnose and to make related treatment decisions . As MGRSThe mechanisms by which the presence of the M-protein results in clinical syndromes not related to progression to a plasma cell or lymphoproliferative neoplasms can be divided according to findings on histopathology. Among these, the M-protein deposition is a common feature of many MGCS. As suggested by Fermand et al., the abnormal biochemical conformation of the monoclonal immunoglobulin results in its deposition that can be classified whether they have organized or non-organized ultrastructural appearance . For insThe monoclonal immunoglobulin may also interact with several self-antigens, causing disease. The autoantibody effect of the M-protein can facilitate an autoimmune response depending on the target self-antigen. This process is seen in IgM-peripheral neuropathy, as the IgM binds directly to gangliosides or myelin glycoproteins (MAG). A relevant epitope in anti-MAG neuropathy is the HNK-1  that is located in the peripheral nervous system. The presence of an autoantibody blocks the physiologic signaling and regulatory processes of MAG resulting in the clinical manifestations ,21,22. IL265P/CXCR4wtMYD88 and the identification of the VH4-34 segment in the IGH loci were more prevalent when compared to IgM MGUS and WM, giving more insight in the clonal origin of the disease [Detecting molecular patterns of disease using high-throughput technologies may raise more solid basis on understanding better MGCS as a different clinical-pathological entity. For instance, sequencing Schnitzler syndrome has revealed a unique upregulation of the inflammasome pathway . In the  disease . BesidesFrom the clinical perspective, MGCS can be categorized regarding the involved organ. This practical approach resembles what is seen at the clinic. Although some of them share the same involved organs , the MGCS list includes the most important diseases with the cardinal involved organ  .Cryoglobulinemia can damage any organ, but the skin is usually the most frequent location. Type 1 cryoglobulinemia is caused by plasma cell or lymphoproliferative disorders, and it is mainly due to IgM or IgG M-protein . ClinicaTreatment depends on the severity of symptoms and the underlaying cause. Besides WM-associated cryoglobulinemia that has international consensus , there iIn patients with MM, combination of proteasome inhibitors and immunomodulatory drugs can achieve good responses before autologous stem cell transplant (ASCT). In a report of 46 patients with an underlying IgG M-protein, most of them responded well to the cryoglobulinemia symptoms whether using bortezomib, alkylating agents, immunomodulatory drugs, or high-dose melphalan. With these data, type 1 cryoglobulinemia patients had 5- and 10-year estimated survival rates of 83 and 68%, respectively .Clinical case 1: A 63-year-old male was admitted because of a 12-month history of skin lesions in the legs and both feet. At that time, blood and basic biochemistry lab tests did not show any abnormality. Autoimmunity and viral serologies in serum were all negative. He was prescribed oral antibiotics because of the suspicion of an infectious disease. However, the skin lesions progressed to painful ulcers and extension to both feet. The skin biopsy showed thrombosis in small vessels. Given a suspicion of an autoimmune disorder, the patient was started on oral corticosteroids with no improvement. Because of the progression of the skin lesions, the patient was referred to a tertiary hospital, where screening tests showed a biclonal M-protein (IgG-kappa and IgA-lambda) by serum immunofixation. Serum cryoglobulins were positive for type 1 cryoglobulinemia. The bone marrow aspirate showed 2% of plasma cell infiltration by optical microscopy morphology , and whole-body CT scan showed osteolytic lesions in right humerus and the skull. In this scenario, the patient was diagnosed with type 1 cryoglobulinemia related to MM and started induction treatment with bortezomib, thalidomide, and dexamethasone followed by ASCT, achieving hematologic complete remission. The skin lesions improved substantially through each cycle of treatment until complete resolution. Two years later, he relapsed in form of symptomatic cryoglobulinemia and bone lesions. He was started on lenalidomide and dexamethasone with no response. Then, ixazomib, lenalidomide, and dexamethasone were considered, but the skin condition did not respond. The third line of treatment was pomalidomide and dexamethasone, but progression was otherwise seen, and the skin ulcers on the leg were severely affected A. The foSchnitzler syndrome is an autoinflammatory disease with an IgM M-protein (rarely IgG) that presents in form of chronic urticaria. According to Strasbourg criteria, major criteria include chronic urticaria rash and IgM or IgG M-protein. Minor criteria are recurrent fever, leukocytosis and/or elevated C-reactive protein (CRP), neutrophilic dermal infiltrate on skin biopsy, and abnormal bone remodeling that may lead to bone pain or arthralgias . To diagMYD88 L265P mutation was positive by allele-specific polymerase chain reaction (AS-PCR). The patient was diagnosed with Schnitzler syndrome related to IgM MGUS and was started on 0.5 mg/kg/d oral prednisone. After 2 months of treatment and tapering the prednisone dose, the patient achieved good response of the skin lesions. Six years later, the patient is still on low-dose prednisone (7.5 mg daily) because when discontinued, he complained again of mild fever and urticaria. During the 6-year follow-up, the M-protein has had an evolving pattern up to 12 g/L, but there are no signs of progression to a lymphoproliferative disorder or further extra-hematological activity.Clinical case 2: A 78-year-old male was referred to our center because of repeated episodes of urticaria on the trunk in the last year. He also complained of fever and arthralgia in each episode. At physical examination, he had pruriginous skin papules on the back and legs . Lab tesClinical case 3: A 43-year-old female was referred to our center because of chronic urticaria, fever, and arthralgia not responding to symptomatic treatment. Lab tests showed mild leukocytosis  and positive serum immunofixation for monoclonal IgG-kappa. Serum electrophoresis showed 10.7 g/L of M-protein. The bone marrow contained 4% plasma cells, most of them with abnormal immunophenotype. Skeletal survey did not show bone lytic lesions. She was diagnosed with probable Schnitzler syndrome related to an IgG-kappa monoclonal gammopathy and started treatment with oral corticosteroids and methotrexate without response. Hydroxychloroquine and anakinra were also tried, but the patient did not show any improvement after two months and developed intolerance to anakinra. In this scenario, treatment against the plasma cell clone with bortezomib was prescribed. After three cycles, she achieved complete response of the skin lesions and a serological very good partial response (VGPR) of the monoclonal gammopathy. She underwent ASCT and achieved complete serological response. After 6 years, she is still in complete remission without recurrence of the Schnitzler syndrome.This entity consists of neutrophilic dermatoses that cause an ulcerative skin condition. It usually starts as a single or multiple ulcerative nodules with violaceous borders in any area of the body . Most caClinical case 4: A 60-year-old male had been suffering from episodic, painful skin ulcers on both legs after minimal accidental trauma. A skin biopsy was consistent with pyoderma gangrenosum. Serum immunofixation and electrophoresis showed a monoclonal IgA-kappa of 8 g/L without any other myeloma-related features . The screening for systemic inflammatory diseases was also negative. After an initial approach with oral corticosteroids, the patient did not respond. Cyclosporin, tacrolimus, and mycophenolate were tried without disease control and developing renal toxicity. Finally, infliximab achieved good response of the ulcers. After 6 years of treatment for the pyoderma gangrenosum, the patient relapsed with ulcers on the left ankle  and compScleromyxedema is a generalized sclerodermoid form of lichen myxedematosus, also known as a type of dermato-neuro syndrome (DNS), typically associated with the presence of an IgG M-protein. Systemic involvement includes cardiomyopathy, pulmonary fibrosis, joint contractures, and reduced esophageal motility . TranscrClinical case 5: An 80-year-old male was referred because of sclerodermoid lesions on the upper and lower extremities causing rigidity limiting physical activity , dysphagAcquired cutis laxa is a rare skin condition that is associated with prior inflammatory diseases that results in elastolysis ,42. HoweClinical case 6: A 52-year-old male was referred because of progressive skin changes in the last 2 years in the form of inelastic skin on body fold areas , with effective remission of symptoms. Anti-myeloma agents should be used only in refractory disease. Non-severe scleromyxedema treatment with IVIG can be considered. For refractory or severe manifestations, addition with anti-myeloma agents can achieve hematological and clinical response. Few experiences regarding pyoderma gangrenosum and cutis laxa are reported. For the first, topical or oral corticosteroids can help, although infliximab has shown good response rates. Treatment of acquired cutis laxa is based on the underlying monoclonal gammopathy  deficiency due to the adsorption of FX by amyloid fibrils that decreases its half-life time causing bleeding complications . AcquirePlatelet aggregation disorders in monoclonal gammopathies have been associated to the presence of a serum M-protein. It has been postulated that the paraprotein binds to platelet receptors involved in aggregation. This leads to prolonged bleeding time and, in some patients, causes unexplained mucocutaneous bleeding or bruising or in others can cause severe bleeding, resulting in hematuria or large hematomas ,53.Clinical case 7: A 38-year-old male without prior medical history was admitted because of severe macroscopic hematuria and clots, causing acute kidney injury. During the admission, imaging studies revealed multiple clots along the urinary tract with no other relevant findings. Coagulation tests and platelets count were normal. Serum immunofixation was positive for IgG-lambda of 15.7 g/L. Urine immunofixation was negative, and the 24-hour urine protein excretion did not show proteinuria. The fat biopsy was negative for Congo red staining. The bone marrow showed 11% of plasma cells. It was considered to perform a kidney biopsy but was otherwise normal, and no complement or immunoglobulin deposits were seen in the immunofluorescence. In this scenario, the patient was diagnosed with unknown severe hematuria and a concomitant IgG-lambda smoldering myeloma. The patient was kept under supportive treatment, showing complete resolution of the episode. He was referred to the hematology and nephrology outpatient clinics for follow-up. One and a half year later, the patient was admitted because of recurrent huge iliac psoas hematoma with no previous traumatic injury. The episodes resolved spontaneously, but more tests were performed. The platelet aggregometry assay showed an absence of response to ADP and a decreased liberation with agonists. These results were consistent with a platelet aggregation disorder related to the IgG-lambda M-protein. The patient was started on four cycles of cyclophosphamide, bortezomib, and dexamethasone followed by ASCT. He achieved serological VGPR (IgG-lambda only detectable by immunofixation) with no recurrence of the bleeding symptoms. Four years later, the patient presented again with every transient episode of hematuria and small hematoma in the pelvic area with spontaneous resolution. Serum IgG-lambda M-protein increased up to 12 g/L and lambda serum free light chain of 36 mg/L. He was diagnosed with relapse of the M-protein bleeding disorder. He started treatment again with four cycles of cyclophosphamide, bortezomib, and dexamethasone followed by a second ASCT. He achieved serological VGPR with a stable IgG-lambda M-protein lower than 2 g/L. He is completely asymptomatic now, two years beyond the second ASCT.Treatment summary recommendation of M-protein related bleeding disorders. Whether the bleeding disorder is caused by an acquired von Willebrand syndrome or a platelet aggregation disorder, supportive treatment with coagulation factors is mandatory in case of life-threatening bleeding or given as prophylaxis before procedures. Definitive treatment is against the underlying monoclonal gammopathy, as it can reverse the hemostatic abnormalities. A benefit-to-risk approach should be made in patients with MGUS. However, as the disease consists of bleeding and is potentially life-threatening, anti-myeloma therapy is recommended  during routine work-up tests. She was kept under follow-up at the hematology outpatient clinic. Eight years later, the patient complained of mild photophobia and ocular pain. The ocular examination revealed corneal deposits in both eyes; visual acuity was otherwise normal. The corneal biopsy demonstrated kappa free light chain deposits by immunohistochemistry. No extracorneal involvement was detected. At that time, serum kappa free light chain increased up to 174 mg/L, and tests revealed a small amount of urine M-protein of 279 mg/24 h. The serum M-protein was 27.6 g/L. In this context, the patient was diagnosed with LCDD with mild corneal involvement. As visual acuity was normal, with only peripheral corneal involvement, local treatment was indicated, and she was kept under observation. Three years later, the photophobia increased with ocular pain. At that time, she had a serum M-protein of 22 g/L, serum kappa free light chain increased up to 238 mg/L, serum involved/uninvolved free light chain was 35.7, and the bone marrow aspirate had 4% of plasma cells. Skeletal survey did not show lytic lesions, and fat biopsy was negative for amyloid. Ophthalmological examination revealed increased corneal deposits. She was started on bortezomib and dexamethasone for four cycles and underwent ASCT conditioned with high-dose melphalan. She achieved stringent complete response with negative minimal residual disease and resolution of ocular symptoms with no progression of corneal deposits on the following visits. Treatment summary recommendation of ocular-related disease. Patients without significant symptoms should be followed with a watch and wait strategy. However, when symptoms worsen with a risk of visual loss, the need of treatment is mandatory. As in other types of LCDD, treatment with anti-myeloma agents can achieve clinical and hematologic responses with long-lasting remissions . Thus, anti-MAG peripheral neuropathy accounts for about 50% of IgM peripheral neuropathy. This syndrome is usually seen in patients older than 60 years old, with insidious onset and with progressive significant disability. Serum ELISA can show high titers of anti-MAG with a good specificity, but titers are not linked to severity. Electrophysiological studies demonstrate a distinctive pattern with slow conduction and prolonged distal motor and sensory latencies ,57. RituMore rarely, some patients can show anti-ganglioside (GM1) antibodies, since motor neuropathy is the main clinical feature. When no specific autoantibodies can be found on screening tests, IgM MGUS peripheral neuropathy usually presents as a painless chronic distal neuropathy with sensory symptoms and, in some cases, tremor or ataxia. Electrophysiological studies show a demyelization pattern ,66. As sMYD88 L265P mutation was negative by AS-PCR. In this context, the patient was diagnosed with anti-MAG peripheral neuropathy related to the IgM MGUS. Given the significant disability, the patients started treatment with four cycles of rituximab 375 mg/m2 weekly. Two months later, the patient had only mild distal sensory symptoms. During the 3-year follow-up, the disease was stabilized with no progression.Clinical case 9: A 72-year-old male was referred because worsening of chronic distal symmetrical dysesthesias over the last year. Neurological examination and electrophysiological studies showed findings consistent with a peripheral demyelinating polyneuropathy. Lab tests showed the presence of a serum monoclonal IgM-kappa of 3 g/L without any other abnormality. Anti-MAG antibodies by Dot-Blot were positive, while testing for anti-gangliosides antibodies was negative. Bone marrow aspirate had 10% of normal lymphocytes by morphology. Immunophenotypic analysis showed mature B lymphocytes without kappa or lambda restriction. Treatment summary recommendation of neurologic-related disease. Single-agent rituximab is the first option for patients with anti-MAG IgM peripheral neuropathy or anti-ganglioside antibodies, with ibrutinib being the most promising option in refractory patients. For IgM MGUS peripheral neuropathy without autoantibodies, immunosuppressive treatment may be the first option, while PE, rituximab, immunochemotherapy, or ibrutinib can be considered for unresponsive patients (patients .Future directions need to be focused on two points. The first one is related to the pathophysiology of the disease: whether there are immune or molecular pathways underlying MGCS that are different from other MGUS and could be related to the clinical features observed. The description of these mechanisms can elucidate new targets and drugs for specific treatment in these diseases. In regard with the latter, a study reported that Schnitzler syndrome has a characteristic activation of the inflammasome compared to healthy controls and raises a question about a distinct mechanism in patients with monoclonal gammopathies . AnotherThe second point that remains to be elucidated is how to predict which patients might develop MGCS. We already know that comorbidities not related to progression to symptomatic disease are higher in patients with MGUS ,71. We aMGCS is a newly emergent concept. Screening for an underlying malignancy, such as MM, WM, AL amyloidosis, or other lymphoproliferative disorders, is mandatory. Treatment is based on the presence of symptoms, particularly if they cause disability. When the diagnosis is established, a risk to benefit approach is the first step. Many of these MGCS are diagnosed in the setting of an already established disease. The next approach should be to assess the M-protein isotype involved, as non-IgM-related diseases are treated with anti-myeloma agents, while anti-CD20-based regimens are the preferred option for IgM-related diseases. Although not enough data are available, this review summarizes the treatment possibilities for MGCS  and give


LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases.In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively, and it calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. We obtain gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix, and we obtain disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs.Compared with lncRNA-disease prediction methods, our proposed method takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied the method to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.The online version contains supplementary material available at 10.1186/s12859-021-04256-8. Long non-coding RNAs (lncRNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides , 2. ManyThe experimentally verified information about disease-related lncRNA is gradually increasing. A large number of databases have been published. The database LncRNADisease contains 3000 lncRNA-disease associations . The datThe disease-related lncRNAs prediction methods can be categorized into network-based approaches and machine learning-based approaches. Biological system is a highly complex heterogenous network involving different molecules. Network-based approaches use multiple features including (but not limited to) lncRNA functional similarity, lncRNA-gene association, gene–gene interaction, gene-disease association, and molecular similarity to construct lncRNA similarity networks, or lncRNA-disease heterogeneous networks, then use network model analysis methods  to predict potential lncRNA-disease associations . RWRlncDRecently, many bioinformatics calculation models based on machine learning algorithms have been proposed to find potential lncRNA-disease associations. Lu et al. used inductive matrix completion and principal component analysis to predict potential lncRNA-disease associations . Based oMeanwhile, modern medical research proves that the alternations of biological factors  may directly or indirectly affect diseases. Earlier studies have shown that RNA–protein interactions regulate gene expression by controlling various post-transcriptional processes. LncRNAs regulate the RNA–protein interactions by recruiting regulatory complexes , 31, andInspired by currently well-performing neural network technologies , 37, we matrix and the gene-disease association matrix. The disease similarity matrix is obtained by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates neighborhood information by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. The stability test results show that LGDLDA is more robust and the simulation data experiments show that LGDLDA performs better than four state-of-art methods in predicting lncRNA-disease association. LGDLDA can effectively predict potential cancer-related lncRNAs and provide more candidates for biological experimental verification. Most of predicted cancer-related lncRNAs are supported by recent literatures.In the results section, the work we do is described as follows: Firstly, we used randomly split samples to observe the robustness of each method. Secondly, we compared LGDLDA with four famous lncRNA-disease association prediction methods on a small lncRNA-disease association simulation network. Four state-of-art methods include NCPLDA , IDHI-MIBefore comparing the performance of LGDLDA with four famous lncRNA-disease association prediction methods in small data, we need to evaluate the stability of these methods. We generally randomly divide the data set into two parts: Ω1 and Ω2. In the first step, based on the training set Ω1, we select different parameters and determine the parameter configuration with good performance. In the second step, we expect that the selected parameter configuration can have an accurate prediction in Ω2. We performed this experiment on a small lncRNA-disease association simulation network which contains 356 lncRNAs, 354 diseases, 132 genes, 736 known lncRNA-gene associations, 462 gene-disease associations and 2169 known lncRNA-disease association instances . Ω1 contTo address the two issues, we observed the performance of the method in two experiments. In the first experiment, we performed 10 random splits on a certain comprehensive data set. For each randomly divided data set, we ran LGDLDA on the data set and calculated AUC values. The AUC values for 10 realizations are shown in Fig. In this section, we compared LGDLDA with four famous methods  on a small lncRNA-disease association simulation network which contains 356 lncRNAs, 354 diseases, 132 genes, 736 known lncRNA-gene associations, 462 gene-disease associations and 2169 known lncRNA-disease associations from breast cancer . LncDisATo avoid the small lncRNA-disease association simulation network favoring our own model, we run each method on data that does not contain gene-related information . Figure In order to observe whether it is necessary to include each omics data, we performed the experiment on the dataset with missing part of the omics data and recorded the AUC values, and compared with the experimental results on the complete multi-omics dataset. The experimental results are shown in Additional file In this section, we applied LGDLDA to real cancer data including gastric cancer, colorectal cancer, and breast cancer. For a given disease, all known related lncRNAs are true labels, and other lncRNAs are candidates for disease. Inspired by the work of Guo et al. , we usedGastric cancer is the second most common cancer in the world , 47. AccBreast cancer is the most common malignant tumor in women and the second leading cause of cancer death , 55. If Prostate cancer is the second most common cancer in men and the fifth leading cause of death worldwide , 60. RecIn case studies, we have found many potential cancer-related lncRNAs. Most of potential association lncRNAs are supported by recent literatures. In future biological experiments, it would be interesting to find the association mechanisms between new potential lncRNAs and diseases.As shown in Fig. It should be noted that the method LGDLDA is the worst one when focusing only on top genes (FRP < 0.05 or in a lesser extent FPR < 0.1). Maybe, this is not the best method when focusing on “top prediction”. We believe that this is because the dataset is too small and affects the performance of the method. We propose two ideas to improve the performance of LGDLDA. The first idea, we use warm start strategy. We apply LGDLDA to similar training datasets to obtain a good performance parameter set β, then further optimize the parameter set β on the training set to improve the performance of LGDLDA. The second idea, we use stability selection strategy. We run LGDLDA multiple times to obtain multiple results, then use the stability selection strategy to average these results to remove the risk of overfitting caused by small datasets.Finally, the real association mechanism between lncRNAs and disease is much more complicated than what we assumed. For example, the relationship between lncRNAs and complex diseases will change over time. We will try to design a new machine learning framework to analyze association data and time dynamic data simultaneously.In this article, we proposed a novel machine learning framework, namely LGDLDA, to find cancer-related lncRNAs by integrating analysis of multi-omics data. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively. LGDLDA calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. LGDLDA obtains gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix. LGDLDA obtains disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. LGDLDA incorporates the prior knowledge of biological network topology including lncRNA similarity networks, lncRNA-gene association network, gene-disease association network, disease semantic similarity networks, and lncRNA-disease association network. In this framework, a deep learning model was used to generate feature matrices. In model optimization, the final optimization problem is a popular matrix completion problem, which can be solved using convex optimization methods. In summary, the method considers more critical information and obtains the performance improvement cancer-related lncRNA predictions.In this section, we will introduce the main steps in the LGDLDA method. (1) LGDLDA uses multiple association similarity matrices  to build lncRNA-gene-disease association network. (2) Based on the matrices generated in the first step, LGDLDA uses the association similarity matrices combined with neural network to calculate the neighborhood information of lncRNAs and diseases, and further embeds it into the low-dimensional spatial node representations. (3) Inspired by the reconstruction matrix algorithm in NNHLDA , LGDLDA aij in A, the value of aij is equal to 1 if disease j related to lncRNA i; otherwise, aij is equal to 0. Let k represents the number of genes.In this paragraph, we will introduce the mathematical formulas used next. For calculating the functional similarity networks of lncRNAs, LGDLDA uses the lncRNA expression profile matrix, lncRNA-protein function association matrix and lncRNA-miRNA association matrix. For calculating the disease similarity network, LGDLDA uses disease information, protein-disease association matrix and miRNA-disease association matrix. All lncRNAs and diseases are annotated with standard corresponding IDs.Following the work of Zhang et al. on data collection , LGDLDA Since the Pearson correlation coefficient is easily affected by outliers, and outliers are inevitably included in the data, we used the biweight midcorrelation (BM) coefficient , 71. ComThe radial basis function (RBF) Gaussian kernel function was applied to lncRNA-miRNA interactions to obtain Gaussian interaction profile kernel similarity , and conGIPlm(li) represents the lncRNA-miRNA interaction profile, GIPlm(li) is a binary vector in which 1 represents presence of interactions between lncRNA li and miRNA and 0 represents absence, αl is the weight factor used to regulate the kernel bandwidth, the parameter Nl denotes the total number of lncRNAs.where GIPlp(li) represents the lncRNA-protein interaction profile, GIPlp(li) is a binary vector. With the help of the method described above, we constructed the similarity network LncSm3.Analogous to lncRNA-miRNA interactions-based Gaussian similarity calculation method, the lncRNA-protein interactions-based Gaussian similarity of lncRNA pairs is calculated by the same method. di and dj, and then construct a weighted disease similarity correlation network DisSm2.GIPdm(di) denotes disease-miRNA interaction profile, GIPdm(di) is a binary vector.We first used the R package “DOSE” to compute the correlation coefficients between diseases , 74. TheLTS represents the lncRNA similarity network LncTSN, and DTS represents the disease similarity network DisTSN.In order to overcome the loss of information caused by the fusion of similarity networks , the idea of network diffusion is employed to generate the topological similarity networks. Motivated by the work of Zhang et al. , the RWRlncRNAi, diseasei and geei, respectively. The initial representations of lncRNA, disease and gene nodes  to accurately represent the features of lncRNA. The aggregation can be defined as follows:W and b denotes the parameters in the neural networks. The nodes are embedded in low-dimensional vectors and normalized:The neural network obtains more powerful feature expression capability by using nonlinear activation functions. Motivated by the work of Zeng et al. , the actEET is used to enforce symmetry of the recovery.In machine learning, the model contains many parameters, and we need to use training data to determine the optimal values of the parameters through training optimization. The optimization goal is to make the difference between the predicted value and the target value  as small as possible. The information loss function between the reconstructed matrix and the original information matrix can be defined as follows:Since the functions in the method are all differentiable, we can use the gradient descent method to iteratively solve step by step to obtain the minimize loss function and model parameter values. LGDLDA uses the gradient descent method to train the model parameters. After training, elements in the reconstruction matrix can predict each associations score. The higher a score is, the larger probability we suggest the potential association exists:In this sense, the final optimization problem is a popular matrix completion problem, which can be solved using convex optimization methods.di, each known disease-related lncRNA is left out as test sample, meanwhile other disease-related lncRNAs are used as training samples. All irrelevant lncRNAs constitute candidate samples. The test samples are positive samples, and other samples are negative samples. In the predicted association matrix, LGDLDA regards elements larger than the threshold as effective associations between lncRNAs and diseases. We used true positive rate (TPR) and false positive rate (FPR) to calculate area under the curve (AUC).To be able to fairly evaluate the performance of the methods, we performed LOOCV  on the verified lncRNA-disease association data. Given a disease Additional file 1: Figure S1. The data processing procedure for disease-lncRNA association instances. Figure S2. The AUC values for 10 realizations on the dataset with 10% incorrect data. Figure S3. The box plots from 50 random splits experiment on a dataset with 10% incorrect data. Table S1. The experimental results on a dataset lacking some omics data. Table S2. The supporting literature of Top 15 gastric cancer-associated LncRNAs predicted by LGDLDA. Table S3. The confirmed databases of Top 15 breast cancer-associated LncRNAs predicted by LGDLDA. Table S4. The supporting literature of Top 15 breast cancer-associated LncRNAs predicted by LGDLDA. Table S5. The confirmed databases of Top 15 prostate cancer-associated LncRNAs predicted by LGDLDA. Table S6. The supporting literature of Top 15 prostate cancer-associated LncRNAs predicted by LGDLDA. Table S7. Summary of data sets used by each matrix.


Populations following a proactive regimen had better outcomes than those following a reactive treatment regimen. Mean BCVA change was greater in younger populations, with lower baseline BCVA and treated with a higher number of injections (p < 0.001). Our results confirm that anti-VEGFs may produce a significant functional improvement at 12 months in patients with nAMD.The use of anti-vascular endothelial growth factor (VEGF) agents has profoundly changed the prognosis of neovascular age-related macular degeneration (nAMD). As clinical experiences have accumulated, it has become mandatory to summarize data to give information that can be useful in everyday practice. We conducted a systematic review to identify randomized controlled trials (RCTs) and observational studies that reported 12-month changes in best-corrected visual acuity (BCVA) in patients with nAMD on anti-VEGF monotherapy. Data were analyzed in a random-effects meta-analysis with BCVA change as the primary outcome. Meta-regression was conducted to evaluate the impact of multiple covariates. Four hundred and twelve heterogeneous study populations  were included. Anti-VEGFs induced an overall improvement of +5.37 ETDRS letters at 12 months. Meta-regression showed that mean BCVA change was statistically greater for RCTs ( Neovascular age-related macular degeneration (nAMD) is the leading cause of irreversible vision loss among the over-50s living in developed countries, with a prevalence rate between 5.8% and 15.1% of the population, which constantly increases with age [In recent times, intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy has become the treatment of choice for nAMD, supported by evidence from randomized clinical trials (RCTs) as well as routine clinical practice, demonstrating efficacy in preventing visual loss and improving vision ,2. CurreAre results between RCTs and real-life/observational studies different?Are results between RCTs and real-life/observational studies different, when analyzing each anti-VEGF agent?Is the outcome influenced by the treatment regimen?Is the outcome influenced by the treatment regimen, when considering only real-life/observational studies?If proactive regimens produce better results, is this accurate when considering each anti-VEGF agent?Is the outcome influenced by the frequency of treatments?If the number of treatments has an effect on the results, is this accurate when considering each anti-VEGF agent?Comprehensively, which agent shows more favorable results?In real life/observational studies, which agent produces better results?Are real-life visual results influenced by baseline characteristics?A stepwise procedure, which includes a systematic literature review (SLR), a meta-analysis, and a meta-regression, was utilized to assess the efficacy/effectiveness of intravitreal therapy in patients affected by nAMD.A SLR of available studies, which include patients affected by naïve nAMD and treated with intravitreal ranibizumab, aflibercept, bevacizumab, or brolucizumab with 52-week follow-up, was conducted. The present review was completed according to the protocols reported in the Cochrane Handbook for Systematic Review of Interventions (v5.1.0). The outcomes are expressed as reported in the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) . In brieThe review was restricted to clinical studies available in peer-reviewed, English language publications, and those published until March 2021. Conference abstracts/papers, editorials, proposals, reviews, notes, letters to authors, news, and commentaries were not included in the review. The reference lists from selected articles were inspected for additional publications. The risk of bias was estimated both quantitatively and qualitatively with the Downs and Black checklist.A meta-analysis of the outcomes obtained from the SLR was performed. Inclusion criteria for the meta-analysis consisted of studies including naïve nAMD patients treated with ranibizumab, aflibercept, bevacizumab, or brolucizumab in monotherapy and reported 1-year (±4 weeks) effectiveness outcomes. The main aim of this meta-analysis was to extract a pooled estimate for effectiveness  change from baseline to week 52 in Early Treatment Diabetic Retinopathy Study (ETDRS) letters). Visual acuities expressed in LogMAR unit or decimal scale were converted to ETDRS letters before performing statistical analysis. Randomized controlled trials, real-life prospective, and retrospective clinical studies were considered. Papers that investigated specific populations affected by retinal angiomatous proliferation, polypoidal choroidal vasculopathy, or fibrovascular pigment epithelial detachment were excluded from the analysis. Studies in which a specific type of anti-VEGF could not be extracted from the results were also not considered. Publications from the same author/organization that included duplicated data were not included.The treatment strategy was categorized into one of three groups. Populations treated on a fixed protocol such as monthly or bimonthly were codified as fixed. Those being injected under a PRN interval were categorized as PRN and in the same manner TAE approaches constituted the TAE group.2 statistic. Egger’s linear regression was used to evaluate publication bias along with visualization of funnel plots.Fixed-effects and random-effects models were utilized to obtain estimates. Heterogeneity was determined with the IWe performed a meta-regression analysis. Pre-selected primary moderators were chosen on the basis of existing evidence. Moderators of interest were age at baseline, baseline BCVA, study type , drug, number of injections, and treatment schedule. The output variable considered was mean BCVA change in ETDRS letters at 52 weeks (±4 weeks).The present study is based on previously published articles and does not imply any new studies of human participants. This work did not necessitate ethical approval as it did not include human participants or animal subjects.The primary search produced 7709 reports. After screening of titles and abstracts and removal of duplicates, 683 potentially relevant papers were identified, and the full texts were extracted and individually screened for eligibility. Two hundred and seventy-six studies with 412 heterogeneous populations fulfilled inclusion criteria and were included in the analysis. ,282,283.Attributes of the 276 analyzed studies are detailed in 2 = 94.478%; p < 0.0001), and thereafter we chose a random-effects model. None of studies showed a significant effect on overall effect size, as showed by a leave-one-out sensitivity analysis. The meta-analysis provided an overall gain in BCVA of +5.37 ETDRS letters (95% CI: 5.01–5.72) at 12 months.We found a high heterogeneity among studies considered in the analysis  regression of difference in means on study type showed a coefficient of +1.32 ETDRS letters favoring RCTs over real-life/observational studies .A statistically significant  showed a coefficient of +1.80 (aflibercept) and +1.84 (ranibizumab) ETDRS letters advantaging RCTs over real-life/observational studies . The same analysis performed for bevacizumab resulted in a not statistically significant difference (p = 0.95).A statistically significant regression of difference in means on study type for aflibercept and ranibizumab (3.Is the outcome influenced by the treatment regimen?p < 0.0001). Treat and extend regimen showed a coefficient of +2.40 ETDRS letters over PRN. . No statistically significant difference was found between fixed and TAE regimen (p = 0.78).Regression of difference in means on regimen was statistically significant, higher benefit was seen for fixed and TAE regimen over PRN regimen. Fixed regimen showed a coefficient of +2.23 ETDRS letters over PRN. . Treat and extend regimen showed a coefficient of +2.02 ETDRS letters over PRN. . No statistically significant difference was found between fixed and TAE regimen (p = 0.61).Regression of difference in means on regimen was statistically significant, in favor of fixed and TAE regimen over PRN regimen, when including only real-life/observational studies. Fixed regimen showed a coefficient of +1.68 ETDRS letters over PRN. , and TAE regimen over PRN regimen  was described. Similarly, ranibizumab-treated populations had better outcomes in studies utilizing fixed regimen over PRN regimen , and TAE regimen over PRN regimen . In patients treated with bevacizumab, regression of difference in means on regimen was not significant (p > 0.5).In patients treated with aflibercept, a statistically significant difference indicating more favorable results for fixed regimen over PRN regimen .A highly statistically significant effect resulted from regression of difference in means on mean number of treatments . The same analysis was not statistically significant for aflibercept  and bevacizumab .When looking at ranibizumab-treated populations, outcomes were significantly influenced by mean number of treatments . A non-statistically significant trend for better results for aflibercept-treated populations over bevacizumab-treated populations was seen . The comparisons between the results published for brolucizumab-treated populations and the populations treated with other anti-VEGF agents were not statistically significant (p > 0.3). However, a non-significant trend towards better outcomes in the brolucizumab studies was detected .Regression of difference in means on drug showed that the studies employing aflibercept reported significantly superior results over ranibizumab  was found for aflibercept . When considering the populations treated with a PRN regimen, a trend to better outcomes  was found for aflibercept . In the populations treated with a TAE regimen, a trend to better outcomes  was found for aflibercept .9.In real life/observational studies, which agent produces better results?p < 0.0001), as shown by regression of difference in means on drug in real life/observational studies.Aflibercept reported significantly better results over ranibizumab  and baseline BCVA .Regression of difference in means was significant on age  and by Begg’s rank correlation test  also suggest the existence of publication bias. After imputing missing studies in the funnel plot, adjustment of effect size for possible publication bias using the trim-and-fill correction results in decreased, albeit still highly significant estimate of pooled mean difference . A ‘one-study-removed’ technique and a ‘cumulative meta-analysis’ technique were used to evaluate the potential influence of a small-study effect. Both techniques express negative results.Funnel plot asymmetry was seen in the present meta-analysis. Egger’s linear regression  and, as expected, we found a higher variability in real-life results. This is in line with previous reports indicating that outcomes achieved with anti-VEGFs in real-life studies for the treatment of nAMD are not as good as those obtained in RCTs. However, it remains a matter of discussion whether a difference of +1.3 ETDRS letters is clinically meaningful. In the present meta-analysis and meta-regression, we choose a random-effects approach as the observed heterogeneity in the estimates may be attributed to between-study heterogeneity in true effects and within-study sampling error.Neovascular AMD is the main cause of vision loss in adult patients in developed countries ,2. The pp < 0.0001). At month 12, each additional treatment induces a +0.51-letter gain. Yet, these results are not uniform among all anti-VEGF agents. The drug most dependent on the number of injections per year seems to be ranibizumab (coefficient +0.69 ETDRS letters per injection). We believe that this finding can be ascribed to both pharmacological properties and to the characteristics of the studies analyzed. In detail, the variability in the number of injections is much wider in ranibizumab studies than in those using aflibercept and bevacizumab. This is mainly because the larger part of ranibizumab studies apply a PRN regimen that involves a wider variability in the number of injections. Moreover, we investigated the role of the treatment regimen employed in obtaining the most favorable results. Results from the present meta-regression indicate that better outcomes are seen when employing a proactive treatment regimen (fixed or TAE) over a reactive treatment regimen (PRN). These results are also confirmed when analyzing real-life studies alone. Actually, many factors may interfere with the therapeutic efficacy of PRN treatment regimen in a real-life scenario, including administrative and logistic considerations. For example, improper appointment scheduling for treatment and monitoring visits is indeed a real-world factor that may result in unsatisfactory outcomes. Moreover, strict adherence to rigorous retreatment criteria is often difficult to obtain in a real-life scenario, due to inhomogeneity in imaging technologies and physicians’ knowledge and skills. This represents a limitation in maximizing visual gains, leading to suboptimal outcomes for the patients.Growing evidence suggests that the regimen employed, and the frequency of anti-VEGF injections, have an impact on the visual outcome when treating a patient affected by nAMD . Data frWhen analyzing baseline characteristics that may influence visual outcomes, our meta-regression showed that the 12-month BCVA change negatively correlated with baseline BCVA, which is consistent with prior experiences, revealing an inverse correlation between baseline BCVA and long-term BCVA change. Our analysis also revealed a negative correlation in BCVA change with increasing age. This negative correlation may be a consequence of worsened functional results at later age of presentation, when both the advanced stage of the disease and a decreased response to therapy may lead to inferior clinical outcomes. Key results from our work are reported in The main strength of the present work is that it provides an exhaustive and paradigmatic overview of the various therapeutic approaches used in real-life clinical practice and in RCTs for nAMD patients. We employed a predefined search strategy, three independent reviewers performed data extraction, and subgroup and sensitivity analysis were also conducted.However, some limitations of the current study should not be ignored. First, the enrolled studies were limited to English language. This may have led to studies not being included, resulting in a not quite comprehensive data set. Second, the quality of included studies is variable. Real-life/observational studies exhibit a higher level of bias than RCT, including publication bias. Third, the heterogeneity among studies was notable, possibly due to confounding variables such as sample sizes, ethnic distribution of the study population, study designs, CNV types, and treatment modalities. Actually, uncontrolled confounding predisposes to bias when comparing observational studies and RCT. Fourth, the data used to establish these results might suffer from sample selection bias.Finally, our results, from a methodological point of view, are also susceptible to ecological bias and study-level confounding, which means that the observed across-study relationships may not properly mirror the individual-level relationships within trials. In this sense, a network meta-analysis is probably less prone to misinterpretation. For all these motives, care must be exercised in conjecturing any form of quantitative relationship, which may alter over time and with a larger number of reports/studies included in the analysis.In conclusion, the evidence for intravitreal therapy with anti-VEGF agents has been confirmed in this meta-analysis to be highly beneficial in the therapy of nAMD both in clinical trials and in real-life experiences. Frequency of injections and proactive treatment regimens are both factors related to best outcomes with currently available anti-VEGF agents.


Background: The human gut microbiota are important to health and wellness, and disrupted microbiota homeostasis, or “dysbiosis,” can cause or contribute to many gastrointestinal disease states. Dysbiosis can be caused by many factors, most notably antibiotic treatment. To correct dysbiosis and restore healthier microbiota, several investigational microbiota-based live biotherapeutic products (LBPs) are in formal clinical development. To better guide and refine LBP development and to better understand and manage the risks of antibiotic administration, biomarkers that distinguish post-antibiotic dysbiosis from healthy microbiota are needed. Here we report the development of a prototype Microbiome Health Index for post-Antibiotic dysbiosis (MHI-A).Methods: MHI-A was developed and validated using longitudinal gut microbiome data from participants in clinical trials of RBX2660 and RBX7455 – investigational LBPs in development for reducing recurrent Clostridioides difficile infections (rCDI). The MHI-A algorithm relates the relative abundances of microbiome taxonomic classes that changed the most after RBX2660 or RBX7455 treatment, that strongly correlated with clinical response, and that reflect biological mechanisms believed important to rCDI. The diagnostic utility of MHI-A was reinforced using publicly available microbiome data from healthy or antibiotic-treated populations.Results: MHI-A has high accuracy to distinguish post-antibiotic dysbiosis from healthy microbiota. MHI-A values were consistent across multiple healthy populations and were significantly shifted by antibiotic treatments known to alter microbiota compositions, shifted less by microbiota-sparing antibiotics. Clinical response to RBX2660 and RBX7455 correlated with a shift of MHI-A from dysbiotic to healthy values.Conclusion: MHI-A is a promising biomarker of post-antibiotic dysbiosis and subsequent restoration. MHI-A may be useful for rank-ordering the microbiota-disrupting effects of antibiotics and as a pharmacodynamic measure of microbiota restoration. Disruptions to the human gut microbiota, known as dysbiosis, can contribute to gastrointestinal, neurologic, epithelial, genitourinary, and oncological disorders . To counClostridioides difficile infections (CDI) or infections by multidrug-resistant Enterobacteriaceae or Enterococci  with confidence limits were generated by fitting a Dirichlet-multinomial (DM) distribution to OTU data using maximum likelihood estimation . Dirichlp values, and odds ratios showed that each class by itself separated the baseline and RBX2660 groups. The odds ratios for Gammaproteobacteria and Bacilli indicated high value of these classes to predict post-antibiotic dysbiosis and low odds ratios for Bacteroidia and Clostridia indicated high value of these classes to predict healthy or non-dysbiotic samples  analyses were conducted within GraphPad Prism using baseline and RBX2660 MHI-A data from the PUNCH CD2 trial. All pairwise hypothesis testing between MHI-A data groups, which used Mann–Whitney tests unless otherwise specified, was conducted within GraphPad Prism.The MHI-A was designed to differentiate post-antibiotic dysbiosis from healthy microbiota. Literature teaches that many antibiotics reduce the relative abundance of Bacteroidetes and Firmicutes phyla compared to healthy populations, with a concomitant increase in Proteobacteria , the median MHI-A was 0.0013, whereas the median MHI-A for RBX2660 was 183 , and the majority were > 7.2 (p < 0.05). Analysis of non-responders (CDI recurrence) was limited because most recurred prior to the earliest 7-day time point, at which point participants received additional antibiotic and/or RBX2660 treatment and were therefore excluded from subsequent microbiome analysis. Among the five 7-day and two 30-day samples that were included from non-responders, most had MHI-A < 7.2  and similar to baseline participants in RBX2660 and RBX7455 trials. This MHI-A decrease was driven by lowered Bacteroidia and Clostridia abundance concurrent with a bloom of Gammaproteobacteria. By 6 weeks and 6 months after the start of treatment, MHI-A had largely recovered to pre-treatment levels. Thus, MHI-A was clearly diagnostic of a post-antibiotic microbiota shift with subsequent restoration in this study.To evaluate how well MHI-A can be generalized as an indicator of post-antibiotic dysbiosis, published studies of antibiotic treatments were evaluated. A literature search revealed three such studies for which longitudinal samples collected before and after antibiotic treatment were sequenced and data publicly available. For each study, sequencing data was processed to OTU from which MHI-A values were calculated. The first study characterized a 4-day course of a broad-spectrum cocktail of meropenem, gentamycin, and vancomycin in 12 healthy adult males using whole-genome sequencing . Prior tp > 0.05, p < 0.05) but converged thereafter. The only antibiotic that significantly decreased MHI-A was clindamycin, and only at 1 month after treatment; notably, clindamycin is known to increase CDI risk . Thus, in this study MHI-A clearly differentiated between the narrower- and broader-spectrum CDI antibiotics.The third study characterized the microbiome before, during, and after a 10-day course of vancomycin or ridinilazole for CDI in a Phase 2 clinical trial , and decreased Gammaproteobacteria correlated with reduced CDI recurrence, trends that are highly consistent with MHI-A. They also cited the MHI-A described here, showing that a small change (1%) in the MHI-A reported herein was more effective at predicting CDI recurrence than changes in individual taxa alone or than the Shannon index. It may be that the ability of MHI-A to account for interconnected positively and negatively correlated taxa increases its diagnostic potential. Overall, the taxonomic changes measured by MHI-A are quite consistent with other metrics reported in the literature.Two additional metrics were proposed to predict response to or recurrence after CDI antibiotics, and both are consistent with MHI-A. In one, elevated abundance of six taxa prior to CDI antibiotic was positively associated with successful antibiotic response . All sixcurrence . They foIt was conceivable that taxonomic levels other than class could lead to a similar index. At the phylum level, the F/B ratio was less effective, as already described. At the order level, Clostridiales shifted concurrently with Clostridia, Lactobacillales with Bacilli, Enterobacterales with Gammaproteobacteria, and Bacteroidales with Bacteroidia. However, the accuracy of MHI-A was not increased when assessed at the order level. At the family or genus taxonomic levels, patient-to-patient variability was higher, which would decrease diagnostic accuracy. Thus, MHI-A appears simplest to calculate while retaining high accuracy.Enterococcus, Streptococcus, or Gammaproteobacteria dominance of the intestinal microbiome was correlated with increased risk of bacteremia, and these microbiome signals preceded infection by up to 7 days  measure of microbiota restoration for LBPs. Typically, PD is defined as the action of a drug in the body, manifested as biochemical or physiologic effects, but debate remains about how to define PD for microbiota-based LBPs . In the A few limitations of this work bear noting. First, it is an apparent limitation that MHI-A was not decreased by all antibiotics evaluated herein, specifically in healthy volunteers that received clindamycin, ciprofloxacin, moxifloxacin, or amoxicillin. This prompts consideration of what is the minimum level of disruption detectable with MHI-A. It bears noting that other studies using culturomics or pyrosequencing methods suggested these antibiotics may be more impactful than was observed in the data set used to calculate their MHI-A shift here . NeverthDespite the potential limitations, there are several key strengths of our work: the large number of samples and studies assessed, the consistency of patient definitions and sample collection processes across three controlled clinical trials, the evaluation of antibiotics as well as investigational microbiota-restoring treatments, consistent performance of MHI-A among three sequencing methodologies, and the inclusion of publicly available data sets to assess the generalizability of observations. Overall, the MHI-A provides a novel perspective and promising prototype microbiome marker that merits further evaluation and development as a tool for monitoring post-antibiotic dysbiosis.The datasets presented in this article are not readily available because they are restricted by an ongoing commercialization process. Requests to access the datasets should be directed to KB.IRB approval was obtained at each study center for NCT02299570, NCT02589847, and NCT02981316. The patients/participants provided their written informed consent to participate in this study.KB and CJ contributed to conception and design of the study. KB, DW, CG, and WS performed the statistical analysis. KB wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.The work was supported by Rebiotix, a Ferring Company, as part of a commercial clinical development program for microbiota-based investigational live biotherapeutics RBX2660 and RBX7455.KB, CJ, and DW are employees of Rebiotix, A Ferring Company. CG and WS are employed by BioRankings, LLC, which received fees for this analysis from Rebiotix.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Growing evidences suggest that autophagy plays a momentous part in the tumorigenesis and development of hepatocellular carcinoma (HCC). However, there are not many researches to predict the prognosis of HCC using autophagy-related genes. Therefore, based on the clinical information and RNA-Seq data of The Cancer Genome Atlas data portal (TCGA), 13 autophagy‑related gene pairs were screened to build the autophagy‑related signature to predict the prognosis by least absolute shrinkage and selection operator (LASSO) regression analysis. Besides, the International Cancer Genome Consortium (ICGC) cohort was further applied to verify the autophagy‑related prognostic signature. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were also used to predict the relevant function of the autophagy-related gene pairs signature. As shown in the results, the autophagy-related gene pairs were mainly involved in process utilizing autophagic mechanism, autophagy, macroautophagy and cellular response to oxidative stress. The immune cell levels in the high-risk and low-risk group were explored, which showed that the three immune cells were obviously increased in the high-risk group, while the five immune cells were obviously increased in the low-risk group. In conclusion, an autophagy‑related prognostic signature was established to predict the prognosis of HCC patients with great accuracy and we found that autophagy‑related prognostic signature was related to infiltrating immune cells.  According to GLOBAOCAN, liver cancer was one of the most deadly cancers around the world in 2018 . NeverthMany researches have proposed signatures based on gene expression for outcome stratification in HCC patients. 11 genes, including TNRC6B, VAV3 andRNF213, with a range of mutational prevalence from 1% to 3%, could predict the prognosis of HCC patients . Jing-XiThe word autophagy date from the Greek word, denoting ‘self-eating’, and refers to the collection of various processes that deliver cellular materials to lysosomes for digestion. In mammals, micro-autophagy, macro-autophagy , and chaIn the study, we established the signature of autophagy-related gene pairs through analyzing the gene expression profile of HCC. the prognosis of HCC patients could be predicted using the signature. Moreover, the association of signature of autophagy-related gene pairs and tumor microenvironment was further analyzed.https://portal.gdc.cancer.gov) [https://icgc.org/) including 243 samples was served as the validation dataset [http://autophagy.lu/clustering/index.html) [The RNA-seq expression matrix and corresponding clinic information of 374 HCC specimens were gained from The Cancer Genome Atlas (TCGA) database (cer.gov) , which w dataset . 232 autex.html) . The expex.html) . ConsequThe score for each autophagy-related gene pair was computed by the paired comparison of the gene expression levels in each patient. Based on the suggested method, if the expression level of preceding gene was much higher than the latter gene expression level in a specific autophagy-related gene pair, the score of this autophagy-related gene pair was 1, otherwise, the score was 0. The common autophagy-related gene pairs in two cohorts were screened out as the candidate gene pairs to reflect the prognosis of HCC. The Kaplan-Meier analysis coupled with the log-rank test was applied for the univariate analysis, which was used to select autophagy-related gene pairs related with prognosis. The least absolute shrinkage and selection operator (LASSO) analysis was applied to establish the ﬁnal model . The autIn order to validate the prognostic signature, the prognostic score was calculated by the autophagy-related gene pairs signature in the ICGC cohort. Furthermore, the HCC patients were also separated into high risk and low risk group based on the identical cut-off value in the ICGC cohort. The log-rank test was performed to compared survival curves between two groups. Meanwhile, patients with different tumor stage and grade were also performed Kaplan-Meier analysis. The ROC curve analysis was applied to demonstrate the specificity and sensitivity and of autophagy-relevant gene pairs signature in outcome prediction. Univariate/multivariate cox-regression models were applied to estimate risk elements that impacted OS.https://www.gsea-msigdb.org/gsea/index.jsp) [To explore the potential functions of the autophagy-related gene pairs signature, GO analysis and KEGG analysis were performed by the ‘clusterProfiler’ package. Biological processes were collected from the Gene Set Enrichment Analysis (MSigDB C2 and C5 databases) (dex.jsp) . The falhttp://cibersort.stanford.edu/) for analysis with the default signature matrix at 1000 permutations.CIBERSORT algorithm, a novel computational method, uses 547 reference gene expression values for estimating enrichment of various immunocyte compositions . In our The heat map of correlation was performed by the ‘corplot’ package, which was aimed to explore the correlation of autophagy-related gene pairs. The relationships between infiltrating immune cells and risk score of patients were also performed.https://www.r-proje ct.org/) and Perl version 5.32.0.1 software (https://www.perl.org/). The differences among the two groups were compared with student’s t-tests. The ‘survival’ package was used to perform the survival analysis. The ROC curves were performed by ‘survivalROC’ package. For all studies, P-value < 0.05 was served as statistically significantly different. Statistical signiﬁcance was displayed as *P < 0.05, **P < 0.01, ***P < 0.001.The statistical analyses were processed by R version 3.6.0 software  >0.5, genes with more large variation were proposed as candidate genes. Among 232 autophagy-relevant genes from HADb, 127 autophagy-related genes were obtained by two cohorts, which were applied to construct 1454 autophagy-related gene pairs ) . In addiTo assess the potential function of the autophagy-related gene pairs signature, we performed the GO, KEGG, and GSEA analysis. The GO analysis consisting of biological processes (BP), molecular functions (MF) and cellular components (CC), and suggested that the autophagy-related gene pairs signature genes were mostly enriched in autophagy, transcription factor complex and ubiquitin-like protein ligase binding . The KEGAccording to the CIBERSORT, we estimated the relative proportion of the 22 immune cells for each HCC patient within the high-risk class and the low-risk class. The comparison of different immune cells in the low-risk class and high-risk class using CIBERSORT was displayed in Despite of significant advances in the prevention, diagnosis, and therapy of HCC, it remained the primary reason of cancer death around the world . AccordiIn this research, we obtained a novel prognosis signature from 13 autophagy-related gene pairs of HCC in TCGA cohort and verified in ICGC cohort. The autophagy signature could effectively separate patients into different subgroups according to their survival outcomes. Based on multivariate analysis and Kaplan-Meier method, autophagy-related gene pairs signature could act as the independent predictive factor of HCC patients. The above results were successfully verified in the ICGC cohort. In a word, autophagy-related genes may be closely associated with prognosis of HCC patients.The traditional prognosis signatures required to preprocess the gene expression levels, which was the vital reason to restrain the wide use of models. The autophagy-related gene pairs signature was obtained through the relative sequencing of the gene expression levels, and paired comparison of weights was only carried out within the patient gene expression profiles, which was a huge advantage. Our prognosis signature could not only eliminate batch effects among different platforms but also did not need the normalization and scaling of data. The formula for the risk score and truncation value could be used in various datasets, which made it easier to be applied in clinical. This method has been performed robustly in cancer-relevant studies , and it Autophagy was associated with a wide range of physiological processes and human diseases, and it has been linked to both tumorigenesis and oncotherapy in recent years. Hence, it was important to build the autophagy-related signature for predict the survival outcome for HCC patients. Our 13 autophagy-related gene pairs signature contained 26 genes, which were mainly participated in the functions of autophagy and tumor occurrence and development. For example, the Tp53, as the gene of autophagy-related prognosis signature, could contribute to the chemoresistance and cell survival in HCC under conditions of nutritional deficiency by modulating the activation of autophagy . In addiMore and more evidences have shown that the tumor microenvironment (TME) was intricately related to cancer initiation, promotion, and progression. And autophagy and immunity took great participation in TME. Some studies have demonstrated that autophagy played a momentous role in the immunity regulation. Autophagy regulated cellular differentiation, development and homeostasis of multiple immune cells like monocytes, T-cells, and B-cells, thus was deeply involved in the innate immune system as well as lymphocyte activation and survival . For insNevertheless, there are limitations in this research. Firstly, we adopted a retrospective method to analyze the data, and to validate the results, a prospective cohort needs to be done. Secondly, more details about the biological functions and molecular mechanisms of 24 autophagy-related genes in HCC patients required further assessment. RT-PCR could be used for further clinical applications.In summary, we established the autophagy-related gene pairs signature with data from the TCGA, ICGC and HADb datasets, which could accurately predict the HCC patient prognosis. The results demonstrated that the low-risk group had a significantly better OS than the high-risk. Moreover, we discovered that the immune cell infiltration levels were obviously different between high-risk group and low-risk group, which indicated that the autophagy-related gene pairs signature was relevant to infiltrated immune cells. The autophagy-related gene pairs signature will act as a crucially predictive marker to offer insights into the therapeutic strategies.Click here for additional data file.


AT-Hook Motif Nuclear Localized (AHL) gene family is highly conserved in land plants, playing crucial roles in plant growth and development. To date, however, the AHL gene family has not been studied in soybean.Soybean is an important legume crop and has significant agricultural and economic value. Previous research has shown that the AHL gene family in soybean, genome-wide identification, expression patterns and gene structures were performed to analyze. We identified a total of 63 AT-hook motif genes, which were characterized by the presence of the AT-hook motif and PPC domain in soybean. The AT-hook motif genes were distributed on 18 chromosomes and formed two distinct clades (A and B), as shown by phylogenetic analysis. All the AHL proteins were further classified into three types  based on the AT-hook motif. Type-I was belonged to Clade-A, while Type-II and Type-III were belonged to Clade-B. Our results also showed that the main type of duplication in the soybean AHL gene family was segmented duplication event.To investigate the roles played by the AHL gene family was involved in stress response in soybean, we performed cis-acting elements analysis and found that AHL genes were associated with light responsiveness, anaerobic induction, MYB and gibberellin-responsiveness elements. This suggest that AHL genes may participate in plant development and mediate stress response. Moreover, a co-expression network analysis showed that the AHL genes were also involved in energy transduction, and the associated with the gibberellin pathway and nuclear entry signal pathways in soybean. Transcription analysis revealed that AHL genes in Jack and Williams82 have a common expression pattern and are mostly expressed in roots, showing greater sensitivity under drought and submergence stress. Hence, the AHL gene family mainly reacts on mediating stress responses in the roots and provide comprehensive information for further understanding of the AT-hook motif gene family-mediated stress response in soybean.To discern whether the AT-hook motif genes were identified in the soybean genome. These genes formed into two distinct phylogenetic clades and belonged to three different types. Cis-acting elements and co-expression network analyses suggested that AHL genes participated in significant biological processes. This work provides important theoretical basis for the understanding of AHLs biological functions in soybean.Sixty-three  AT-Hook Motif Nuclear Localized (AHL) gene family is highly conserved across all land plants, and the AHL transcription factors were previously described in mosses and flowering plants [bHLH and NAC gene families [AT-hook motif gene family is highly conserved across plant species and plays relevant roles during plant development.The g plants . It has families –7. HowevAT-hook motif gene family is involved in in very important biological processes in plants. For example, AHL genes are associated with the regulation of plant reproductive development and the formation of ears in maize [DP1 gene, encoding for an AT-hook DNA binding protein, plays an important role in flower development [AT-hook motif gene family is also able to regulates the expression of cell-specific genes. The overexpression of the GIANT KILLER(GIK) gene, which encodes an AHL protein, leads to serious defects in the reproductive organs and the reduction of expression levels in associated genes [Arabidopsis, the AHL gene BoMF2 is preferentially expressed in the stamens and its overexpression results in a significantly shorter siliques and a decrease in pollen vigor relative to the wild type [AHL gene family also has been identified to regulate hormone balance in plants, especially gibberellin [AtAHL13 is a key factor regulating jasmonic acid biosynthesis signal transduction and pathogen immunity [AHL22 in Arabidopsis mutant exhibits delayed flowering, significantly decreased transcription activity and acetylation of histone H3 at the FLOWERING LOCUS, and to an increased demethylation rate of H3 Lysine 9 [AHL gene, is involved in the regulation of silent TEs. Specifically, knocking down of TEK leads to increased histone acetylation and decreased H3K9me2 and DNA methylation levels in the target loci [AHL genes have been identified in maize. The transcription levels in different tissues suggest that AHL proteins are involved in maize pollen development, drought response and senescence [AHL genes also be found in different three cotton genomes, and gene expression analysis indicated that the majority of AHL genes in Clade-B were expressed in the stem whereas the Clade-A genes were expressed in the ovules [AHL genes uncovered in rice exhibited three expression patterns, all OsAHL genes may be functional genes with 3 different expression patterns [OsAHL1 improved rice response to multiple stress tolerances, especially drought resistance [The in maize . In riceelopment . Moreoveed genes . In Arabild type . Importaberellin , jasmoniberellin –15. Thisimmunity . ImportaLysine 9 . It has get loci . Recentlnescence . A high e ovules . Furtherpatterns . The oveAT-hook motif gene family not only plays important roles in plant growth and development of plants, but also affects plant response to stress and hormonal stimulus. These studies still lack a systematic investigation on how the AT-hook motif gene family regulates plant stress. Hence, this study evaluated plant response to drought and submergence stress mediated by AHL genes.These studies suggest that the AT-hook motif gene family regulates plant growth and development through DNA-protein interoperability and the formation of protein-homo/hetero-trimeric complex [AHL proteins contain two conserved domains, the AT-hook motif and the plant and Prokaryote Conserved (PPC) domain, also known as the Domain of Unknown Function#296 (DUF296) . The PPC complex , 26.AHLs retained their biological functions in the course of evolution [Phylogenetic analysis of land plants showed that the AHL proteins can be divided into two categories based on differences in the PPC domain, Clade A and Clade B . The convolution .Glycine max L. Merr) is the major leguminous species and an important source of protein worldwide, playing a vital role in human survival and development [AT-hook motif genes provides the basis for our research and the detailed genome-wide analysis of the AT-hook motif gene family in soybean has been not performed. In this study according to the findings of the AT-hook motif gene family in maize and cotton, we annotated the AT-hook motif gene family in the soybean genome and identified 63 AHL genes. We then analyzed function of these genes and respective protein structure features, as well as their chromosome locations, gene duplication events, Gene Ontology annotations, phylogenetic relationships, collinear co-expression network and expression patterns. Our results will foster understanding of the biological functions of the AHL family in soybean.Soybean , lacking the UTR, and some showing a variable number of introns and exons . Type-I genes were the shortest and contained the lowest number of CDS, which began to increase from Glyma.20G202300. Among them, Type-II and Type-III have two or more introns, which are more obvious than Type-I. Thus, we believe that Type-II and Type-III evolved from Type-I. This result is consistent with the report of maize AHL gene family [Zm00001d018515 and Zm00001d051861 [AHLs expand the abundance of AHL proteins. In Type-I of maize, only one gene has UTR, while most genes have UTR in soybean [AHLs gene structure of different species is diverse. In summary, we suspect that Type-II and Type-III introns enable plants to acquire more complex and diverse biological functions, and at the same time lay the foundation for the further expansion of intron-carrying AHLs.We implemented a gene structure analysis and estimated the length of AHL genes, and the variability in the number of CDS and UTRs Fig. , Table 1e family . In eukae family . After te family . This wa1d051861 . The alt soybean , indicatNext, MEME website was used to predict the protein motifs Fig. . We founThe motifs 3 and 6 had a common conserved Arg-Gly-Arg core, whereby likely belong to the AT-hook motif family. The motif 3 is defined as type I AT-hook motif, and motif 6 is defined as II AT-hook motif. Type-I AHL proteins contains a I AT-hook motif, Type-II contains both I and II AT-hook motifs, and Type-III only has a II AT-hook motif. The sequences downstream of the Arg-Gly-Arg core share common conserved that play an important role in AHL proteins . InteresAHL gene family has a consistent and evolutionary diversity in soybean and other land plants [In summary, the results of our gene structure and motif prediction analyses indicate that the d plants , includid plants  and cottd plants .Arabidopsis thaliana, sorghum (Sorghum bicolor L) and soybean as materials and constructing a phylogenetic tree a phylogenetic tree , molecular functions(MF), and cellular component(CC). Among all the enriched biological functions, we detected an association that the biological process(BP) biological process is related to flowering development, indicating that the AHL gene family interfere in the growth and development of floral organs in soybean, which is consistent with the data published in Arabidopsis [In the current study, we then used GO enrichment analysis to predict the potential biological functions of bidopsis . As for AHLs in soybean, we analyzed gene duplication events in the AT-hook motif gene family, as shown in Fig. AHL genes result from segmental duplication events, while 13% represent tandem gene duplication events, and the remaining 3% are proximal. These results suggest that segment duplication events may be the main driver of AHL gene family evolution.Gene duplication is a common process in plant evolution that leads to the expansion of gene families, of which tandem and segmental gene duplication events are the most common in angiosperms –33. In oAHLs of two dicotyledonous plants (Poplar and Medicago) and two monocots plants (rice and maize) plants were investigated in order to explore the potential evolutionary relationships  gene is expressed in both nutritional and reproductive tissues in tomato and this gene family is also involved in GA signal transduction [AHL gene of Glyma.20G212200 was co-expressed with two Glyma.05G140400 and Glyma.08 g095800 DELLA genes. Similarly, Glyma.16G204400 and Glyma.08 g095800 Glyma.05G140400 DELLA genes interact to regulate the gibberellin transduction pathway in soybean. Therefore, we consider that the AT-hook motif gene family is involved in gibberellin signal transduction pathway in soybean. Together, our results show that the AHL gene family is involved in regulating biological processes such as energy transduction, the gibberellin pathway and the nuclear entry signal pathway in soybean.A co-expression network was used to represent the upstream and downstream genes that interact with pes Fig. . We picksduction . In our AT-hook motif gene family, we selected the representative soybean cultivars, Jack and Williams82 at different tissues and during the VC stage. The transcription data is available from NCBI (accession number: SRP285849) [AT-hook motif gene family between different soybean varieties  . W82 andin maize . In the AHLs mediate plant response to drought stress [AHLs, after PEG treatment, the AHL genes has different degrees of response to the stress [AHLs in soybean may also impact in drought stress responses in in soybean. Hence, we tested the expression of genes in the leaves and roots of W82 under submergence and drought conditions (PRJNA574626) at the V1 stage  [AT-hook motif genes of soybean and sorghum were from JGI Phytozome website  and Ensemble Plants (https://plants.ensembl.org/index.html) [https://pfam.xfam.org) to predict the genes containing the PPC domain, and then filtered out the genes containing both the PPC domain and AT-hook motif [Arabidopsis thaliana, soybean and sorghum was performed. We used online ExPASy program (http://www.expasy.org/tools/) to determine the biochemistry of each AHL protein, including the number of amino acids, the molecular weight (MW) and predict the isoelectric point (pI) parameters [The is.org/) . The amiex.html) , 55. We ok motif . The homrameters .AHL genes [Arabidopsis thaliana, Glycine max and sorghum were selected to construct the phylogenetic tree by using the MEGA7 software [We used a Neighbor-Joining tree to represent the phylogenetic relationship between the HL genes . The amisoftware . We implsoftware .http://meme-suite.org/) to predict the conserved motif of AT-hook motif in the AHL gene family with an e-value of 10− 5 in soybean [AT-hook motif genes was analyzed using the TBtools software [http://smart.embl-heidelberg.de/) to evaluate the accuracy of the selected proteins [We used MEME  website to conduct GO analysis on 63 AT-hook motif genes.Chromosome mapping information for the software . We selesoftware , 62. We AT-hook motif gene family of Glycine max from NCBI. The cis-acting elements were analyzed using TB tools [AT-hook motif gene family was derived from find new members of a pathway in SoyNet (www.inetbio.org/soynet) [We obtained 2100 bp genome sequences spanning the promoter regions of the TB tools . Co-expr/soynet) . The res/soynet) .https://www.ncbi.nlm.nih.gov). We processed the transcriptome data and constructed the heat map in R. The fragments-per-kilobase-per-million (FPKM) value was used to quantify gene expression. The heatmap map was built according to the observed expression levels.The transcription data was obtained from the NCBI database  as qRT-PCR reagent. The designed qRT-PCR primers are shown in Table n levels .Table 8